esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Deep Continuous Aggregation for 3D Vehicle Detection
## 3 Approach
### Feature Deep Continuous Aggregation Network
#### 3.2.2 3D Proposal Generator  
Referring to the RPN structure in Faster R-CNN, feature maps generated from both the image and BEV maps are fused and fed into a 3D proposal generator to regress the difference between the 3D anchors and the ground truth.  
The 3D anchor was represented as an axis-aligned box encoding, where two clustering centers were selected to cluster the anchor size for vehicles in 3712 samples. At the same time, the orientation of each anchor was set to 0 and 90 degrees, i.e., there were four anchors in one location. As shown in Figure 6, the centers of the anchors were sampled at a stride of 0.5 m, eventually generating 89,600 anchors in BEV. The empty target anchors that did not contain LiDAR points were removed using preliminary screening, leaving about 21,000 anchors, as shown in the green box in Figure 6.  
Figure 4: The cross-layer block structure in the encoder.  
Figure 5: The network parameter structure in the encoder.  
As shown in Figure 7, the features of each anchor were cropped and resized to \(3\times 3\) after dimensionality reduction via a \(1\times 1\) convolution. Then, the corresponding features from each view were fused via an element-wise operation. Finally, the fused feature vectors were passed into a light FCN network to output the categories of anchors and the axis-aligned box deviations between the anchors and the ground truth. The first k proposals based on the confidence score sorting were selected, and then the feature maps of the corresponding region from each proposal were cropped and resized to \(7\times 7\), which were finally fed to the second-stage refinement network.  
The feature cropping and extraction operations of anchors and proposals have a great influence on the subsequent detection. RoIPooling [1] is the most commonly used operator; however, when extracting the feature of small-scale vehicles, the integer operation will lose a large amount of object information, and these quantization operations will lead to the extracted feature not matching the real object. RoIAlign [33] avoids integer operations by using floating-point arithmetic to accurately align the original image area and feature area of the object, and uses bilinear interpolation to regenerate the feature area accurately. Therefore, RoIAlign was exploited to generate the equal-sized features to improve the feature extraction accuracy of the anchors and proposals in this paper.
# Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization
## 2 Model of Reference Trajectory Optimization and Related Principles for Automatic Vertical Parking
### Coordinate Transformation Principle for the Parking Plane  
The standard coordinate system of a parking plane takes the far corner point of the garage bottom as the origin, and the garage bottom line as the positive side of the abscissa axis (x-axis). Using the standard coordinate system of a parking plane simplifies the parking calculation. In addition, real-time parking situations enable driver assessment, which enhances the rationality of parking decision making. Based on this, the principal devices of an intelligent automatic parking system, such as the parking feasibility decider, the reference trajectory optimizer, the parking controller, and the parking monitoring upper computer, are more suitable for use with the standard coordinate system of a parking plane. To convert the live-action coordinate system from the parking imaging system into the standard system of a parking plane, there are three steps: first, we cut the rectangular live-action parking scene \(\Omega\) with the maximum range; then, the coordinate system is rotated according to rotation angle \(\angle\alpha\) of the live-action projection; finally, we translate the origin according to the coordinate \((x_{0},y_{0})\) of the far corner point at the bottom of the garage in the live-action coordinate system.  
Suppose there exists a point \(s\) in the parking area, its coordinate is \((x_{s},y_{s})\) in the standard parking plane system, and the angle between it and the abscissa axis is \(\angle\beta\); obviously, \(\angle\beta=\arcsin\left(\frac{y_{s}}{\sqrt{{x_{s}}^{2}+{y_{s}}^{2}}}\right)\).  
