esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
COP data were collected through the SERCS at a 100-Hz sampling rate. In both datasets, the primary outcome measure from the COP data was the analysis of rambling and trembling components, which are indicative of postural stability and control. According to the authors of [23], rambling and trembling components were extracted from the COP signals in both the x-, or mediolateral, and y-, or anteroposterior, axes. To process the COP data, each trial was analyzed using a custom Python script (version 3.12.7). Force signals were first smoothed using a 4 th-order low-pass Butterworth filter, with a 6-Hz cutoff, which facilitated accurate identification of zero-crossings, or IEPs, without attenuating the high-frequency content necessary for subsequent trembling analysis. The COP signal was then mean-centered to remove any offset in COP position, focusing the analysis on postural excursions. To decompose the COP trajectory, IEPs were identified as the time points where the horizontal ground reaction force crossed zero, signifying moments of mechanical equilibrium. The COP values at these IEPs were interpolated using cubic splines to reconstruct the rambling trajectory. Trembling was subsequently defined as the residual signal obtained by subtracting the rambling trajectory from the mean-centered COP. The decomposition algorithm was designed to robustly handle missing data and to maintain reliable interpolation, even in cases of sparse IEPs.  
For the spectral analysis, the power spectral density (PSD) of both the rambling and trembling components was computed using the Fast Fourier Transform (FFT), consistent with prior work [30]. The PSD was then integrated within predefined frequency bands (low frequency: 0-0.3 Hz, medium frequency: 0.3-1 Hz, high frequency: 1-3 Hz, reflecting contributions from the visual, vestibular/somatosensory, and proprioceptive systems, respectively) to quantify the power in each band [30]. An extraction of output files that carry out the frequency analysis on the COP data exclusively without decomposition into rambling and trembling was also performed in order to confirm the use of specific sensory modalities.
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 4 Experiments and Discussions
### Experimental Settings
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type** & **Method** & **Short Explanation** \\ \hline \multirow{3}{*}{Traditional} & \(DCP\)[8] & Dark channel prior \\  & CEEF[6] & Joint contrast enhancement and exposure fusion \\  & FADE[41] & Fast region-adaptive defogging and enhancement \\ \hline \multirow{3}{*}{Learning-based} & DehazeFlow [42] & Multi-scale conditional flow dehazing network \\  & SRKTDN [43] & Dehazing network with super-resolution method and knowledge transfer method \\  & DWGAN [44] & Discrete wavelet-transform GAN \\  & RefineDNet [17] & Weakly supervised refinement dehazing framework \\  & USIDNet [45] & Unsupervised single-image dehazing network via disentangled representations \\ \hline \multirow{3}{*}{Zero-shot} & ZID [14] & Zero-shot dehazing \\  & DDIP [35] & Coupled deep image prior \\ \cline{1-1}  & YOLY [13] & You only look yourself \\ \cline{1-1}  & ZIR [16] & Zero-shot single-image restoration \\ \cline{1-1}  & Ours & Our proposed dehazing method \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of various dehazing methods.
## 5 Numerical Modeling
### Results of the Numerical Study and Discussion
#### 5.3.2 Deformation of the STS Structure  
The stability of the STS structure directly influences the safety of the whole construction. Therefore, deformation of the STS structure in different excavation stages is discussed in the following.  
Vertical Deformation of the Horizontal Section of the STS Structure  
The deformation profile of the horizontal section of the STS structure is shown in Figure 27. Initial ground deformation is incurred by the excavation of the side tunnels (Z5 and Z6), and the deformation is larger at the ends of the horizontal section of the STS structure. The STS structure above pilot tunnel Z1 shows the largest deformation of about 5.9 mm. This is because the STS structure is unsupported along the whole span prior to the construction of column C1, resulting in a comparably large deflection in the STS structure when tunnel Z1 is completed.  
Figure 26: Surface settlement curve.  
The largest deformation of the STS structure is at the top of pilot tunnel Z3 with a deformation of about 3.3 mm during the construction of pilot tunnel Z3, Z4 and steel column C2. The deflection in the STS structure after stage 3 of the construction is not as pronounced as stage 2, given that the structure is now partly supported by column C1, as discussed previously. The largest deflection in the STS structure, which is 12.7 mm, happens when the soil is excavated, and the station hall is constructed.  
