esa-sceva/satcom-chunk-collection huggingface dataset:
# MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications
## 3 Proposed Method
### SecondFPN  
The SecondFPN network employed a carefully designed feature fusion strategy to integrate high-level semantic information from deeper layers with high-resolution features from shallower layers, enhancing semantic richness, as shown in Figure 2.  
The process was as follows:  
Feature Extraction: Input feature maps \(I\) were processed through deep convolutional layers \(\mathcal{C}^{(k_{d}s_{d}s_{d}P_{d})}\) to extract features, where \(k_{d}\), \(s_{d}\), and \(p_{d}\) denote the kernel size, stride, and padding, respectively.  
\[F^{(1)}=\mathcal{C}^{(k_{d}s_{d}P_{d})}(I) \tag{1}\]  
Dimensionality Reduction: To optimize channel numbers and reduce computational parameters, \(1\times 1\) convolutions \(\mathcal{C}^{(k_{1}s_{1},p_{1})}\) were applied to the second and third feature maps \(F^{(2)}\) and \(F^{(3)}\), where \(k_{1}=1\), \(s_{1}=1\), and \(p_{1}=0\).  
\[F^{(2)} =\mathcal{C}^{(k_{1},s_{1},p_{1})}\Big{(}F^{(1)}\Big{)} \tag{2}\] \[F^{(3)} =\mathcal{C}^{(k_{1},s_{1},p_{1})}\Big{(}F^{(2)}\Big{)} \tag{3}\]  
Upsampling and Fusion: The lower-resolution, semantically rich fourth feature map \(F^{(4)}\) was upsampled using \(\mathcal{U}^{(k_{a})}\) to match the resolution of the lower layer feature  
Figure 2: SecondFPN structure.  
map \(\mathrm{F}^{(2)}\), where \(\mathrm{k_{u}}\) is the upsampling kernel size. Feature fusion was performed by element-wise addition.  
\[\mathrm{F}^{(5)}=\mathrm{F}^{(2)}+\mathcal{U}^{(\mathrm{k_{u}})}\Big{(}\mathrm{F }^{(4)}\Big{)} \tag{4}\]  
Prediction: The fused feature map \(\mathrm{F}^{(5)}\) underwent a convolutional prediction layer \(\mathcal{P}^{(\mathrm{k_{p}},\mathrm{s_{p}},\mathrm{P_{p}})}\) for object classification and localization, where \(\mathrm{k_{p}}\), \(\mathrm{s_{p}}\), and \(\mathrm{p_{p}}\) are the kernel size, stride, and padding, respectively.  
\[\mathrm{P}=\mathcal{P}\Big{(}\mathrm{k_{p}},\mathrm{s_{p}},\mathrm{P_{p}} \Big{)}\Big{(}\mathrm{F}^{(5)}\Big{)}=\mathrm{Conv}\Big{(}\mathrm{F}^{(5)}, \ \mathrm{k_{p}},\mathrm{s_{p}},\mathrm{P_{p}}\Big{)} \tag{5}\]  
The SecondFPN fusion strategy enhanced feature representation and increased the network's sensitivity to multi-scale objects, demonstrating exceptional performance in complex autonomous driving and robotic navigation scenarios.
## 1 Introduction
used modified Hill spectra to simulate turbulent phase screens and proposed an OAM mode CNN-OAM mode classifier for detecting high-order and zero-order distorted vortex beams after atmospheric turbulence. However, the model's generalization is relatively poor [20]. In the same year, [PERSON] et al. proposed an improved MobileNetV2 model with fewer parameters and higher accuracy for detecting distortion levels caused by turbulence in FSO communication links. The improved method achieved an average accuracy of 89.19% [21]. The performance of deep learning models often relies on a large amount of annotated data, especially in the case of limited training data, which often limits the model's generalization ability and accuracy. With the continuous increase in data volume, it brings certain difficulties to data collection and training. Transfer learning, as an effective method to solve the problem of data scarcity, has been widely applied in various tasks, especially in situations where data are limited. By utilizing pre-trained models on large datasets, transfer learning can transfer existing knowledge to new tasks, thereby improving the performance of the model on small datasets. In 2020, [PERSON] et al. used a pre-trained VGG model on ImageNet and transferred its parameters to their own network, successfully completing the recognition task of 8 diseases with only 500 rice images and 466 corn images [22]. In 2024, [PERSON] et al. applied transfer learning to brain tumor detection tasks and combined it with the ResNet152 model, achieving an accuracy of 98.5% with only 7023 annotated data [23]. Through transfer learning, they avoided the difficulty of training models from scratch and fully utilized the features learned from pre-trained models, thus achieving high recognition accuracy even with limited data. This result demonstrates the effectiveness and advantages of transfer learning in dealing with small sample learning tasks. The main problem with existing methods is that they require a large dataset for training and have low model detection accuracy. This paper introduces the transfer learning method and constructs an orbital angular momentum modal classifier based on transfer learning (Transfer-OAM). It systematically studies the impact of the amount of data, turbulence strengths, and transmission distance on the detection accuracy of distorted vortex beams. Meanwhile, it also analyzes the generalization ability performance of the models obtained under the conditions of single training sets and mixed training sets. The Transfer-OAM model classifier proposed in this article can achieve high-precision detection of 15 OAM states, accumulating a new method for the detection of vortex beams and helping to realize future high-capacity optical communication technologies based on OAM.
