esa-sceva/satcom-chunk-collection huggingface dataset:
# Rotation Invariant Graph Neural Network for 3D Point Clouds
## 6 Results
### Classification  
We compared the original RGCNN network with our own extensions, which achieve robustness to rotation noise. The methods can be seen in Figure 12. The tests were conducted on the Modelnet40 dataset. We selected 512 as the number of points for the input point clouds of the networks. We used only the point coordinates in our experiments. At this number it has comparable performance with the networks trained on 1024 and 2048 points while taking half the time of the 1024 point network. The original RGCNN network is shown in Figure 12a and uses \(N=512\) points as input and outputs the selection vector for the class. The RGCNN with an oriented bounding box, illustrated in Figure 12b, contains the preprocessing part, which places a point cloud in the canonical rotation. The preprocessing can also store the calculated bounding box width, length, and height. RGCNN with Gram matrix preprocessing is shown in Figure 12c. Pre-processing converts the input point cloud into a set of characteristics of dimension \(N\times N\). This set of features is passed to the RGCNN network, thus needing a change in the first spectral convolution module. For the original RGCNN module, the input feature size for the first spectral convolution is \(f_{in}=3\) and the output feature dimension is \(f_{out}=128\). For the Gram matrix preprocessing, the input size will be \(f_{in}=N\) and \(f_{out}=128\). For the multi-view networks shown in Figure 12d,e, the original point cloud is placed in the canonical rotations using the oriented bounding box in Figure 12d and eigenvector decomposition in Figure 12e. Since there is an ambiguity in the selection of the eigenvectors for the second and third dimensions, four possible canonical rotations are considered as detailed in [8]. This means that the input point cloud is placed in a canonical rotation, the distance graph is computed, and the point cloud in the canonical rotation is rotated three times. Thus, the pre-processing and extra rotations will convert the input point cloud of dimension into a batch of four point clouds. The net effect is similar to a four-fold increase in the batch size, but it reduces the effect of the sign ambiguity in the eigenvector decomposition.  
Figure 12: RGCNN versions tested for classification.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Spatiotemporal Feature Enhancement Based on Attention
#### 3.3.1 Spatiotemporal Feature Enhancement Based on Channel Attention  
Channel attention plays a crucial role in lip-reading systems by modulating the influence of channel features on performance. As different channels possess varying degrees of discriminative power, their impact on recognition accuracy can be substantial. By applying channel attention to assign dynamic weights to each channel, the model can prioritize the most relevant channels while suppressing irrelevant or redundant ones, thereby improving overall performance. SE blocks are frequently employed in lip-reading to draw out the rich tapestry of channel attention features.  
Based on the SE module, [PERSON] et al. [105] designed a novel method called SE-ResNet by imposing Squeeze-and-Excitation (SE) blocks on ResNet in the front-end architecture aimed at harnessing attention mechanisms to capture channel dependencies. This  
Figure 10: The typical spatiotemporal feature enhancement methods based on attention. (**a**) The architecture of Ref. [98]. (**b**) The architecture of Ref. [99]. (**c**) The architecture of Ref. [100]. (**d**) The architecture of Ref. [101]. (**e**) The architecture of Ref. [102]. (**f**) The architecture of Ref. [103]. (**g**) The architecture of Ref. [104].  
method enriches the spatiotemporal features of video frames by assigning greater weights to important areas within the input, allowing the model to concentrate on the most informative channels and effectively tackle the visual ambiguity present in phoneme recognition.  
Differently from SE-ResNet, [PERSON] et al. [98] harnessed the power of SE-ResNet18 as the front-end network to enrich spatiotemporal features in speech recognition. It is significant that this approach adeptly reduced noise interference, achieving superior performance on both the LRW dataset and a new dataset with sentence-level Mandarin recognition. These findings underscore the significance of channel attention in improving recognition accuracy, particularly in challenging noisy environments.