Assuming the coordinate \((x_{s},y_{s})\) is given, the equation for converting it to the coordinate \((x_{r},y_{r})\) for the live-action parking plane system can be determined:  
\[\begin{array}{l}x_{r}=\sqrt{{x_{s}}^{2}+{y_{s}}^{2}}\cdot\cos(\angle\beta- \angle\alpha)+x_{0},\\ y_{r}=\sqrt{{x_{s}}^{2}+{y_{s}}^{2}}\cdot\sin(\angle\beta-\angle\alpha)+y_{0}. \end{array} \tag{1}\]  
In the same way, assuming the coordinate point \((x_{s},y_{s})\) is given, the coordinate \((x_{r},y_{r})\) is also obtained easily. In the standard coordinate system for the parking plane, \(x(P_{is})\) and \(y(P_{is})\) are the horizontal and vertical coordinates of the central point \(P_{is}\) of the feature region _is_. Assuming that the starting inclination angle \(\Delta\angle_{B}\) is small enough, in order to obtain the shortest possible parking reference trajectory, parking experiences show that the constraint of a decreasing abscissa and the ordinate of the central point of the feature regions must be met. The specific expression is  
\[x(P_{ns})\leq,\cdots,\leq x(P_{is})\leq,\cdots,\leq x(P_{1}), \tag{2}\]  
\[y(P_{ns})\leq,\cdots,\leq y(P_{is})\leq,\cdots,\leq y(P_{1}). \tag{3}\]
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 2 State-Of-The-Art  
Solutions to increase random access capabilities and packet loss recovery can be divided in three categories, depending on which element in the video distribution chain needs modification: client-based, network-based, and content-based. The proposed work is related to the content-based approaches described here.
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
###### Abstract  
Turbo Codes (TCs) are a family of convolutional codes that provide powerful Forward Error Correction (FEC) and operate near the Shannon limit for channel capacity. In the context of modern communication systems, such as those conforming to the DVB-RCS2 standard, Turbo Encoders (TEs) play a crucial role in ensuring robust data transmission over noisy satellite links. A key computational bottleneck in the Turbo Encoder is the non-uniform interleaving stage, where input bits are rearranged according to a dynamically generated permutation pattern. This stage often requires the intermediate storage of data, resulting in increased latency and reduced throughput, especially in embedded or real-time systems. This paper introduces a vector processing algorithm designed to accelerate the interleaving stage of the Turbo Encoder. The proposed algorithm is tailored for vector DSP architectures (e.g., CEVA-XC4500), and leverages the hardware's SIMD capabilities to perform the permutation operation in a structured, phase-wise manner. Our method adopts a modular Load-Execute-Store design, facilitating efficient memory alignment, deterministic latency, and hardware portability. We present a detailed breakdown of the algorithm's implementation, compare it with a conventional scalar (serial) model, and analyze its compatibility with the DVB-RCS2 specification. Experimental results demonstrate significant performance improvements, achieving a speed-up factor of up to 3.4\(\times\) in total cycles, 4.8\(\times\) in write operations, and 7.3\(\times\) in read operations, relative to the baseline scalar implementation. The findings highlight the effectiveness of vectorized permutation in FEC pipelines and its relevance for high-throughput, low-power communication systems.  
digital signal processing; digital video broadcasting-return channel satellite; permutations; turbo codes; vector processor; very large instruction word +
Footnote †: journal: Electronics Letters
# COMS Visible Channel Calibration Using Moon Observation Data
## 3 Results
### Seasonal Variation  
To investigate seasonal variation in the degradation ratio, the difference between degradation ratio values and their regression values in Figure 2 were calculated using Equation (10),  
\[\Delta\mathrm{R}=\mathrm{R}(\mathrm{I}_{\mathrm{Instrument}}/\mathrm{I}_{\mathrm{ ROLO}})-\mathrm{R}^{\prime}[\mathrm{y}=\mathrm{mx}+\mathrm{b}]. \tag{10}\]  
In Figure 3, \(\Delta\mathrm{R}\) clearly shows seasonal variation throughout the year. Periodicity is evident with a six-month period from January to June as well as from July to December. The patterns of both periods are almost the same, as shown in Figure 3.  
To find the cause of seasonal variation periodicity, the relationship between the irradiance ratio and the Moon phase angle (the angle between Sun-Moon-COMS MI) was examined. Figure 3b shows the annual seasonal variation in the phase angle from April 2011 to December 2017.  
