esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Signal Model
### Mathematical Model  
For the distributed SAR, range compression and motion compensation based on the inertial navigation system (INS) are performed first. If there is only an azimuth delay between the signal echo of \(a_{m}^{\prime}\), i.e., \(s^{\prime}{}_{m}(\tau,\eta)\), and the echo signal of \(a_{0}\), then the signal at \(a_{m}\) can be expressed as  
\[\begin{array}{l}s_{m}(\tau,\eta)=\Gamma_{m}\exp(j\phi_{m})\\ \exp\left(-j\frac{4\pi\Delta R_{m,m}(\tau)}{\lambda}\right)\exp\left(-j\frac{4 \pi\Delta R_{m,m}(\tau)}{\lambda}\right)\exp\left(-j\frac{4\pi\Delta R_{m}( \eta)}{\lambda}\right)\\ s_{m}^{\prime}\left(\left(\tau-\Delta\tau_{m}-\frac{2\Delta R_{m,m}(\tau_{0}) }{c}-\frac{2\Delta R_{m,m}(\tau_{0})}{c}\right),\eta\right)\end{array} \tag{1}\]  
where \(\tau\) represents fast time, \(\tau_{0}\) is the fast time corresponding to the scene center, \(\lambda\) is the wavelength, \(c\) is light speed, \(\Gamma_{m}\) represents the channel amplitude error, and \(\phi_{m}\) represents the channel inherent phase error. \(x_{m,m,c}\) and \(z_{m,m,c}\) denote the measured values of the vertical CTB and the horizontal CTB of \(a_{m}\), respectively. \(x_{m,\sigma}\) and \(z_{m,\sigma}\) denote the measured errors of the vertical CTB and the horizontal CTB. \(v_{c}\) is the platform's radial velocity error and \(a_{c}\) represents the platform's radial acceleration error. The slant range difference between the channels corresponding to the CTB \(\Delta R_{m,m\epsilon}(\tau)\), the slant range difference corresponding to the measurement error of the CTB \(\Delta R_{m,m\epsilon}\), and the slant range difference corresponding to the motion error are expressed as follows:  
\[\Delta R_{m,m\epsilon}(\tau)=z_{m,m\epsilon}\cos\varphi(\tau)-x_{m,m\epsilon} \sin\varphi(\tau) \tag{2}\]  
\[\Delta R_{m,\epsilon r}=z_{m,\epsilon r}\cos\varphi(\tau_{0})-x_{m,\epsilon r }\sin\varphi(\tau_{0}) \tag{3}\]  
\[\Delta R_{m\epsilon}(\eta)=v_{c}\eta+\frac{1}{2}a_{c}\eta^{2} \tag{4}\]  
where  
\[\varphi(\tau)=\arccos\left(\frac{2H}{c\tau}\right) \tag{5}\]  
Figure 1: The geometry of the distributed SAR.  
The following assumptions are made:  
Firstly, the platform flight attitude maintains the remaining constant radial velocity and the radial acceleration within the sub-aperture.  
The channel envelope migration and phase error due to the CTB of the channel are azimuth time invariant. The second-order phase error due to motion error is time variant, but the coefficients are constant.  
Secondly, the envelope migration corresponding to the CTB is constant within the scene.  
The slant range difference corresponding to the CTB varies with fast time \(\tau\), leading to a range-variant phase error. However, the range variance of envelope migration caused by the CTB is much less than half of the range cell in the imaging scene and can be considered constant.
## 4 Results and Discussion  
### Dataset Description and Assessment  
To train the deep learning model and evaluate the learning ability of the model, the point clouds data of road scenes need to be labeled with the corresponding tags. To obtain datasets with features of different road scenes, the point clouds data of relevant road sections from different road scenes are selected as the training datasets for the deep learning model. Based on the self-developed mobile LiDAR system, the road point clouds of the dataset are based on the road data around Beijing city, including the Daxing Airport loop, Jingtai highway, Jingkai highway, and Beijing ring road (Figure 5b,c). To prevent overfitting from occurring, the research area is about 180 km long. The data collection of different types of roads is made into a road point clouds dataset, and the features are divided into the road surface, lane lines, guard trails, streetlights, signage, etc. During the acquisition process, the mobile LiDAR system collects point clouds data at 90-110 km/h. During the data processing, the automatic segmentation distance is 5 m, and the samples with high integrity are selected, with a total of 3968 samples. Each sample size is about 4 M, 85,000 points; 80% of the samples are used for training, 12% for testing, and 8% for validation.  