###### Abstract  
Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), with millimeter-level accuracy and full-resolution capabilities, is essential for monitoring urban deformation. With the advancement of SAR sensors in spatial and temporal resolution and the expansion of wide-swath observation capabilities, the number of permanent scatterers (PSs) in high-density urban areas has surged exponentially. To address these computational and memory challenges in high-density urban PSInSAR processing, this paper proposes an efficient method for integrating regular grid partitioning and connected component constraints. First, adaptive dynamic regular grid partitioning was employed to divide monitoring areas into sub-blocks, balancing memory usage and computational efficiency. Second, a weighted least squares adjustment model using common PS points in overlapping regions eliminated systematic inter-sub-block biases, ensuring global consistency. A graph-based connected component constraint mechanism was introduced to resolve multi-component segmentation issues within sub-blocks to preserve discontinuous PS information. Experiments on TerraSAR-X data covering Fuzhou, China (590 km\({}^{2}\)), demonstrated that the method processed \(1.4\times 10^{7}\) PS points under 32 GB memory constraints, where it achieved a 25-fold efficiency improvement over traditional global PSInSAR. The deformation rates and elevation residuals exhibited high consistency with conventional methods (correlation coefficient \(\geq 0.98\)). This method effectively addresses the issues of memory overflow, connectivity loss between sub-blocks, and cumulative merging errors in large-scale PS networks. It provides an efficient solution for wide-area millimeter-scale deformation monitoring in high-density urban areas, supporting applications such as geohazard early warning and urban infrastructure safety assessment.  
P 2025 17 1518
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 4 Algorithm Design
### A Location Privacy-Preservation Method Based on Dummy Locations under Road Restriction  
The specific procedure of a location privacy-preservation method based on dummy location under road restriction can be follows:  
1. Based on the historical data of service requests, the LBS server counts the number of service requests initiated by vehicle users in each cell, and the service request probability of \(cell_{i,j}\), \(i=1,2,\dots,I,j=1,2,\dots,J\), \(q_{i,j}=f_{i,j}/F\), where \(f_{i,j}\) is the number of service requests initiated by vehicle users in \(cell_{i,j}\), and \(F\) is the number of service requests in the area. The service semantics of service \(u\) is \(q_{i,j}=f_{(i,j),u}/f_{i,j}\), where \(f_{(i,j),u}\) is the number of requests of service \(u\) initiated by vehicle users in \(cell_{i,j}\), \(u=1,2\),..., \(U\).
2. The LBS server constructs and distributes the information matrix \(Q(\mathbf{r},\mathbf{q},\mathbf{e})\) within the RSU's jurisdiction to each RSU.
3. RSU broadcasts \(Q(\mathbf{r},\mathbf{q},\mathbf{e})\) and \(\mathbf{R}\) to users in its covered area.
4. According to the privacy preservation level \(V\), the vehicle user calculates its privacy parameter \(k\) by (5).
5. The vehicle user generates \(k-1\) dummy locations using dummy location selection algorithm under road restriction. The details are as follows: 1. Let \(k^{\prime}=2k\). Within the locations in \(\mathbf{R}\), other \(k^{\prime}-1\) locations apart from the vehicle user's location are selected as dummy locations by solving the problem formulated in (7). Hence, a candidate set \(\mathcal{G}^{\prime}\) is constructed with the vehicle user's location and \(k^{\prime}-1\) selected dummy locations. 2. Within set \(\mathcal{G}^{\prime}\), other \(k-1\) locations apart from the vehicle user's location are selected as dummy locations by solving the problem formulated in (8). Hence, set \(\mathcal{G}^{\prime\prime}\) is constructed with the vehicle user's location and \(k-1\) selected dummy locations.