The Moon phase angle shows six-month periodicity; there was little difference among the years studied. This result was confirmed during Moon calibration by MTSAT-2, which was equipped with a similar meteorological image to that of COMS MI [30]. As discussed in Section 3.4 and Appendix A.1.2, more study is required to decrease the uncertainties of both the GIRO model and calculation of instrument irradiance at a large phase angle.  
Figure 2: Temporal trend of COMS MI visible channel calibration from April 2011 to March 2017. Colors represent Moon phase angles and orange shades represent the 95% confidence interval of the linear regression.
## 4 Conclusions  
With the introduction and promotion of the precision agriculture concept, agricultural production is developing towards a standardized scale and way of operation [72]. Precision seeding, as one of the ways to realize precision agriculture, is essentially the improvement of mechanized planting quality, and has significant development prospects in different tillage methods, mainly through including precision seeding in various modes such as the no-till operation, compound operation, and high-speed operation; at the same time, combined with relevant frontier technologies such as prescription map, sensor detection, GPS positioning, and unmanned driving, precision seeding has diversified developmental directions. Although the precision seeding conditions for soil-covering and compacting technology have been developed and perfected, there are still some problems to be solved to meet the evolving technological needs and sowing quality requirements.  
1. The soil-covering operational device of a large amount of soil covering will lead to excessive soil flow velocity on the seed bed impact, meaning that the seed displacement and grain-spacing consistency are affected; at the same time, the amount of uneven soil covering too much much will cause the dry soil and wet soil to be mixed, so that the seed cannot not germinate in the damp soil environment. In the actual operation process, the soil-covering process is carried out once. No secondary adjustment can be made, so achieving a stable and consistent soil-covering depth in the one-time soil-covering process is necessary.
2. The high-speed operation of precision seeding in China also provides an excellent test for the soil-compacting effect of the soil-compacting device. As the operating speed increases, the soil-compacting time of the unit area of the soil-compactingwheel decreases. The lack of soil-compacting pressure means that the seed and soil contact is not close enough, affecting the seed germination. At the same time, the soil-compacting pressure is constantly decreasing in the soil, and there needs to be more research on how to achieve a suitable and consistent soil-compacting pressure around the seed bed and on the soil surface.  
Conceptualization, W.Y. and J.H.; methodology, W.Y.; software, C.L.; validation, C.L., H.L. ([PERSON]) and H.Y.; formal analysis, H.L. ([PERSON]); investigation, W.Y.; re-sources, C.L.; data curation, H.Y.; writing--original draft preparation, W.Y.; writing--review and editing, H.L. ([PERSON]), H.Y. and H.L. ([PERSON]); visu-alization, C.L.; supervision, J.H.; project administration, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28010402).  
For studies not involving hu-mans or animals.  
Not applicable.  
The data in Figure 9 are from China National Knowledge Infrastructure.  
The authors declare no conflict of interest.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 3 Description of the INSPIRE-SAT 7 Mission and Mission Operations
### The Space Segment
#### 3.2.1 The ERS Sensors
Finally, twelve temperature sensors (2-terminal integrated circuit temperature transducer) are used to measure the interface temperature of these ERS sensors. Indeed, it is necessary to monitor the temperature (T\({}_{\rm{ERS}}\)) of the ERS sensors with a high accuracy to consider the temperature radiation loss of the nano-thermopiles (Stefan-Boltzmann law). The INSPIRE-SAT 7 mission uses the six SLCD-61N8 photodiodes (sensitivity spectral range between 400 and 1100 nm) to measure incoming solar radiation (TSI) and outgoing solar radiation (OSR).
## 2 Position and Heading Estimation Approach Based on Cross-Modal Image Matching
### Fine Alignment Based on End-to-End Trainable Matching Network  
Fine alignment for cross-modal images is very challenging due to the complicated grayscale variation and the local ground feature changes. The image matching methods based on handcrafted features have very limited ability for addressing the challenges [10; 24].  
