esa-sceva/satcom-chunk-collection huggingface dataset:
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
###### Abstract  
There is a critical need for a global monitoring capability for Tailings Storage Facilities (TSFs), to help protect the surrounding communities and the environment. Satellite Synthetic Aperture Radar Interferometry (InSAR) shows much promise towards this ambition. However, extracting meaningful information and interpreting the deformation patterns from InSAR data can be a challenging task. One approach to address this challenge is through the use of data science techniques. In this study, the representation of InSAR metadata as Entity Embeddings within a Deep Learning framework (EE-DL) is investigated for modelling the spatio-temporal deformation response. Entity embeddings are commonly used in natural-language-processing tasks. They represent discrete objects, such as words, as continuous, low-dimensional vectors that can be manipulated mathematically. We demonstrate that EE-DL can be used to predict anomalous patterns in the InSAR time series. To evaluate the performance of the EE-DL approach in SAR interferometry, we conducted experiments over a mining test site (Cadia, Australia), which has been subject to a TSF failure. This study demonstrated that EE-DL can detect and predict the fine spatial movement patterns that eventually resulted in the failure. We also compared the results with deformation predictions from common baseline models, the Random Forest model and Gaussian Process Regression (GPR). Both EE-DL and GPR greatly outperform Random Forest. While GPR is also able to predict displacement patterns with millimetric accuracy, it detects a significantly lower number of anomalies compared to EE-DL. Overall, our study showed that EE-DL is a promising approach for building early-warning systems for critical infrastructures that use InSAR to predict ground deformations.  
InSAR; tailings dam; tailings dam failure; deep learning; entity embeddings; Gaussian Process Regression 2023154910[[https://doi.org/10.3390/rs15204910](https://doi.org/10.3390/rs15204910)]([https://doi.org/10.3390/rs15204910](https://doi.org/10.3390/rs15204910))
# Inter-Subject MEG Decoding for Visual Information with Hybrid Gated Recurrent Network
## 5 Conclusions  
In this paper, a hybrid gated recurrent network (HGRN) is proposed for inter-subject MEG decoding with visual information. Evoked by visual stimulations, the MEG signals were recorded by an equipment with hundreds of channels at a high sampling rate. By building a multi-layer recurrent structure, HGRN model can capture the features of MEG signals of different subjects. In the cross-validation experiment on a public database with 16 subjects, the proposed HGRN model achieved higher performance than many existing methods. Moreover, based on the HGRN, the spatial feature of MEG signals of each subject can be extracted for analysis. According to our experiment, we found that only the signals in occipital area play important roles in the decoding procedure which conformed to many previous studies. The above experimental results and analysis show that the proposed HGRN model can extract the features of MEG signals and effectively improve visual MEG decoding performance. In the future, we would like to build more efficient networks to model brain signals in multiple modalities. Moreover, some new emerging machine learning techniques can be a new inspiration for the methodology in brain-computer interface as well as neuroscience research.  
[PERSON] proposed the idea, conducted the experiments, and wrote the manuscript. [PERSON] provided advice on the research approaches and signal processing. [PERSON] checked and revised the manuscript. [PERSON] offered important help that guided the experiments and analysis methods. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under grant 62006082, 62076103 and 61836003, the Key Realm R and D Program of Guangzhou under grant 202007030005, the Guangdong Natural Science Foundation under grant 2020A1515110294, the SCNU Research Fund under grant 19 KJ02, Guangdong Natural Science Foundation Doctoral Research Project (2018A030310365) and International Cooperation open Project of State Key Laboratory of Sub  
Figure 7: Typical MEG signals evoked by face or scrambled face stimulation.  
tropical Building Science, SCUT (2019 ZA02), and the Guangdong General Colleges and Universities Special Projects in Key Areas of Artificial Intelligence of China under Grant 2019 KZDZX1033.  
The study mainly involves an open dataset and proposed a new algorithm to process the dataset that did not directly involve humans or animals.  
The study did not directly involve humans.  
The used dataset is available at [[https://www.kaggle.com/c/decoding-the-human-brain](https://www.kaggle.com/c/decoding-the-human-brain)]([https://www.kaggle.com/c/decoding-the-human-brain](https://www.kaggle.com/c/decoding-the-human-brain)) and [[https://www.openfmri.org/dataset/ds000117/](https://www.openfmri.org/dataset/ds000117/)]([https://www.openfmri.org/dataset/ds000117/](https://www.openfmri.org/dataset/ds000117/)).  
The authors declare no conflict of interest.