Figure 28 shows the horizontal deformation of the vertical section of the STS structure and the side walls at y = 15 m. The deformation profiles for both the right and left sections are almost identical, however, the right section shows slightly more deformation due to unsymmetrical nature of the construction sequence. The largest deformation of around 5 mm appears at the intersection of the right vertical section of the STS structure and the right-side wall, where a weak joint may have resulted in the large deformation. The largest horizontal deformation of the bottom of the side walls is 2.4 mm. After construction of the station hall, the largest deformation of STS structure is 3.4 mm, which occurs at the temporary braces. The three steps excavation is applied during the construction of the station hall, and when the first step is excavated the temporary braces were not installed in time, which may cause a large releasing of stress.  
Figure 27: Deformation of STS structure.  
Figure 28: Horizontal displacement profile of vertical sections of the STS structure and the side walls: (**a**) Left vertical section of STS structure and left side wall; (**b**) Right vertical section of STS structure and right-side wall.
## 3 Results and Discussion
### Gasification Performance of Fuels and Their Mixture with Carbon Dioxide Reagent
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **KL** & **BS** & **KL/BS 75:25** & **KL/BS 50:50** & **KL/BS 25:75** \\ \hline Ti (\({}^{\circ}\)C) & 600 & 645 & 600 & 605 & 605 \\ Tmax (\({}^{\circ}\)C) & 789 & 865 & 857 & 875 & 903 \\ Rmax \(\times\) 10\({}^{2}\) (min\({}^{-1}\)) & 3.4 & 9.4 & 3.4 & 4.1 & 5.0 \\ Rmax/Tmax \(\times\) 10\({}^{4}\) (1/min \({}^{\circ}\)C)) & 0.43 & 1.1 & 0.4 & 0.5 & 0.6 \\ H\({}_{2}\) & 0.4 & 1.2 & 0.2 & 0.1 & 0.2 \\ CO & 96.6 & 95.8 & 98.5 & 99.6 & 99.0 \\ Gas (mol\(\%\)) & CH\({}_{4}\) & 2.7 & 0.1 & 0.5 & 0.1 & 0.5 \\ H\({}_{2}\)O & 0.2 & 2.9 & 0.7 & 0.2 & 0.2 \\ C\({}_{x}\)H\({}_{y}\) & 0.1 & 0.1 & 0.1 & 0.1 & 0.1 \\ HHV (MJ/m\({}^{3}\)) & 13.4 & 12.3 & 12.8 & 12.8 & 12.9 \\ GE (\(\%\)) & 62.9 & 94.5 & 63.2 & 69.8 & 75.2 \\ V\({}_{\rm syn}\) (m\({}^{3}\)/kg) & 0.55 & 0.84 & 0.56 & 0.61 & 0.66 \\ \hline \end{tabular}
\end{table} 
Table 2: Gasification characteristics of fuels with carbon dioxide reagent. Figure 9: DTG gasification profiles of Kardia lignite and barley straw chars under carbon dioxide atmosphere (RSD= 0.3–0.8\(\%\)). Additionally, the much lower O/C molar ratio of the blends (0.03-0.07) compared to lignite (0.11), implying a higher order and more stable aromatic structure [3], could have caused a drop in gasification reactivity. Nevertheless, by blending the lignite with the barley straw biomass material, upgraded chars were produced with higher organic matter, exhibiting higher gasification reactivity and conversion than the lignite. As to the effect of the gasifying agent on the gasification efficiency of the fuels studied, the above results show that in the case of lignite, although the heating value of the product gas was higher under a carbon dioxide atmosphere, the reaction under a steam atmosphere increased conversion and syngas yield. On the other hand, for barley straw fuel, upon carbon dioxide gasification, conversion and syngas yield were enhanced, whereas the heating value of the product gas was also higher, compared to steam gasification.
## 4 GPS L1/L5 Receiver: L5 Pattern Assumption
### Availability and GDOP
Indeed, in this portion, the availability for L5 pattern 3 is almost half that for the other two patterns, which is reflected in the mean number of satellites, 7.13, 6.21, and 3.74, respectively, for the first, second, and third assumed L5 patterns. For the other two portions the statistics are quite similar; for instance, less than one satellite is the difference of the mean number of observations among the three considered assumptions. The smaller availability during portion 3 is probably related to the big drop in the gain pattern of L5 pattern 3 between 35\({}^{\circ}\) and 50\({}^{\circ}\) elevation, as shown in Figure 5.