6. The vehicle user generates service query \(Lq^{\prime}\) including locations in \(\mathcal{G}^{\prime}\), their corresponding service contents, and the privacy preservation level, and then, \(Lq^{\prime}\) is sent to the LBS server via RSU.
7. Receiving service query \(Lq^{\prime}\), the LBS server retrieves service results according to \(k\) locations and the corresponding service contents, and then, the LBS server returns service results to the vehicle user through RSU.
8. The vehicle user selects the required result from service results according to its location.
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 3 Materials and Methods  
### Overview of the Study Area  
In this paper, a total of 10 counties (cities) including Wenchuan, Beichuan, Mianzhu, Shifang, Qingchuan, Mao, An, Dujiangyan, Pingwu, and Pengzhou were selected as the study area [40]. The study area is depicted in Figure 1a, with a land size of 26,177 km\({}^{2}\).  
The DEM used herein are ASTER GDEM v3 data collaboratively developed by METI (Ministry of Economy, Trade and Industry) of Japan and NASA (National Aeronautics and Space Administration) of the United States, and jointly distributed to the public freely [41] (Data webpage: [[https://urs.earthdata.nasa.gov/](https://urs.earthdata.nasa.gov/)]([https://urs.earthdata.nasa.gov/](https://urs.earthdata.nasa.gov/)), accessed on 5 April 2023). After acquiring the data, the DEM of the target study area was obtained by stitching, projecting the coordinate system, specifying the extent of cropping, and filling the pits in ArcGIS 10.7 for the subsequent extraction of the derived topographic factors. Considering that the DEM dataset did not have significant discontinuities or large deviations, the average value stitching approach was chosen. The projection coordinate system was set to WGS_1984_UTM_Zone_47N. Additionally, the vector data of 10 counties (cities) in the hardest-hit areas were obtained from the National Geomatics Center of China (data webpage: [[http://www.ngcc.cn/ngcc/](http://www.ngcc.cn/ngcc/)]([http://www.ngcc.cn/ngcc/](http://www.ngcc.cn/ngcc/)), accessed on 7 April 2023).
## 2 Versatile Video Coding (VVC)
### Entropy Decoder  
The entropy decoding process used in VVC is similar to that used in HEVC. However, the VVC CABAC engine is more advanced than its predecessor. Here, the index of the current state was used to linearly express the probability. Therefore, the pre-computed look-up table of HEVC was removed, and the multi-hypothesis probability update model was applied to improve the accuracy. In addition, the VVC CABAC introduced a QP dependent initialization model where total initialization values have 6-bit precision. In addition, VVC was dependent on the transform block size for the selection of coefficient group sizes. It allowed coefficient groups of 1 \(\times\) 16, 2 \(\times\) 8, 8 \(\times\) 2, 2 \(\times\) 4, 4 \(\times\) 2 and 16 \(\times\) 1.  
Figure 1: General block diagram of a VVC decoder.  
Figure 2: VVC supported partitioning schemes: quad, binary and ternary splitting structures.
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 4 Discussion
### Grassland Degradation Analysis  
Grassland degradation on the TP is the result of a combination of multiple potential factors, which can be classified into environmental factors and anthropogenic factors. The alpine grassland ecosystem of TP has slower energy flow and material cycling rates than other ecosystems, which leads to a less resilient ecosystem [57]. Compared with other grassland types, alpine grasslands are more difficult to recover after being disturbed by external forces [58]. The average annual mean temperature on the TP has increased by 0.4 \({}^{\circ}\)C over the past half century, and annual precipitation has been increasing in most areas since the 1980s, with slight decreases in the southeast [59]. The combined effect of decreased precipitation and increased temperature on the TP has led to a reduction in soil water content, which is detrimental to grass growth on the southeastern of the TP [60]. Overgrazing affects community structures by altering the species composition of the TP, leading to biomass decline and triggering grassland degradation [61]. The activities of rodents are also an important cause of grassland degradation on the TP. In degraded grasslands, bare land provides favorable conditions for rodent digging, and rodent activity further accelerates degradation [62; 63]. In addition, large-scale surface destruction such as mining and the construction of railroads are also important factors in the degradation of grasslands on the TP, which will take decades to recover [64].  