## 3 Methodology
### Compositing Method
#### 3.2.2 Image Composition Algorithm  
In this paper, the constrained view angle-maximum value composite algorithm (CV-MVC) inherited from MODIS Vegetation Index (VI) product was used for compositing of H1A/B products. It was chosen for two reasons. Firstly, one of the purposes of this study is to exam the capability of the composites for providing spatio-temporal details for the high heterogeneous land surface on Zoige  
Figure 3: The tile coordinate scheme of China (**left**) and the observation frequency of HJ1-A/B for the study area in the year 2010 (**right**). The tiles are generated in this paper from the upper left coordinate of China in 150 km \(\times\) 150 km and are defined in the Albers Equal Area projection. The color gradient represents the observation frequency of all the HJ-1A/B imagery from paths 14–19, row 76.  
Plateau. The CV-MVC method is helpful for the obtaining of the optimum vegetation observation and eliminating the terrain shadow and cloud shadow to some extent [15]. On the other hand, since the swath width of HJ-1A/B images are large and the CCD camera is side viewing [26], the CV-MVC algorithm is also helpful for minimizing the view angle effects and, consequently, BRDF related issues for compositing of HJ-1A/B products to some extent [42].  
The cloud mask generated in Section 3.1 was firstly used to exclude cloud contaminated pixels. Then, all the cloud-free HJ-1A/B observations within the time window were counted. If more than two cloud-free observations existed, observations closest to nadir view between the two highest NDVI pixels will then be selected to represent the pixel in the composite cycle. If only one cloud-free observation was in the time series, the cloud-free observation should be directly taken as the output. If all the observations are cloudy, the pixel with the largest NDVI will be selected as the output, and the pixel will still be marked as cloud in the cloud mask layer.  
Three temporal intervals, which are 8-day, 16-day and one month were set to generate the HJ-1 composites. Ideally, there will be 4 and 8 observations within an 8-day and 16-day composition period, respectively. However, the number of observations is not enough to conduct the reliable BRDF correction using to a semi-physical BRDF model such as Walthall model [43]. Therefore, the BRDF effects are not corrected in the current method (to be discussed in detail in Section 5).
###### Abstract  
In order to enhance the stability of vehicle pose estimation within driving videos, a novel methodology for optimizing vehicle structural parameters is introduced. This approach hinges on evaluating the reliability of edge point sequences. Firstly, a multi\(-\)task and iterative convolutional neural network (MI\(-\)CNN) is constructed, enabling the simultaneous execution of four critical tasks: vehicle detection, yaw angle prediction, edge point location, and visibility assessment. Secondly, an imperative aspect of the methodology involves establishing a local tracking search area. This region is determined by modeling the limitations of vehicle displacement between successive frames. Vehicles are matched using a maximization approach that leverages point similarity. Finally, a reliable edge point sequence plays a pivotal role in resolving structural parameters robustly. The Gaussian mixture distribution of vehicle distance change ratios, derived from two measurement models, is employed to ascertain the reliability of the edge point sequence. The experimental results showed that the mean Average Precision (mAP) achieved by the MI\(-\)CNN network stands at 89.9%. A noteworthy observation is that the proportion of estimated parameters whose errors fall below the threshold of 0.8 m consistently surpasses the 85% mark. When the error threshold is set at less than 0.12 m, the proportion of estimated parameters meeting this criterion consistently exceeds 90%. Therefore, the proposed method has better application status and estimation precision.  
vehicle pose estimation; edge point sequences; structural parameter optimization; multi\(-\)task and iterative; convolutional neural network +
Footnote †: journal: Applied Sciences
## 2 The Cohesive Force Fatigue Damage Constitutive Model
### Implementation Process  
The UMAT subroutine is a FORTRAN program interface provided by the Abaqus computational software (2020) to allow users to customize material properties. Using the UMAT subroutine, users can define material constitutive relationships according to their requirements, thus enabling the computation of user-defined materials. Based on the cohesive force element damage constitutive model in Section 2.1, a UMAT subroutine is  
Figure 4: State of cohesive force unit under cyclic load.  
developed. Subsequently, by importing the developed UMAT subroutine into the Abaqus software, the cohesive force fatigue damage element model is completed. The compilation process of the UMAT subroutine for the cohesive force fatigue damage element model is illustrated in Figure 5.  
Step 1: Read in material parameters, which include the initial cohesive force normal strength \(\sigma_{max,0}\) under monotonic loading conditions; the separation corresponding to the initial cohesive force normal strength \(\delta_{0}\) and the fatigue limit \(C_{f}\).  