Figure 4: Structure of channel attention mechanism.  
The network model is built based on Python 3.6, and the main libraries used are PyTorch, CUDA 10.0, cuDNN 7.5, etc. All experiments were run in a GeForce RTX 2080 ti 11G environment. The Adam optimizer is used in the training phase, which requires less memory and has excellent acceleration for neural network training. The initial learning rate was set to 0.001, the batch size was set to 16, the learning rate decay rate was 0.7, and the step size (epoch) was set to 48.
# Simulation Study on an ICT-Based Maritime Management and Safety Framework for Movable Bridges
## 5 Methodology
### Measures for Collision Prevention
#### 5.3.2 DSRC Safety Command  
DSRC is a short-range communication system between vehicle to vehicle and vehicle to infrastructure. For the default DSRC system, the maximum range is 300 m [22] which has been improved up to 11 miles by the Utah Transportation Department. In this research, DSRC is maintained for communication between ships to bridge the control system. Using the DSRC system, a safety message warning system is maintained when ships move within a short distance of a movable bridge that is currently maintaining traffic flow. During this condition, the bridge will prioritize the safety of vehicles on the bridge and disregard the moving command. At the same time, the bridge control system will give a safety message using the DSRC system to stop any ships from moving too close. The message acts as a direct command to the ship operator before passing the Emergency Belt. The ship detection scenario at DSRC range and bridge impromptu condition is simulated and shown in Figure 12.  
A buoy is placed exactly D\({}_{\text{dsrc}}\) apart from the bridge Emergency Belt. In the figure, it is denoted as _Ba_ for route AB and _Ec_ for route CD. When a ship is about to pass the buoy, _Ba_, its position is detected, and a signal is transferred to the bridge control system. Subsequently, the bridge control system checks the evacuation condition. If the count at bridge evacuation is nonzero, there must be a vehicle remaining on the bridge, and the bridge considers itself unprepared to move for the safety of bridge traffic. Thus, the bridge control system of the bridge directly gives the safety command using the DSRC system to stop the ship before an Emergency Belt.  
Figure 12: Ship detection scenario at DSRC range and impromptu bridge condition.
## 3 Results
### SEM and \(\mu\)-CT Analysis
#### 3.2.1 Reference Cell  
The EDX results of the reference (Cell 6) are presented in Figure 4. The colors show the constituent elements of the anode. A good and homogeneous distribution of the elements Ni Gd and Ce is found. The grain size ranges between approx. 1 \(\upmu\)m near the interface and approx. 8 \(\upmu\)m (for a few) grains in the contact layer. These images are intended to serve as a comparison with the images of the other samples in series F in order to document microstructural changes as a function of thermal and electrochemical loading over time.  
The SEM images were also used for microstructure analysis, and an initial value for the 2D porosity was calculated using ImageJ(r) software 1.52d [40]. The porosity resulting from this analysis is, of course, only indicative, as the 2D image from which it is calculated is not necessarily representative of the total volume of the anode. The samples were examined using computer tomography to determine the total porosity, as shown in Figure 5.  
For the reference Cell 6, the total porosity (open and closed) resulting from the \(\upmu\)-CT is 31.58%. The 2D analysis resulted in a slightly increased porosity for the contact layer of 37.2% and a porosity of 29.8% for the active layer.
## 4 Methods
### Sample Extraction  
At uniform and highly-reflective clusters, resulting from thresholding and the intersection of the layers previously generated, 130 samples of 5 \(\times\) 5 pixels were collected from FASat-C and RapidEye imagery for cross-calibration. Additionally, for a preliminary evaluation of the results, 300 samples were randomly collected across the whole common area of both acquisitions, although it was not possible to maintain the same pattern, due to the lack of some land covers in the neighborhood of the site. Water bodies or dense vegetation with sufficient extension, according to the GSD of both sensors, were not available in Frenchman Flat; thus, the few vegetation samples identified cover areas of just 3 \(\times\) 3 pixels. Regarding the outlier detection, for both calibration and evaluation samples, the FASat-C digital numbers (DN) were plotted against RapidEye radiances, with outliers being considered the measurements more than two standard deviations (2\(\sigma\)) away from the best-fit line.