## 3 Results
### Electrical Percolation Threshold of MWCNT/PDMS under Magnetic Forces  
MWCNT/PDMS nanocomposites were prepared with MWCNT concentrations ranging from 0.01 to 5 wt%. The impact of applying a magnetic field upon the MWCNT distribution in the nanocomposite, and thus on the nanocomposite's electrical conductivity, was evaluated. The electrical resistivity of the samples was determined using Equation (1):  
\[\rho=\text{R}\times\text{A}/1 \tag{1}\]  
where \(\rho\) is the electrical resistivity of the nanocomposite sample, R is the electrical resistance between electrodes, A is the cross-sectional area of the sample, and 1 is the distance between the electrodes.  
The nanocomposites' electrical resistivity, prepared with the magnetic field (magnetic orientation) and without magnetic field (random distribution), is presented in Figure 6, defining the electrical percolation threshold obtained in each case.  
Figure 6 depicts the impact of the magnetic field's application on the nanocomposites' electrical resistance. The magnetic field application reduces the MWCNT concentration at the electrical percolation threshold from 0.85-1 wt.% to 0.01-0.1 wt.% MWCNT. The longitudinal images of the nanocomposites obtained by digital microscopy show the effect of the magnetic field on the nanocomposite morphology, as illustrated in Figure 7a,b. The magnetic field forces the localisation of the nanoparticles near the magnet, increasing the contact between conductive particles. A numerical simulation was performed to predict the localisation of the MWCNTs relative to the magnet. The simulation was performed using ANSYS Maxwell software to illustrate the effect of the magnetic field on two stacked permanent magnets. Figure 7c,d show that magnetic flux density is predicted to be greater at the magnet's edges.  
To confirm the magnetic flux variation between the edges and centre of the magnet, the magnetic flux density along the black line drawn in Figure 7c was calculated and represented in Figure 7d. The magnetic flux density is observed to decrease from the edges  
Figure 6: Electrical resistivity of MWCNT/PDMS nanocomposites prepared with and without magnetic alignment.  
Figure 7: Magnetic pattern in MWCNT/PDMS samples with concentrations of (**a**) 0.01 and (**b**) 0.1 wt.% and (**c**) magnetic flux density profile on the surface of a permanent neodymium magnet, with (**d**) magnetic flux density in the region of the black line.  
to the centre of the magnet, which coincides with the MWCNT localisation on the PDMS matrix, showing that the MWCNT are attracted to the locations with higher magnetic flux.  
The curves depicting the electrical percolation threshold for the nanocomposites in Figure 6 show that at 5 wt.% MWCNTs, the nanocomposite's electrical resistivity is similar with and without applying a magnetic field. At this concentration and above, the conductivity of the nanomaterial is so high that its distribution in the nanocomposite does not affect the overall electrical resistivity.
## 6 Path Planner Performance Tests
### SITL Test  
Gazebo simulation environments shown in Figure 6 (front view) and Figure 7 (top views) were built, in which the path planner was to be tested.  
The gazebo world has left and right sections. Each section has a width (\(y\)-axis) of 10 m and a length (\(x\)-axis) of 30 m. The UAV located in the left section has to avoid the obstacles on its mission to arrive at landing pad, which is located in the right section. During path planner performance tests, the poles and trees were randomly re-located in the simulation environment. Every time the arrangements of these obstacles are changed, the path followed by the UAV changes. Figure 7c,d shows two traced trajectories for the obstacles' arrangements shown in Figure 7a,b, respectively.  
The 3D models of the obstacles imported to gazebo world were pedestrian, open window, poles, tunnel, trees, and two consecutive nets. The obstacles were designed in consideration of UAV mission for in-house first aid, law enforcement during suspect monitoring and door-to-door package delivery services in urban areas where the aforementioned obstacles are assumed to be potential threats in such missions. The UAV is supposed to pass by or through these obstacles on its way to a targeted location, in this case, the landing pad.  
Figure 6: Front view of gazebo environment  
The overall simulation infrastructure is shown in Figure 8. The 3D path planner written as ROS node communicates with the PX4 module named Mavlink_main. MAVROS bridges the ROS topics of the path planner with MAVLink messages of PX4 firmware. In addition to bridging ROS topics with MAVLink messages, MAVROS has extra-advantage in taking care of coordinate transformation between the ROS frame and PX4 Flight Control Unit (FCU) frame. ROS works with the East-North-Up (ENU) frame, and FCU works with the North-East-Down (NED) frame. PX4 firmware has a module called simulator_maxlink that lets the firmware interact with the 3D model of the UAV in the Gazebo world. The message exchanges between the PX4 firmware and gazebo simulator are handled by simulator MAVLink protocol.  