Step 2: Define the state variables needed, including: damage \(D\); current maximum normal separation \(\Delta u_{n,max}\); separation at the last iteration step \(\Delta u_{t\cdot\Delta t}\); current separation \(\Delta u\); and accumulated separation \(\delta_{M}\).  
Step 3: Update the separation matrix.  
Step 4: Compare the sizes of \(\Delta u\) and \(\Delta u_{n,max}\) to determine the corresponding state of the element at this time. If \(\Delta u\) is greater than \(\Delta u_{n,max}\), the cohesive force element is in the loading state. If \(\Delta u\) is less than 0 and \(D\) equals 1, the cohesive force element is in the contact state. If \(\Delta u\) is less than 0 and \(D\) is less than 1, the cohesive force element is in the compression state. In all other cases, the cohesive force element is in the unloading state.  
Step 5: Update the state variables defined in step 2, completing this computation and starting the next iterative computation.  
Step 6: Stop the iterative computation when the set number of iterations is reached or when a certain state variable reaches a preset value.
## 3 Determination of Commonality Indexes Weights and Calculation of Comprehensive Score  
### Standardization of Evaluation Indexes  
The dimensionlessness in the evaluation index system is the prerequisite for the integration of indexes. If the nondimensionalized value of the index is called the index evaluation value, then the dimensionlessness process is the process of converting the actual value of the index into the evaluation value of the index, and the dimensionlessness method is to eliminate the influence of the primitive variable (index) dimension by the mathematical transformation. When the indexes are nondimensionalized, it is necessary to note that the positive and negative indexes have different effects on the overall target. For example, indexes such as economic benefits, policy support, and response capacity are positive indexes. The higher the index value, the better; the indexes such as power outage time, number of failures, and response cost are negative indexes, the lower the index value, the better.  
In summary, the threshold method in the linear nondimensionalization method is used to nondimensionalize the index; threshold method is a dimensionless method to get the index evaluation value through the ratio of the actual value to the threshold of the index. The corresponding formulas are as follows.  
Assume that there are \(m\) regions, \(n\) evaluation indexes, and \(x_{ij}\) represents the index value of the \(i\)th region under the \(j\)th index [23].  
Positive indexes  
\[x^{\prime}_{ij}=\frac{x_{ij}-\min\big{\{}x_{1j},\cdots,x_{mj}\big{\}}}{\max \big{\{}x_{1j},\cdots,x_{mj}\big{\}}-\min\big{\{}x_{1j},\cdots,x_{mj}\big{\}}} \tag{2}\]  
Negative indexes  
\[x^{\prime}_{ij}=\frac{\max\big{\{}x_{1j},\cdots,x_{mj}\big{\}}-x_{ij}}{\max \big{\{}x_{1j},\cdots,x_{mj}\big{\}}-\min\big{\{}x_{1j},\cdots,x_{mj}\big{\}}} \tag{3}\]
## 3 Results and Discussion
### Separation Performance  
To evaluate the efficiency of the recycled HIPS membranes in inorganic and organic separation process, three different solutes were used: magnesium sulphate (MgSO\({}_{4}\), a univalent salt), sodium sulphate (Na\({}_{2}\)SO\({}_{4}\), a divalent salt), and Reactive Black 5 dye. Figure 5 summarizes the separation performances of R-HIPS and sulfonated R-HIPS membranes, highlighting their effectiveness in filtering these selected solutes. The dye rejection in R-HIPS membranes correlated with a morphology of decreasing pore size, increasing hydrophilicity, and an enhanced negative charge in sulfonated membranes (see Figure 5). R-HIPS membrane exhibited the lowest rejection of Black 5 dye at 5.5%, which can be attributed to the micro-sized pores present on its surface. In contrast, R-HIPS-3 (57% dye rejection) and R-HIPS-5 membranes, which had the smallest pore size (3.3 nm), almost completely blocked the dye, achieving a 94% dye rejection. Due to the fact that R-HIPS sulfonated membranes and the dye have similar charges, the electrostatic repulsion between them can prevent the dye from permeating through the membrane [39]. Additionally, this repulsion helps to minimize the dye adsorption onto the membrane surface, which can contribute to reducing membrane fouling.  