[PERSON] et al. [24] proposed an image matching method based on convolutional neural network (CNN) for both instance-level and category-level matching, which can handle substantial appearance differences, background clutter, and large deformable transformation. Based on this method, [PERSON] et al. [10] proposed a two-stream symmetric network for aerial image matching which can adapt to the light change and the variation of ground appearance, and the matching precision is further improved in paper [25]. However, the matching precision of these methods is low, mainly due to the lower resolution of the input image and the lower resolution of the features extraction, as pointed out in paper [24].
# Risk Propagation Mechanism and Prediction Model for the Highway Merging Area
### Study on Traffic Accident Risk Prediction  
Generally, predicting the risk of traffic accidents requires a large amount of traffic flow data. By mining and extracting the characteristics of traffic accidents and establishing prediction models, it is possible to predict traffic accident risks in a certain area over a period. Traditional methods usually adopt parameter modeling methods. For example, [PERSON] et al. [16] comprehensively used a random multinomial logit model and Bayesian belief network to predict the risks of highway traffic accidents in real time. [PERSON] et al. [17] used the Bayesian logistic model to predict the risks of highway traffic accidents under heavy fog conditions. [PERSON] et al. [18] proposed an accident probability prediction model based on a dynamic Bayesian network using speed-time series data. In recent years, some researchers have also built some non-parametric machine learning models [19]. For example, [PERSON] et al. [20] explored the application of support vector machines in the real-time risk division of traffic flow. [PERSON] et al. [21] proposed a real-time traffic accident prediction method based on the AdaBoost classifier by selecting the standard deviation features of traffic flow characteristics. [PERSON] et al. [22] used support vector machines to evaluate the risks of rear-end accidents on highways. [PERSON] et al. [23] constructed a highway accident risk model based on the random forest algorithm. With the continuous development of deep learning technology, many researchers have also applied deep neural network models in the field of risk assessment. [PERSON] et al. [24] constructed a model based on a differential time-varying graph convolutional network and used multi-task learning methods to predict both traffic flow and accident risks. [PERSON] et al. [25] proposed the Hetero-Conv LSTM model, which collected multi-source heterogeneous data to predict the number of traffic accidents occurring in the next time step. [PERSON] et al. [26] constructed a traffic accident prediction model based on a deep spatiotemporal graph convolutional network based on traffic flow speed, weather conditions, and POI (point of interest) hotspots. [PERSON] et al. [27] used traffic flow, weather, and air data to predict the trend of regional traffic accidents through the LSTM model. Their approach showed significant improvements compared with SAE (sparse autoencoder) and SVM (support vector machine) models. [PERSON] et al. [28] established a stacked autoencoder model to study the impact of pedestrian flow on traffic accident risks.  
The above studies have identified the applications of traffic flow data and historical accident data in real-time accident risk prediction. However, most of these studies focused on ordinary sections of highways and urban roads. Most of these results neglected the spatial characteristics of accidents and the impact of potential factors on accidents.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 1 Introduction
* **Multi-Dimensional Signaling:** We extend sparse codes [24] designed for specific cases (i.e., \(M\leq 8\), \(K_{1}=2\)) in low dimension to generalized cases (i.e., \(2\leq M<\infty\), \(1\leq K_{1}<\infty\)) in multi-dimensional signal space. Also, to optimize multi-dimensional signal constellation, we propose a heuristic algorithm which effectively maximizes the minimum Euclidean distance among constellation points. Simulation results demonstrate the feasibility of high-order modulation for the RF tags with small form-factor and BPSK backscatter modulators. * **Reflection Coefficient Projection and Expansion:** We modify the low number of projection methods [26; 27] considered in active radios with oscillators to support passive ambient backscatter radios implemented via reflection and OFDM carriers from ambient sources. The proposed method can be applied to general AmBC scenarios, extending the previous work [24]. To the best of the authors' knowledge, a generalized construction of multi-dimensional signal space using sparse codes for both the long-range and high-rate AmBC has not yet been proposed in the literature. The rest of the paper is organized as follows. Section 2 describes the AmBC system model including sources and channels. The sparse codes at the tags will be discussed in Section 3 and the detection scheme at the reader is described in Section 4. Numerical results in Section 5 demonstrate the feasibility of the multi-dimensional signalling for AmBC. Finally, concluding remarks are given in Section 6.