As part of the 3D path planner's efficiency verification tests, video (named as Video S1) is submitted with this manuscript. The livestreamed videos on dgroundcontrol (a ground control station for UAVs) were recorded, and the snapshots of a video at the instants of attitude or altitude changes, to avoid obstacles, are displayed in Figure 9.  
Figure 7: Top views of two simulation environments and traced trajectories.  
Figure 8: Software_In_The_Loop infrastructure.  
Figure 9: Instant snapshots during obstacle avoidance phases.  
The position and attitude accuracy for the environments Figure 7c,d are shown in the first and second columns of Figure 10, respectively.
###### Abstract  
Underwater acoustic sensor networks (UASNs) play an increasingly crucial role in both civilian and military fields. However, existing routing protocols primarily rely on node position information for forwarding decisions, neglecting link quality and energy efficiency. To address these limitations, we propose a fuzzy logic reasoning adaptive forwarding (FLRAF) routing protocol for three-dimensional (3D) UASNs. First, the FLRAF method redefines a conical forwarding region to prioritize nodes with greater effective advance distance, thereby reducing path deviations and minimizing the total number of hops. Unlike traditional approaches based on pipeline or hemispherical forwarding regions, this design ensures directional consistency in multihop forwarding, which improves transmission efficiency and energy utilization. Second, we design a nested fuzzy inference system for forwarding node selection. The inner inference system evaluates link quality by integrating the signal-to-noise ratio and some metrics related to the packet reception rate. This approach enhances robustness against transient fluctuations and provides a more stable estimation of link quality trends in dynamic underwater environments. The outer inference system incorporates link quality index, residual energy, and effective advance distance to rank candidate nodes. This multimetric decision model achieves a balanced trade-off between transmission reliability and energy efficiency. Simulation results confirm that the FLRAF method outperforms existing protocols under varying node densities and mobility conditions. It achieves a higher packet delivery rate, extended network lifetime, and lower energy consumption. These results demonstrate that the FLRAF method effectively addresses the challenges of energy constraints and unreliable links in 3D UASNs, making it a promising solution for adaptive and energy-efficient underwater communication.  
UASNs; fuzzy logic; adaptive forwarding; routing protocol +
Footnote †: journal: Marine Science and Engineering  
_Article_  
A Three-Dimensional Routing Protocol for Underwater Acoustic Sensor Networks Based on Fuzzy Logic Reasoning  
Fuzzy Logic Reasoning _J. Mar. Sci. Eng._**2025**, _13_, 692. [[https://doi.org/10.3390/jmsc13040692](https://doi.org/10.3390/jmsc13040692)]([https://doi.org/10.3390/jmsc13040692](https://doi.org/10.3390/jmsc13040692))
# A Multi-Scale Attention Fusion Network for Retinal Vessel Segmentation
## 2 Related Works
### Attention Mechanism  
[PERSON] et al. [15] introduced the Transformer architecture, allowing the model to attend to different parts of a sequence without being constrained by the sequence length, thus enhancing the model's ability to handle long-range dependencies. Subsequently, attention mechanisms have found widespread application in computer vision tasks [16; 17; 18; 19; 20; 21; 22; 23; 24].  
Researchers have proposed many methods for retinal vessel segmentation that combine attention mechanisms with the U-Net backbone. [PERSON] et al. [25] proposed a cascaded U-Net framework to progressively extract features at different hierarchical levels from images. They introduced a residual attention block, incorporating an attention mechanism to enhance the network's focus on crucial image regions, thereby improving the precision of vessel segmentation. The experimental results on the DRIVE dataset and CHASE_DB1 dataset demonstrated its outstanding segmentation performance. [PERSON] et al. [26] introduced SA-UNet for retinal vessel segmentation. SA-UNet replaces the original convolutional blocks in the U-Net framework by incorporating DropBlock [27] and batch normalization. Additionally, a spatial attention module is integrated between the encoder and decoder. The segmentation performance of SA-UNet is excellent on the DRIVE dataset and CHASE_DB1 dataset.  
However, despite the good performance exhibited by U-Net and its variants in the field of medical image segmentation, increasing the network depth may lead to issues such as the loss of crucial information due to multiple pooling operations and the inadequate handling of local contextual features caused by skip connections. These issues can have an impact on the accurate segmentation of retinal vessels.