Regarding salts rejection, the R-HIPS membranes showed a tendency where R-HIPS-5 exhibited the highest rejection, followed by R-HIPS-3 and R-HIPS. This order can be attributed to pore size and surface properties. The smaller pore size of the R-HIPS-5 membrane contributed to its enhanced ability to reject divalent and monovalent salts, as smaller pores can physically block larger ions from passing through them. Additionally, the higher negative charge from the sulfonic acid groups enhanced electrostatic repulsion, particularly against positively charged ions, improving rejection efficiency [35].  
Moreover, more hydrophilic membranes tend to have better antifouling properties, and they can interact more effectively with water, facilitating selective ion transport while reducing the passage of salts. Thus, the combination of smaller pore size and increased negative charge is crucial for the superior performance of R-HIPS-5 in rejecting salts.
# Evolution of the Seismic Forecast System Implemented for the Vrancea Area (Romania)
## 2 Explanation of Relevant Data Analysis Methods  
This article builds upon the analysis methods described by the same authors in ([PERSON] et al., 2014; [PERSON] et al., 2014; [PERSON] et al., 2014), incorporating data gathered over one year of monitoring. As outlined in ([PERSON] et al., 2014), \"The logical tree of the forecast parameters of the Vrancea area\", the decision matrix and comparative results of the anomaly detection methods used are presented in this section. The method applied in our Operational Earthquake Forecasting (OEF) system involves five key steps, as detailed in ([PERSON] et al., 2014).  
In the first step, the seismic zone is selected based on geological data and the location of installed equipment. This is a crucial phase because gas emissions are typically concentrated in fault zones. All monitoring stations are placed in these areas, although some sensors have been relocated occasionally due to interference from local activities.  
In the second phase, data on the daily and seasonal evolution of forecast parameters are collected. During this step, trigger levels and interactions between parameters are defined. It is optimal to use long-term data, particularly given that annual variations are increasingly influenced by global warming. The NIEP seismic network includes over 150 stations, providing robust seismicity data. Additionally, all stations are equipped with meteorological instruments to assess the influence of weather conditions.  
Phase three involves offline data analysis to determine the optimal detection parameters, thresholds, and methods--typically using the STA/LTA algorithm or standard deviation-based techniques. The outcomes depend on sensor type, sampling rate, and local environmental conditions.  
In the fourth phase, the events recorded by each piece of equipment are processed through a decision matrix that employs a logically weighted voting system.  
In the final phase, it is important to remember that these methods are empirical. Therefore, decision-making often involves accountability, especially when specialized intervention departments are engaged. A false alarm can cause panic and undermine the credibility of the entire monitoring system. The main conclusion is that only a multidisciplinary network enables the comprehensive analysis required to minimize false alarms and increase the likelihood of accurate decision-making.
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 2 Methodology
### Design of Cutting Space Under Kinematic Constraints
#### 2.2.4 Generation of Trajectory Space Under Section Constraints  
In the absence of cutting section constraints, the cutting arm moves within the range of the joint space, forming the motion space at the current pose. This motion space intersects with the target cutting section, resulting in the desired cutting space.  
Let the motion space of the cutting head vertex under the current roadheader pose be denoted as \(P_{i}(1,2,3\cdots n)\), with the corresponding joint space \(\theta_{i}(1,2,3\cdots n)\). The cross-section model is given in Equation (8). Solving the motion space involves finding the mapping relationship between \(P_{i}(1,2,3\cdots n)\) and \(\theta_{i}(1,2,3\cdots n)\). For any point \((x\_left_{j},\)\(y\_left_{j},z\_left_{j})\) within the coordinate set \(P_{solution}\), it must satisfy \((j=1,2,\cdots,j<n)\).  
\[\left\{\begin{array}{l}x\_left_{j}\leq R_{h}+C_{h}\\ y\_left_{j}\geq-0.5*W_{h},and,y\_left_{j}\leq 0\\ z\_left_{j}\geq 0,and,y\_left_{j}\leq 0.5*H_{h}\end{array}\right.\right.\end{array}\right. \tag{8}\]  
Similarly, the constraint conditions for the right-side cross-section can be determined based on the current pose of the roadheader and the shape and position of the right-side cross-section.  
\[\left\{\begin{array}{l}x\_right_{j}\leq R_{h}+C_{h}\\ y\_right_{j}\geq 0,and,y\_right_{j}\leq 0.5*W_{h}\\ z\_right_{j}\geq 0,and,z\_right_{j}\leq H_{h}\end{array} \tag{9}\]  
Based on the spatial range of each joint of the cutting arm, the forward kinematics model is utilized to set joint space increments \((\Delta\theta_{1},\Delta\theta_{2},\Delta d)\). Starting from the initial state, spatial point sets are screened according to cross-section constraint conditions to obtain feasible cutting space under the current roadheader pose. During this process, the size constraint of the cutting head must be considered, with calculations performed using the spatial coordinates of the cutting head's outer envelope surface.  