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 3 Equalizers for SDM
### Single-Mode 2 \(\times\) 2 Dynamic Equalization
#### 3.1.1 Least Mean Square  
The LMS equalization is one of the most well-known equalizers based on an approximation of the Wiener-Hopf solution [21]. The LMS error function is computed as the square difference between the equalized signal and the expected one for each polarization:  
\[f_{LMS}(h(n))=|\overline{x}(n)-\hat{x}(n)|^{2}+|\overline{y}(n)-\hat{y}(n)|^{2}, \tag{4}\]  
where \(\hat{x}\) and \(\hat{y}\) are the expected signals. The LMS equalizer can operate in two different ways: training mode and decision-directed (DD) mode. In training mode, transmitter and receiver agree in using a specific signal, called the training sequence, which is known by both. The training sequence is used for enabling the initial pre-convergence of the weights. After the training period, the equalizer switches to DD mode, directly applying decisions over the equalizer data to estimate the transmitted signals.  
According to the introduced description of the stochastic gradient descent update rule, the LMS algorithm has the following error coefficients:  
\[C_{1}(n)=2(\overline{x}(n)-\hat{x}(n))\overline{x}(n);C_{2}(n)=2(\overline{y} (n)-\hat{y}(n))\overline{y}(n). \tag{5}\]  
As expected, it is observable that \(C_{1}(n)\) and \(C_{2}(n)\) are only functions of \(x\) and \(y\) polarization respectively.
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 3 Effective Information Traffic
### Effective Information Traffic
\tag{4}\]  
In fact, the node does not need to calculate the current \(I(t)\) in real-time and does not need to update in real-time. Just recalculate the value of \(I(t)\)and add it to the hello packet before sending it. Therefore, it is required to recalculate the \(I(t)\) with \(t_{h}\) as cycle. In this paper, we set the reset interval as twice as the hello packet interval: \(T_{s}=2T_{h}=2t_{h}\). Under such a mechanism, \(I(t)\) can reflect the connectivity of a node and be considered in routing decisions. Figure 3 shows the variety of effective information traffic and number of neighbors for a node in a period of time. The node gradually moves from the center area to the edge area. It can be found that at the time of 0 s, the number of neighbors is 3, but there is no data is forwarded to the node, and the effective information flow is 0. The value of \(I(t)\) is increased after forwarding at 1 s and 2 s, with the number of neighbors is 3. At 3 s, 4 s, and 5 s, the \(I(t)\) decreased to 0.
# Shared Graph Neural Network for Channel Decoding
###### Abstract  
With the application of graph neural network (GNN) in the communication physical layer, GNN-based channel decoding algorithms have become a research hotspot. Compared with traditional decoding algorithms, GNN-based channel decoding algorithms have a better performance. GNN has good stability and can handle large-scale problems; GNN has good inheritance and can generalize to different network settings. Compared with deep learning-based channel decoding algorithms, GNN-based channel decoding algorithms avoid a large number of multiplications between learning weights and messages. However, the aggregation edges and nodes for GNN require many parameters, which requires a large amount of memory storage resources. In this work, we propose GNN-based channel decoding algorithms with shared parameters, called shared graph neural network (SGNN). For BCH codes and LDPC codes, the SGNN decoding algorithm only needs a quarter or half of the parameters, while achieving a slightly degraded bit error ratio (BER) performance.  
graph neural network; channel decoding algorithm; shared graph neural network; BCH codes; LDPC codes +
Footnote †: journal: Applied sciences
# Reactance Regulation Using Coils with Perpendicular Magnetic Field in the Tubular Core design
## 6 Conclusions  
In this article, we studied the special design of orthogonal magnetic coils to produce controllable impedance. The DC current one (control) coil regulates the impedance of another one (working) coil. The degree of magnetic independence between perpendicular coils is in the range of 0.015-0.017 only. The controllability is also verified with experimental investigations. The impedance of the working coil can alter 10-15 times the initial value through the control current. It is applicable for regulating the power flow between the grid and a consumer. The equivalent resistance of the working coil is responsible for the heating represented mainly by ferromagnetic losses. Henceforth, the investigation of ferromagnetic steels should be organized to develop new materials with more anisotropy of magnetizing curves and lower losses. The electrical influence between the grid and the consumer can be significantly diminished during fault situations, and the connection remains uninterrupted. The prosumers return to usual feeding without problems caused by non-synchronization between the phases/frequencies of generators and the grid.  