## 5 Algorithms and Standard Products for Inferring Crop-Biophysical Characteristics
### Vegetation Fraction (VF)  
VF is useful as an indicator of not only crop phenological status but also of final crop yield. Reflectances in only the visible range of the spectrum have been used to quantitatively estimate VF. For close-range sensing, we use the modified Visible Atmospherically Resistant Vegetation Index (VARI) with a red (660-680 nm) band and bands in the either green (540-560 nm) or red edge (700-710 nm) region [17]:  
\[mVARI_{green}=\big{(}\rho_{green}-\rho_{red}\big{)}/\big{(}\rho_{green}+ \rho_{red}\big{)} \tag{2}\]  
\[mVARI_{red\ edge}=\big{(}\rho_{red\ edge}-\rho_{red}\big{)}/\big{(}\rho_{red \ edge}+\rho_{red}\big{)} \tag{3}\]  
To make the indices resistant to atmospheric effects, a blue spectral band was employed [17]:  
\[VARI_{green}=\big{(}\rho_{green}-\rho_{red}\big{)}/\big{(}\rho_{green}+\rho_ {red}-\rho_{blue}\big{)} \tag{4}\]  
\[VARI_{red\ edge}=\big{(}\rho_{red\ edge}-1.7\rho_{red}+0.7\rho_{blue}\big{)}/ \big{(}\rho_{red\ edge}+2.3\rho_{red}-1.3\rho_{blue}\big{)} \tag{5}\]  
where \(\rho_{blue}\) is in the range 470-480 nm, \(\rho_{green}\) is in the range 540-560 nm, \(\rho_{red\ edge}\) in the range 700-710 nm, The error in estimating VF using VARI has been documented to be less than 10% [17; 18].  
The VARI was also used for evaluation the phenological development of maize [18], and it allowed for the identification of the timing of phenological transitions that are related with the maize physiological development. The index also allowed identification of the onset of the grain fill period, which is important since maximum yield potential of maize plants depends on optimal environmental conditions during this period.
## 3 Approaches for Screening the ORR and HER Performances  
### Evolution of ORR Performance  
Commonly, the ORR performance of any electrocataly is evaluated by three major aspects: (i) mass activity, (ii) specific activity, and (iii) onset and half-wave potential. The rotating disk electrode (RDE) method is a widely accepted approach to measure the aforementioned parameters. This section includes a detailed discussion of these approaches:  
**(i)**: **Mass activity**: The mass activity (MA) is the most important parameter to evaluate the ORR performance of any catalyst, which is the measure of performance per unit loading of metal. Generally, the MA is calculated by the following equation:  
\[mass\ activity\left(mA\ mg^{-1}\right)=J_{k}\times\frac{area}{mass\ of\ catalyst} \tag{15}\]  
where \(J_{k}\) is the kinetic current density (mA/cm\({}^{2}\)) and the area is the geometric area of the working electrode. The mass activity of the catalyst is estimated via the calculation of \(J_{k}\) and normalization to the catalyst loading on the glassy carbon rotating disk electrode.
**(ii)**: **Specific activity**: The specific activity (SA) indicates the number of active sites presents on the surface of the catalyst. The electrochemically active surface area (ECSA) plays an important role in evaluating the SA. The ECSA of the experimental catalysts can be calculated by acquiring the columbic charge for reduction of the monolayer Pt or Pd oxide after integration and double-layer correction using the following equation:  
\[ECSA=\frac{Q_{Pt/Pd}}{Q_{ref}\times m} \tag{16}\]  
where \(Q_{ref}\) denotes the charge required for reduction of the monolayer oxide from the bright Pt surface (i.e., 0.405 mCcm\({}^{-2}\)), m stands for metal loading, and \(Q_{Pd}\) represents the charge required for oxygen desorption, which is calculated by the following equation:  
\[Q_{Pd}=\frac{1}{\
u}\int(I-I_{d})dE \tag{17}\]  
where \(\
u\) is the scan rate for cyclic voltammetry (CV) analysis and integral parts refer to the area under the Pd oxide reduction peak in the CV curves. The kinetic current density (\(J_{k}\)) and the number of electrons transferred in ORR are calculated based on the equations below:  
\[\frac{1}{J}=\frac{1}{J_{K}}+\frac{1}{J_{L}}=\frac{1}{J_{K}}+\frac{1}{B\omega^{ 0.5}} \tag{18}\]  
\[B=0.62 nF{C_{0}}_{2}{D_{0}}^{\frac{2}{3}}{v^{-\frac{1}{6}}} \tag{19}\]  
where \(J\), \(J_{k}\), and \(J_{L}\) are the experimentally measured, mass transport free kinetic, and diffusion-limited current densities, respectively, \(\omega\) is the angular velocity of the electrode, \(n\) is the transferred electron number, \(F\) is the Faraday constant, \(C_{02}\) is the bulk concentration of O\({}_{2}\), \(D_{02}\) is the diffusion coefficient, and \(v\) is the kinematic viscosity of the electrolyte. The SA can be obtained when \(J_{K}\) is normalized to the Pd ECSA.  