## 3 Method
### Data Preprocessing  
To enhance the convergence of the neural network and improve the generalization ability of the model, the DDM is normalized. The DDM can be normalized by using the following formula [42]:  
\[DDM_{normal}(\tau,f)=DDM(\tau,f)/DDM_{\max} \tag{5}\]  
where \(DDM_{normal}(\tau,f)\) denotes the normalized pixel value; \(DDM(\tau,f)\) denotes the raw pixel value; and \(DDM_{\max}\) denotes the maximum pixel value in the raw DDM.  
The auxiliary parameters also need to be standardized between 0 and 1. The normalization of the auxiliary parameters can be described as follows [42]:  
\[A(i)_{normal}=A(i)/A(i)_{\max} \tag{6}\]  
where \(A(i)\) denotes the \(i\)-th auxiliary parameter; \(A(i)_{\max}\) denotes the maximum value of the \(i\)-th auxiliary parameter; and \(A(i)_{normal}\) denotes the normalized auxiliary parameter.  
Data quality management is crucial to the reasonable use of data during construction of a neural network SSH retrieval model. To guarantee the quality of the dataset, quality control of the CYGNSS dataset is performed according to the following criteria [22].  
1. Nan samples are all discarded.
2. DDM observations should be non-negative.  
Figure 3: Schematic diagram of the structure of the new MRMDL model.  
* All land data should be excluded.
* Data quality control (QC) flags provided by CYGNSS's L1 level data set: quality_flags = 0, quality_flags_2 = 0.
* The BRCS uncertainty is below 1.
* The range correction gain of DDM is greater than 3
* DDM Signal-to-noise Ratio (SNR) is larger than 5.
* The elevation angle is greater than 60\({}^{\circ}\).  
After quality control and data filtering, the daily data were down sampled to alleviate the computational stress and overfitting problems. A total of about 1,071,836 data samples with SSH range between [\(-\)100, +80] m were obtained.
###### Abstract  
Limited studies have been conducted on the effect of a magnetic field on the corrosion behavior of steels. Investigating the effect on pipeline material in the oil and gas industries will be beneficial regarding corrosion prediction and control. In this work, the effect of a magnetic field on the corrosion process of API 5L X65 carbon steel was investigated in a well-developed flow loop using potentiodynamic polarization curves and electrochemical impedance spectroscopy (EIS). Using permanent magnets and a well-designed corrosion electrode set-up, the corrosion mechanism of API 5L X65 steel was studied at different magnetic orientations and different flow conditions in a NaCl solution. The surface morphology of the corroded samples was studied using a scanning electron microscope, and the micro-morphologies of the corrosion deposits and the surface elemental composition were analyzed. The results show that the presence of a magnetic field increases the corrosion rate of API 5L X65 carbon steel, and that flow velocities and magnetic orientation have a significant influence on the anodic corrosion current. The results of the polarization curves indicate a negative shift in the Tafel curve, leading to an increase in the corrosion rate with the introduction of a magnetic field in the flow system. The results of the EIS show that the charge transfer rate is decreased when a magnetic field is applied. This work provides important direction in terms of the understanding of the combined effect of magnetism and flow on the corrosion in pipelines used in the oil and gas industries.  
m pacs: 29 August 2021  
4 October 2021  
Published: 8 October 2021  
**Published's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Improved DQN for Dynamic Obstacle Avoidance and Ship Path Planning
## 2 Related Work
### Algorithm of Path Planning  
The goal of ship collision avoidance is to complete the navigation planning of the current and target position of the ship in combination with the external environment and the ship situation in the case of restricted navigation and obstacles. The obstacle avoidance problem of ships is that the ship perceives the unknown environment during the navigation process to avoid adverse factors and achieve the goal of safe navigation. During navigation, the operator will adjust the local path of the ship. Traditional ship collision avoidance algorithms, such as \(A^{*}\)[19], artificial potential field [26], RRT [27], and other models have limitations such as being prone to fall into local optimal solutions, low search efficiency, or slow calculation speed.  