Conceptualization, S.R., A.S. and M.A.; methodology, S.R., E.L. and M.A.; formal analysis, S.R. and E.L.; investigation, resources, and data curation, S.R., E.L., A.S. and M.A.; writing--original draft preparation, S.R., E.L. and M.A.; writing--review and editing, S.R. and M.A.; supervision and project administration, M.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# Stretching and Eccentric Exercises Normalize Gait Parameters in Gastrocnemius Tightness Subjects
## 3 Results  
Recruitment ran from 1 January 2016 to 1 January 2019. A total of 20 subjects was enrolled in the study: 10 individuals with asymptomatic bilateral IGT and 10 controls. Each lower limb in both groups was individually analyzed.  
The two groups were comparable in age and weight but not gender (\(p\) = 0.02) (Table 1). The Silfverskiold test confirmed that the study group had IGT and the control group did not (Table 1).  
Prior to the Stanish protocol, the IGT group showed greater knee flexion compared with the control group during the midstance phase (respectively, 8.9 +/ - 4.6 vs. 3.4 +/ - 2.3, \(p\) < 0.001). There was no difference in maximum ankle dorsiflexion, maximum forefoot pressure, or rearfoot contact time between the groups at this time (Table 2).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Control Group (n = 20)**} & \multicolumn{2}{c}{**IGT Group (n = 20)**} \\  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **\(p\)-Value** \\  & **(95\% CI)** & **(95\% CI)** & **(95\% CI)** & \\ \hline Age & 37.8 \(\pm\) 14.5 & 42.2 \(\pm\) 15.8 & \multirow{3}{*}{0.16} \\  & (31–44.6) & (34.8–49.7) & & \\ \hline Weight & 65.7 \(\pm\) 13.4 & 65.3 \(\pm\) 10.6 & \multirow{3}{*}{0.76} \\  & (59.5–71.9) & (60.3–70.3) & & \\ \hline Gender & Male & 8 (40\%) & 16 (80\%) & \multirow{3}{*}{0.02} \\  & Female & 12 (60\%) & 4 (20\%) & \\ \hline Ankle dorsiflexion with the knee in extension (in degrees) & 2.2 \(\pm\) 2.6 & \(-6.5\pm 7.1\) & \multirow{3}{*}{\(<\)0.001} \\  & (0.9–3.4) & (\(-9.8\)Regarding the range of motion analysis, the Stanish protocol did not change the maximum ankle dorsiflexion in the IGT group (13.5 +/ - 4.1 to 13.8 +/ - 4 degrees, \(p\) = 0.75, d = 0.07) (Figure 1), whereas it did change the corresponding knee flexion angle (8.9 +/ - 4.6 to 3.7 +/ - 2.3 degrees, \(p\) < 0.001, d = 1.43) (Figure 2). No correlation was found between the improvement in knee flexion induced by the Stanish protocol and the age (\(\rho\) = \(-\)0.17; \(r2\) = 0.033; \(p\) = 0.484). Comparing the IGT knee flexion after Stanish with that of the control group showed no significant difference (3.7 +/ - 2.3 degrees in IGT vs. 3.4 +/ - 2.3 degrees in controls, \(p\) = 0.72, \(d\) = 0.13) (Figure 3).  
Figure 3: Knee flexion comparison between IGT after Stanish protocol (n = 20) and controls (n = 20).  
Figure 2: Corresponding knee flexion before and after a Stanish protocol in the IGT group (n = 20).
## 2 Materials  
In this section, we summarize the main characteristics of SAR data used for our experiments and acquired by the ENVISAT ASAR sensor (which has been primarily exploited for marine applications), as well as by the newly launched ICEYE constellation. The interest in the proposed comparison analysis resides in the growing availability of a wide range of spaceborne-based SAR missions operating at different microwave frequencies [2]. Figure 1 shows a diagram that pictorially describes SAR systems' development over time, grouped for operative bands (e.g., X-band, C-band, and L-band). The following subsections describe the main characteristics and the acquisition modes of the specific SAR data exploited for the experiments presented in Section 4.  
Figure 1: Spaceborne SAR missions over time.