Figure 5 illustrates the design process of the cutting trajectory space under target cross-section constraints. Given the pose of the roadheader body, the inherent motion space is calculated based on the motion range of the cutting arm joints, and then cutting trajectory points satisfying the constraints are screened in combination with target cross-section constraints. Adding the outer envelope surface of the cutting head forms an effective cutting space. However, the obtained cutting trajectory points are disordered and cannot directly guide the movement of the roadheader, requiring further optimization.  
The cutting space is derived from joint space parameters, encompassing spatial coordinates of the end effector and its envelope surface. The trajectory points represent the spatial positions of the robot's joints, allowing for direct control without the need for frequent recalculations of inverse kinematics during operation, thus improving both control efficiency and accuracy.
## 2 Infrastructure, Instrumentation and Calibration Methods
### Calibration and Validation Methods
#### 2.2.1 Sea-Surface Cal/Val  
Satellite altimeters are designed to measure the sea (or water) surface height (SSH) above a reference ellipsoid. The sea-surface calibration method compares the sea surface height, as measured by a satellite  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Cal/Val Method** & **Mission/Pass** & & **Instrumentation** \\ \hline \multirow{4}{*}{CDN1} & \multirow{4}{*}{Transponder} & S3A P14 & \(\bullet\) & Ku-band range transponder \\  & & S3B P335 & \(\bullet\) & Microwave radiometer \\  & & JA3 P18 & \(\bullet\) & Two GNSS * receivers \\  & & CryoSat-2 & \(\bullet\) & Two meteo stations \\ \hline \multirow{4}{*}{GVD8} & \multirow{4}{*}{Sea-Surface} & JA3 P18 & \(\bullet\) & Five tide gauges (radar, \\  & & JA3 P109 & & acoustic, pressure) \\  & & S3A P335 & \(\bullet\) & Two GNSS receivers \\  & & S3A P14 & \(\bullet\) & Two meteo stations \\ \hline \multirow{4}{*}{RDK1} & \multirow{4}{*}{Sea-Surface} & JA3 P109 & \(\bullet\) & Two tide gauges (radar) \\  & & S3B P71 & \(\bullet\) & One GNSS receiver \\ \hline \multirow{4}{*}{CRS1} & \multirow{4}{*}{Sea-Surface} & S3A P278 & \(\bullet\) & Two tide gauges (radar, pressure) \\  & & S3B P14 & \(\bullet\) & One GNSS receiver \\  & & HY-2B P280 & \(\bullet\) & One meteo station \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing (PFAC) Cal/Val sites along with the main instrumentation at each Cal/Val site.  
altimeter at a certain location and time in open water, to sea surface heights measured by reference ground measurements. A number of corrections need to be applied to the measured satellite range to account for propagation errors caused by the atmosphere (ionosphere, wet and dry troposphere), the sea state bias due to surface roughness impacting the radar measurement, and geophysical corrections including solid earth tide, ocean tides, etc. Ideally, the bias of an altimeter-derived sea-surface height is determined as the difference between the satellite and the ground-truth observations implemented at the same location and time.  
Ground-truth observations are provided by a combination of water level sensors, absolute positioning receivers, meteorological and oceanographic sensors, and models of the geoid, tides, mean dynamic topography, etc. At the PFAC, the land mass of Gavdos and Crete contaminates the altimeter's signal, leading to invalid sea-surface measurements close to the coast (about 7.5 km). To overcome this, the Cal/Val site observations have to be transferred to the open sea at about 20 km, with about 3000 m depth, far from the coast where valid satellite measurements exist. This introduces an uncertainty in the approach compared to the ideal case where truly contemporaneous and co-located measurements are available.