The percentages of degraded grassland in Changdu City, Ganzi Tibetan Autonomous Prefecture, Gannan Tibetan Autonomous Prefecture, Yushu Tibetan Autonomous Prefecture, Aba Tibetan and Qiang Autonomous Prefecture, Rikaze City, Shannan City and Nagqu City were more than 20%, and the area of grassland degradation exceeded 2000 km\({}^{2}\). Among them, other prefecture-level cities except for Changdu City are pastoral areas. With the increase in the number of livestock in pastoral areas, overgrazing has caused serious degradation of pasture on the TP. Irrational grazing behavior on the TP has further intensified grassland degradation. To reduce grassland degradation, the Chinese government privatized grasslands in the 1990s [65]. Changes in the land regime have reduced the mobility of pastures, allowing traditional grazing practices to be undermined by individual grazing [66]. In addition, to improve the living conditions of local people, the government has implemented a series of comprehensive measures, such as the establishment of settlements [67; 68]. However, the grassland near the settlements has been severely degraded by trampling and nibbling [69].
# Enhancing Channelized Feature Interpretability Using Deep Learning Predictive Modeling
## 3 Results  
### Applied Network Architecture  
Elegant Fully Convolutional Network (FCN) has been introduced [24] which requires minimal training data and yet results in precise pixel-based segmentation in bio-medical image processing, with U-shaped architecture consists of contracting path (encoder), middle of the network and expansive (decoder) path. The proposed architecture for the automated predictive modeling is generally similar to the established building blocks, which consist of eight convolutional layers, pooling layers, batch normalization, and dropout layers. Convolutional layers involve a multiplication process of a filter to an input, producing repeated feature maps. Hence, an enormous number of filters can be learnt automatically in parallel. Meanwhile, pooling layers can be applied to lessen dimensions of the feature maps by reducing various parameters that need to be learnt by the network. This layer improves the robustness of the model towards positional variation of the features for the input image. Batch normalization layers are proven to improve deep neural network performance by restoring inputs that may potentially be shifted or stretched while propagating towards stacked hidden layers, hence reducing the possibility of blockages during training. A range of dropout layers were tested for multiple facies classifications to serve as an adaptive training regularizer and to prevent possibilities of overfit during training. The overall U-Net pipeline is divided into three main components:  
1. Contraction path (also known as encoder with four convolutional layers to extract important features from the input image.
2. Bottleneck (which acts as a bridge when propagating input from encoder), and
3. Expansion path (known as decoder).  
The encoder is solely a process involving four convolutional and multiple stacks of pooling layers with 3 \(\times\) 3 convolutional operators and 2 \(\times\) 2 maximum pooling operators. The number of feature maps being produced are twice the number of the input block, so that the algorithm can learn complicated structures efficiently. The symmetrical second path enables accurate localization by using transposed convolutions and eliminates the function of pooling operators. The end results shall produce higher resolution of output images. The coarse outputs are convolved with learnable 3 \(\times\) 3 filters to produce denser feature maps. The output from the last decoder layer is fed into a 1 \(\times\) 1 \(\times\) 1 convolutional layer to produce feature maps corresponding to two labels of channel or non-channel. The last layer is the softmax layer, which produces the probabilities of each label for each pixel in the seismic image.
## 2 Material and Methods  
### Description of Materials and Specimens  
The benchmarking of the synergistic PLI-LT/PA strategy was performed on aircraft-grade composite of different scales: initially on composite coupons of material UD pre-prep IMS-977-2 having layup of \(\left[45/-45/90/0/90/0/90/-45/45\right]\) and \(\left[45/-45/90/0/90/0/90/-45/45\right]_{28}\) and two flat laminates, and subsequently one curved laminate of same material as coupons and \(\left[45/-45/90/0/90/0/90/-45/45\right]\) layup. All of the laminates carried artificially BVID inflicted using a gas gun; the coupons carried BVID of variable dimensions due to the varying of the impact energies in an impact drop tower.  