Although the research on intelligent collision avoidance of ships has made significant progress in recent years, the above algorithms have their advantages. However, no intelligent collision avoidance method that can replace human pilots has been found with the extensive application of deep reinforcement learning in robots, games, image processing, medicine, and other fields [28; 29; 30; 31]. In particular, deep reinforcement learning has made a breakthrough in intelligent driving and uncrewed vehicles. The successful application of reinforcement learning in the field of intelligent vehicles has quickly extended its experience to collision avoidance algorithms in the field of intelligent ships. The reinforcement learning method has developed rapidly in intelligent navigation because of its simple structure and strong adaptability. It was first proposed by [PERSON], whose essence is to learn from the interaction. In recent years, especially the Deep Q-Network (DQN) algorithm proposed by the Google team has caused a boom in theoretical research and technical implementation of reinforcement learning. In the shipping industry, reinforcement learning is widely used in collision avoidance to integrate the navigation prior knowledge to design the ship's perception state and reward function, design the action space based on the course control, and apply the DQN method to the ship collision avoidance. [PERSON] et al. [32] developed a new intelligent collision avoidance algorithm based on approximate representation reinforcement learning (AR-RL) and applied it to ship collision avoidance in continuous space.  
Figure 2: Dynamic environment modeling of path planning.  
[PERSON] et al. [33] proposed a multi-ship automatic collision avoidance method based on a double deep Q network (DDQN) with prioritized experience replay.
# In-Motion Coarse Alignment Method Based on Position Loci and Optimal-REQUEST for SINS
## 4 Attitude Determination Using the Optimal-REQUEST Algorithm
### Algorithm Summary  
Compared with the REQUEST algorithm, the OP-PL algorithm uses the updated estimation error to define the special cost function and calculates the scalar gain \(\rho_{k+1}^{*}\). Thus, the OP-PL can better reduce the impact of GPS measurement errors which are contained in the observation vectors. For clear explanation, the whole in-motion coarse alignment procedure is shown in Table 1 and Figure 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Initialization** & \(k=0\), \(m_{0}=\delta m_{0}\), \(\rho_{0}^{*}=1\), \(\mathrm{C}_{n(0)}^{0}=\mathrm{C}_{b(0)}^{b0}=\mathrm{I}_{3}\), \(\mathrm{K}_{00}=\delta\mathrm{K}_{0}\), \(\mathrm{P}_{00}=\Re_{0}\) \\ \hline Step 1 & \(k=k+1\) \\ \hline Step 2 & Update \(\mathrm{C}_{n(t)}^{n(0)}\) and \(\mathrm{C}_{b(t)}^{b(0)}\) using Equations (2) and (3) \\ \hline Step 3 & Calculate and normalize \(\alpha_{p}\) using Equations (15)–(18) \\ \hline Step 4 & Calculate and normalize \(\beta_{p}\) using Equations (27) and (28) \\ \hline Step 5 & Calculate \(\delta\mathrm{K}_{k}\) and \(\Re_{k}\) using Equations (30)–(32) and (44)–(48) \\ \hline Step 6 & Calculate \(\rho_{k}^{*}\) and \(m_{k}\) using Equations (62) and (35) \\ \hline Step 7 & Update \(\mathrm{K}_{k/k}\) using Equation (34) \\ \hline Step 8 & Calculate the attitude matrix \(\mathrm{C}_{b(0)}^{n(0)}\) \\ \hline Step 9 & Calculate the attitude matrix \(\mathrm{C}_{b(k)}^{n(k)}\) \\ \hline Step 10 & Back to the Step 1 until the end \\ \hline \end{tabular}
\end{table}
Table 1: In-motion coarse alignment based on the OP-PL algorithm.
## 5 Results and Discussion
### Experimental Validation of Quantitative Reliability Calculations
#### 5.2.3 Quantitative Calculations of Reliability of Sea to Underwater Missions  
In the GO model of the ship electronic information system for reliability of the sea-to-underwater mission, operator 1 is a type 5 operator, and according to the aforementioned formula, operator 1 calculates the cumulative probability of the state as shown below.  
\[A_{R1}(1)=P_{S1}(1) \tag{41}\]  
Operators 13 and 14 are two-state units, and operator 32 is a linear combination generator with a combination probability of \([0.6,0.4]\), so that the cumulative probability of success for the sea-to-underwater mission, i.e., the state cumulative probability output of operator 32, can be introduced as follows.  