**Onset and half-wave potential:** The onset potential (Eoc) and half-wave potential (E\({}_{1/2}\)) are the techniques for the quick screen of ORR performance. Both of these parameters can be obtained from the linear sweep voltammetry (LSV) curve (Figure 2).
## 4 Results
### Snow Depth Retrieval
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Photoscan**} & \multirow{2}{*}{**Points Cloud Density**} & \multirow{2}{*}{**DEMs Final**} & \multirow{2}{*}{**Total Error**} \\
**Area** & & & & & & **(Points m\({}^{-2}\))** & & & **Resolution (m)** & & **(RMSE) (m)** \\
**Name** & & & & & & & & & & \\ \cline{2-10}  & **SDEM** & **TDEM** & **SDEM** & **TDEM** & **SDEM** & **TDEM** & **SDEM** & **TDEM** \\ \hline
**Sval1** & 0.003 & 0.003 & _Medium_ & _Medium_ & 1337.5 & 273.6 & 0.03 & 0.06 & 0.09 & 0.057 \\
**Sval2** & 0.004 & 0.003 & _Medium_ & _Medium_ & 801.5 & 301.1 & 0.04 & 0.09 & 0.044 & 0.054 \\
**Green1** & 0.003 & 0.009 & _Medium_ & _High_ & 1518.5 & 163.7 & 0.04 & 0.1 & 0.057 & 0.23 \\
**Green2** & 0.001 & 0.006 & _Low_ & _High_ & 4284.5 & 80.0 & 0.02 & 0.09 & 0.045 & 0.093 \\
**Green3** & 0.003 & & _Low_ & _High_ & 4413.1 & & 0.02 & 0.09 & 0.05 & 0.086 \\
**Green4** & 0.004 & 0.021 & _Low_ & _High_ & 3867.8 & 133.2 & 0.03 & 0.09 & 0.04 & 0.086 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the Photoscan reconstruction quality and details of analysis for all survey areas. Shown is the ground sample distance (GSD) for the acquired imagery and the final resolution of the Digital Elevation Models (DEMs). The total error represents the Root Mean Square Error (RMSE) of the Euclidean distance from the reference GCPs to the corresponding points estimated in the 3D model. SDEM and TDEM refer to snow and terrain, respectively. and in Figure 2f. It is plausible that this led to the considerable underestimation of the \(HS_{UAS}\) for these points. This possibility is also suggested by the Green2 snow-free validation histogram in Figure 5 that displays a clear bias towards negative \(HS_{UAS}\). It is also suspected that the lack of co-georeferencing for this area could have affected the results. Difference between the individual DEMs total errors (Table 3) and the snow depth validation statistics (Table 4) confirms that the co-georeferencing plays an important role in mitigating the error on the estimated \(HS_{UAS}\) by rendering the DEMs subtraction relative to their own reference system. Figure 4: Produced snow depth maps for all the surveyed areas, displayed over their underlying terrain reliefs. Maps were filtered from outliers outside the displayed color bar range (scattered and isolated pixels) for each specific map. The maps include the location of the validation points (VPs) that measured the difference between probed snow depth (\(HS_{m}\)) and the estimated snow depth (\(HS_{UAS}\)) locations in meters (m) (cross marks). This figure showcases the overall feasibility and performance of the proposed low-cost method to produce snow depth maps. It is noted that sectors distant from the GCP network are expected to be less representative of the actual snow depth due to inferior georeferencing.
## 6 Results and Discussions
### Access Point 1  
The number of bytes transmitted and received by Access Point 1 when using the proposed and shortest-path routing methods is shown in Figures 7a and 8a, respectively. The data traffic of Access Point 1 when using each of the evaluated routing protocols is as follows:  
* Shortest-path routing method: The bytes are transmitted from Access Point 1 to Access Point 4 through Access Point 2.
* Proposed SDN-based energy-aware routing method: Access Point 1 transfers bytes to Access Point 2 or Access Point 3 depending on the SDN controller's decisions. Regardless the transmitted bytes are received by Access Point 2 or Access Point 3, Access Point 1 transmits bytes during the whole communication time as shown in Figure 7a.  
Figure 10: Receiving energy.  
Finally, the transmission energy consumption of Access Point 1 is shown in Figure 9a for the proposed and shortest-path routing methods. It is the same in both cases since the transmission of Access Point 1 lasts the whole communication time.