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 1 Introduction
This means that the random matrix does not have to be a random Gaussian or a random Bernoulli matrix with rather restrictive conditions, such as zero mean, and which are computationally expensive [22]. In real applications either measurements are noisy, signal sparsity is inexact, or both. Here inexact sparsity refers to the situation where a signal contains a small number of significant components in magnitude, while the magnitudes of the rest are small, but not necessarily zero. Such approximately sparse signals are compressible, too [24]. CS is an emerging methodology with a solid theoretical foundation that is still evolving. Most previous analyses in CS theory relied on the restrictive isometric property (RIP) of the measurement matrix A. These analyses can be called matrix-based. The non-RIP analysis, on the other hand, is subspace-based and utilizes the classic KGG (Kashin, Garnaev, and Gluskin) inequality to supply the order of recoverable sparsity [24]. [PERSON] proposed a CS method to enhance GNSS signal acquisition performance with interference present. The interference is mitigated through the orthogonal feature between interference and the desired signal using the subspace projecting method. Meanwhile, the RIP can be preserved by projecting the Toeplitz-structured sensing matrix to ensure that the linear projection of the signal can retain its original structure and allow the recovery of the correlation output (sparse signal) [16]. This method is aligned with the topic in compressive sensing for this research, in the sense that it is subspace-based, but still uses the RIP approach to sound theory. The proposed CS model for the GNSS signal includes the three aspects shown in Figure 4. The first part is the sparse representation of the signal which consists of Toeplitz matrix design and sparse decomposition via matrix multiplication. The second part of this model is the compressed transmission; by linearly transforming the observation vector, the dimension can be reduced, which is far less than the original signal dimension. The third part is the reconstruction of the GNSS signal, since the observation vector can be calculated from the left singular and right singular vectors; the essence of the reconstruction is completed by using the convex relaxation method to match the original GNSS signal and, as part of this research, the GNSS-SVD-Convex algorithm is proposed to compress and reconstruct the signal.
## 5 Discussion
### Relationship between NMR Fractal Dimension and Mineral Compositions  
The relationships between TOC content, different mineral composition, and NMR fractal dimensions of the lower S\({}_{1}\)I\({}^{1-1}\) sub-layer and its upper layer samples were also investigated (Figure 12). The plots show that bound fluid pore fractal dimensions and movable fluid pore fractal dimensions have moderate positive correlations with TOC content while showing no correlation with quartz content. Previous studies have also demonstrated the absence of a correlation between fractal dimensions and quartz concentration due to the fact that the quartz surface is rather smooth and has fewer pores [15; 34].  
Previous research explained the increased number of micropores resulting from higher TOC and the number of micropores is mainly influenced by the specific surface area [34]. Therefore, the increasing TOC content leads to a significant increase in pores within the organic matter, thereby facilitating the development of micropores derived from the organic matter pores [35]. The pore system of shales in the middle and upper parts of the  
Figure 11: The fractal dimension, porosity, and permeability of shale samples in the Z-3 well.  
Figure 10: The relationship between porosity and permeability of shale samples in the Z-3 well (excluding the Q1 sample).  
S\({}_{1}\)11-1 sub-layer are developed with less complicated structures and thus lead to lower fractal dimensions.  
Heterogeneous pore space distribution was formed during the hydrocarbon process, influenced by the differences in structure, composition, and intensity of decomposition or condensation reactions within organic matter [36]. This process facilitated the expansion of storage space and enhanced pore connectivity. Thus, the TOC content exhibits a relatively weak positive correlation with the fractal dimension of the movable fluid pores (D\({}_{2}\)).