## 2 Structural Optimization Method with Multiphysical Field Coupling
### Theoretical Basis of Optimization Method
When the conduction heat transfer coefficient \(h\) is constant, a larger mechanical contact area \(A_{c}\) and the welding metallurgical bonding area \(A_{w}\) enhance heat dissipation efficiency. If the welding area is fixed, increasing the contact area can effectively lower the temperature change rate. (4) Thermodynamics during current flow  
The rate of heat accumulation in the conductor structures follows the principle of energy conservation:  
\[ \begin{array}{l}\frac{dQ}{dt}=\frac{dQ_{+}}{dt}-\frac{dQ_{-}}{dt}=I(t)^{2}R( T)-h(A_{c}+A_{w})(T(t)-T_{w}(t))\end{array}  \tag{6}\]  
By combining Equations (1) and (6), the rate of temperature change on the conductor structures can be expressed as  
\[ \begin{array}{l}\frac{dT}{dt}=\frac{\rho_{+}(T)}{C_{p}(T)\rho}\cdot\frac{I(t )^{2}}{S^{2}}-\frac{h(A_{c}+A_{w})(T(t)-T_{w}(t))}{C_{p}(T)m}\end{array}  \tag{7}\]  
In summary, Equations (5) and (8) indicate that under constant current \(I\) and fixed welding area \(A_{w}\), the current-carrying cross-sectional area \(S\) of the structure and the contact area \(A_{c}\) between the structures are the key factors influencing the temperature evolution of conductor structures. (5) Evaluation methodology  
As illustrated in Figure 1, the proposed evaluation methodology aims to enhance the current-carrying capacity of lithium-ion batteries and achieve an optimized design for highly efficient conductor structures. This is accomplished through a systematic approach that integrates theoretical derivation, multiphysics coupling finite element analysis (FEA) [42; 43; 44; 45], and experimental validation, all while adhering to high-amperage constraints. First, theoretical models for heat generation, dissipation, and transfer within conductor structures are established. These models derive the functional relationships between the cross-sectional area of structural components, the contact area, and the resulting temperature variations. Next, multiphysics coupling simulations are conducted by increasing the thickness of structural components and incorporating reinforced welded structures to enhance structural parameters. This allows for a comprehensive analysis of key factors influencing temperature distribution and thermal behavior within the conductor structures. Finally, experimental validation is performed to verify the impact of these critical parameters on temperature variation. The experimental results help determine the optimal configuration and material modifications needed to maximize current-carrying efficiency. Through this integrated methodology, the most effective design pathways for optimizing conductor structures are identified.
# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 1 Introduction
### Ship Detection Using Feature Engineering  
In the process of image processing, feature extraction and target detection are often integrated in the same algorithm. In addition, in the early stages of development, most of object detection methods are implemented by utilizing visual features (such as color, texture, local binary pattern, spatial relationships, etc.). In [15], a semisupervised model is presented to distinguish between ships and nonships. Besides common shape and texture features, this article also adds local multiple patterns to enhance feature representation. Reference [16] proposes a new detection method. It analyzes whether the sea surface is uniform through two features, and uses a new linear function to select candidate regions. Based on this, it can ignore non-candidate regions to reduce calculation time and achieve fast detection.  
In [17], a robust algorithm is proposed. It combined a visual attention model with a local binary pattern (CVLBP), and was analyzed in complementary ways. This method can reduce the sensitivity of clouds and illumination, as well as having a better detection effect. Furthermore, in order to increase the separability between ships and background, reference [18] proposed to rearrange the spatially adjacent pixels in the vicinity of ships intoa vector, so that it can be endowed with some contextual information. At the same time, histograms of oriented gradients (HOGs) are used to validate real ships out of a selection of candidates. In addition, aiming to deal with complex scenes, a contour refinement and the improved generalized Hough transform (GHT)-based ship detection scheme are proposed in [19]. It can achieve a more accurate ship detection by repairing damaged bow contours and removing any erroneous candidates. However, these methods often rely upon one or more handcraft features, and they cannot find deep semantic information, so it is difficult to achieve better detection results.  
In addition, since ships are often conspicuous in the background of the ocean, their salient characteristics have provided us with another research idea. We can detect the regions that represent the scene as auxiliary features for finding a ship. For example, Ref. [20] propose a spectral residual (SR) approach for visual saliency detection. It converts the input image to the spectral domain, and finds the spectral residual to obtain a saliency map. In [21], a context-aware saliency detector (CASD) was designed. The main design principle is that the context of the dominant objects is just as essential as the objects themselves. They are simple to implement, fast in processing speed, and can show good performance. However, for some complex ocean scenes the acquired saliency map may contain scattered clouds or ocean waves, so it can only be used as a reference.