\[A_{R32}(1)=0.6A_{R12}(1)P_{C13}(1)+0.4A_{R12}(1)P_{C14}(1) \tag{42}\]  
Integrating the formula for the cumulative probability of states for \(A_{R12}(1)\), the formula for the success rate \(A_{R32}(1)\) to underwater missions is expressed as the following:  
\[\begin{array}{l}A_{R32}(1)=0.6A_{R12}(1)P_{C13}(1)+0.4A_{R12}(1)P_{C14}(1)\\ =[0.4P_{C13}(1)+0.6P_{C14}(1)][[P_{S1}(1)+P_{S2}(1)-P_{S1}(1)P_{S2}(1)]^{2}A_{ R22}{}^{2}(1)A_{R25}{}^{2}(1)\\ A_{R28}{}^{2}(1)A_{R31}(1)P_{C4}(1)^{2}P_{C5}(1)P_{C6}(1)P_{C7}(1)P_{C8}(1)P_{C 9}(1)P_{C10}(1)P_{C11}(1)P_{C12}(1)]\end{array} \tag{43}\]  
Figure 12: Results of quantitative calculations of the GO model for the sea-to-land mission.  
Substituting \(C_{1}=C_{2}=C_{3}=C_{4}=\lambda\beta=0.00075\) into the unit reliability data in Table 6, the probability of success for the sea-o-underwater mission is 0.8264.  
Then, based on the designed AADL transformation GO model, the unit reliability data in Table 6 is substituted into the GO model of the reliability of the underwater mission, based on the GO method analysis platform quantitative calculation results are shown in Figure 13, the system output is 32-1, and the calculated value of the mission reliability is 0.8264.  
The mission requirement is that the mission success rate should be more than 0.8, and after calculation, it can be seen that the mission reliability requirements for the sea-to-air mission, sea-to-land mission, and sea-to-underwater mission are all met. This fully confirms the feasibility and effectiveness of the method proposed in this study for the quantitative calculation of the reliability of various missions of reconfigurable ship electronic information systems.
# Multidimensional Study on Users' Evaluation of the KRAKEN Personal Data Sharing Platform
## 3 Results
### Sharing Health or Education Data  
Participants to the health pilot were asked \"Would you be willing to share your data product with other entities through the KRAKEN platform if it's pseudo anonymized, anonymized (e.g., universities pharmaceutical companies, private organizations, research institutions)? Why and why not?\" Six participants expressed willingness to share anonymized data products, while six participants replied that they would be willing to share data only for research purposes, not for commercial purposes, since they were motivated mainly by ethics reasons for sharing personal data. Three participants explicitly mentioned they would share data with public entities (universities, research foundations) for research goals, but not with private entities, such as pharmaceutical companies. Participants were also asked \"Which kinds of entities would you be willing to share it with?\" In response, seven participants mentioned they would share their data with an entity if they clearly knew the purpose of the data usage, two participants said they would share data in case they can keep control over their data and can even revoke access to data, two participants mentioned ethics reasons (e.g., contributing to improving healthcare treatments), and one participant said they would be motivated by receiving some form of acknowledgement or credit for sharing their data.  
Participants to the education pilot were asked \"What types of data do you feel comfortable sharing/selling? What types of data should the education pilot support in addition to the ones we support?\" All three participants expressed interest for sharing education data, such as CVs and diploma. They were willing to share also other personal data, such as passports, citizenship certificates, but they had more concerns in sharing for example health data. Participants were also asked \"Which kinds of entities would you be willing to share data with?\" In response, two participants said they would prefer to share data with universities, employers, government agencies, since this would facilitate bureaucratic processes in a secure and privacy preserving mode. One participant said he would be willing to share data with these entities if the KRAKEN platform provides guarantee that the user keeps control of his data, and the platform properly manages any possible system failure. We asked participants \"Will you feel comfortable letting other entities (organizations, universities) see your data using the KRAKEN platform? Do you think it will be secure?\" Here, all three participants stressed they would provide access to their education data to universities and state entities, rather than private companies that may want to access data with a commercial purpose. One participant said KRAKEN would allow to share data in a more secure way if compared with transfer of documents in hard copies, and it would support a better control of personal data by providers.