A stringer stiffened composite panel representative of the aircraft structure with dimensions of 1250 x 865.5 mm\({}^{2}\) was prepared by stacking carbon-fiber prepreg DeltaTech T800S-150-DT120-35 with the symmetric [45/ \(-\)45/90/0/90/0/90/\(-\)45/45] sequence; T-shape stringer stacking followed a [45/ \(-\)45/90/0/90/\(-\)45/45] sequence was also manufactured. The schematic of Figure 1 presents the skin with attached stringer geometry, dimensions, and layup sequence. A copper-mesh layer was integrated in the stacking sequence to account for the lighting-strike protection system typical of commercial and military aircraft. The mesh, DEXMET 2 Cu6-100 FA, an expanded film of copper of a thickness of 0.051 mm and a cavity area of 76%, Long Way Measurement (LWM) of 2.54 mm, was the second ply in the laminate and it resided just below the top GFRP ply, at an approximate depth of 0.18 mm. Autoclave co-curing at 120 \({}^{\circ}\)C and 6 bars of pressure for 90 min., of the skin with attached stringers pursued, as per prepreg manufacturer specifications. Finally, the external surface of the panel was finished using the same aircraft-grade paint as the coupons. Three barely visible impact damages, termed D1, D2, and D3, were inflicted equally in number locations on the panel while using a gas gun; the schematic of Figure 1b shows their exact positions. D1 was inflicted on the skin region between stringers, D2 on a skin region just above a stringer foot bond area, and D3 on a skin region just above a stringer. Neither damages produced observable surface dents on the skin. Figure 1c shows a view of the panel's rear face with stringers. Thermographic and ultrasonic inspection was performed with the IRT and PA modules that were mounted on prototype VR platforms to perform automated operation on this panel.  
Figure 1: Stiffened composite flat panel (**a**) finished external surface clamped for infrared thermography/Phased Array (IRT/PA) inspection and panel rear face (**b**) schematic of barely visible impact damages (BVID) locations on the panel front face (**c**) T-stringer configuration.
# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 2 Data and Proposed Model  
### Data Description  
The ionospheric data used in this paper are from the Center for Orbit Determination of Europe (CODE) TEC grid data, and the data have a temporal resolution of 2 h [26; 27]. Eight regions in China are selected as the research objects, namely Beijing (40\({}^{\circ}\) N, 115\({}^{\circ}\) E), Guangzhou (22.5\({}^{\circ}\) N, 115\({}^{\circ}\) E), Harbin (45\({}^{\circ}\) N, 125\({}^{\circ}\) E),Tibet (30\({}^{\circ}\) N, 90\({}^{\circ}\) E), Yushu (35\({}^{\circ}\) N,90\({}^{\circ}\) E), Ziyang (30\({}^{\circ}\) N, 105\({}^{\circ}\) E), Hangzhou (30\({}^{\circ}\) N, 120\({}^{\circ}\) E), and Heze (35\({}^{\circ}\) N, 115\({}^{\circ}\) E). The time range for selecting experimental data is from 0:00 on 1 January 2002 to 24:00 on 31 December 2011.  
The information on the geographical location of the eight areas selected is shown in Table 1 and Figure 1, and the values of TEC are shown in Figure 2.
## 3 Selection of Topology Optimization Design Domain and Model Establishment
### Establishment of Topology Optimization Design Domain Model  
Since the topology optimization model is relatively simple and has few sharp corners or chamfering, the element size of the topology optimization design domain of the vehicle  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure Size** & **L/mm** \\ \hline Car-body length & 25,000 \\ Fixed distance & 17,800 \\ Car-body width & 3360 \\ Car-body height & 4050 \\ Height from coupler centerline to rail surface & 950 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CRH car body’s basic dimensions.  
Figure 2: Geometric model of car-body structure: (**a**) axonometric drawing of car body; (**b**) cross-section of the car body; (**c**) the view of the car-body frame.  
body is 50 mm with an eight-node hexahedral mesh [26]. The number of grid elements is determined by both the car-body structure parameters, as shown in Table 1, and the recommended mesh sizes in the topology optimization model, and the model has 390,620 meshes and 475,326 nodes after mesh division. Figure 3 shows the finite element model of the car-body structure. The selected material is aluminum 6005-T6, commonly used in the production of car-body structures. Its mechanical properties are as follows: the elastic modulus is 6.9 \(\times\) 10\({}^{4}\) MPa, the density is 2.7 \(\times\) 10\({}^{3}\) kg/m\  
It can be seen from the calculation results that the maximum Von Mises stress in the design domain is 86.8 Mpa, which is far less than the allowable stress of the material and has ample space for optimization, and the design domain space is reasonable.