esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP
## 3 Methodology  
### Introduction to the ACO-BP Model  
#### 3.1.1 BP Neural Network  
The BP neural network is a common type of artificial neural network that can be applied to problems such as recognition, classification, and prediction [23]. The BP neural network exhibits several key benefits:  
1. Strong nonlinear fitting ability, capable of handling complex nonlinear problems.
2. Good adaptive ability, able to automatically adjust the network parameters according to the changes in the input data, to improve the robustness of the model and generalization ability.
3. It has good fault tolerance and can maintain good performance even if some neurons or connections in the network fail.
4. Good interpretability, with the network structure and parameters capable of adjusting to understand the workings of the network and the decision-making process.  
The transfer function for the input and hidden layers of the BP neural network is set as _tansig_, while the transfer function for the output layer is defined as \(purelin\). The training function selected for the network is \(trainlm\). The hidden layer can have more than one layer, but due to the requirements of the accuracy of the recognition of the driving style, this paper's implicit layer selected a layer with the specific topology shown in Figure 1.  
Training Steps for the BP Neural Network:  
Step 1: Determine the basic structure of the BP neural network, including the number of nodes in the input layer (\(n\)), the number of nodes in the hidden layer (\(r\)), and the number of nodes in the output layer (\(l\)). Set the initial parameter values.  
Step 2: Calculate the outputs of the hidden layer, as shown in Equation (1):  
\[H_{j}=f(\sum_{i=1}^{n}w_{ij}x_{i}-a_{j}),\ \ \ i=1,2,\cdots,r \tag{1}\]  
In Equation (1), \(w_{ij}\) is the connection weight between the input layer and the hidden layer, \(x_{i}\) represents the input vector, \(a_{j}\) is the threshold of the hidden layer, and _tansig_ is the transfer function, as shown in Equation (2):  
\[y=\frac{2}{1-e^{-2x}}-1 \tag{2}\]  
Figure 1: Structure of the BP neural network.  
Step 3: Calculate the actual output of the output layer, as shown in Equation (3):  
\[O_{k}=\sum_{j=1}^{r}H_{j}w_{jk}-b_{k} \tag{3}\]  
Step 4: Calculate the error \(e_{k}\) between the network output and the true value, as shown in Equation (4):  
\[e_{k}=O_{k}-Y_{k} \tag{4}\]  
In Equation (4), \(O_{k}\) represents the predicted value and \(Y_{k}\) represents the true value.  
Step 5: Update the weights and thresholds, as follows:  
\[w_{ij}=w_{ij}+\eta H_{j}(1-H_{j})x_{i}\sum_{k=1}^{m}w_{ij}e_{k},\ \ i=1,2, \cdots,n;j=1,2,\cdots,r \tag{5}\]  
\[w_{jk}=w_{jk}+\eta H_{j}e_{k},\ \ j=1,2,\cdots,r;k=1,2,\cdots,m \tag{6}\]  
\[a_{j}=a_{j}+\eta H_{j}(1-H_{j})\sum_{k=1}^{m}w_{jk}e_{k},\ \ j=1,2,\cdots,r \tag{7}\]  
\[b_{k}=b_{k}+e_{k},\ \ k=1,2,\cdots,m \tag{8}\]
# Local Attention Sequence Model for Video Object Detection
## 3 Kinematic Control of CDHRM
### Modified ConvGRU  
Video object detection has a high demand for real-time performance. We introduce ConvGRU to establish video temporal information. However, ConvGRU has a large amount of parameters and calculations, which seriously affects the efficiency of the model. Therefore, we modify the traditional ConvGRU and optimize its parameters and calculation so as to improve the video object detection performance without excessively increasing the running time.  
The network details of the modified ConvGRU are shown in Figure 4. First, a concatenate layer is used to connect the input state \(X_{t}\) and the hidden state \(H_{t-1}\), which can make full use of the spatial and temporal information. However, it also causes the feature dimension to be expanded by 2 times. Therefore, \(1\times 1\) convolution is used to compress the feature dimensions in order to reduce the calculation amount of subsequent modules, and, then, the design of grouped convolution [24] is adopted to optimize the parameters and calculation.  
Figure 3: Update network.  
Formally, let X and H be the current feature map and the hidden feature map, respectively. Here we use ReLU6 as the activation function to make the gating unit activation sparse so that more unimportant historical information will be forgotten. We establish the temporal information as  
\[\begin{array}{c}\mathbf{\hat{X}}_{t}=\mathbf{W}_{1}*[\mathbf{X}_{t},\mathbf{ H}_{t-1}]\\ \mathbf{Z}_{t}=\frac{1}{6}\text{ReLU}6\big{(}\mathbf{W}_{2}*\mathbf{\hat{X}}_{t }\big{)}\\ \mathbf{R}_{t}=\frac{1}{6}\text{ReLU}6\big{(}\mathbf{W}_{t}*\mathbf{\hat{X}}_{ t}\big{)}\\ \mathbf{\hat{M}}_{t}=\mathbf{W}_{2}*[\mathbf{X}_{t},\mathbf{R}_{t}\odot\mathbf{ H}_{t-1}]\\ \mathbf{\hat{H}}_{t}=\text{ReLU}(\mathbf{W}_{h}*\mathbf{\hat{M}}_{t})\\ \mathbf{H}_{t}=\mathbf{W}_{3}*\big{(}(1-\mathbf{Z}_{t})\odot\mathbf{H}_{t-1}+ \mathbf{Z}_{t}\odot\mathbf{\hat{H}}_{t}\big{)}\end{array} \tag{3}\]  
where \(\mathbf{Z}\) and \(\mathbf{R}\) represent the update gate and reset gate, respectively; [x,y] is the concatenate operation; \(*\) is the convolution calculation; and \(\odot\) is the matrix element multiplication. \(W_{1\sim 3}\) represents \(1\times 1\) convolution parameters, and \(\mathbf{W}_{\{\mathbf{z},\mathbf{z},\mathbf{h}\}}\) represents grouped convolution parameters. The gating unit activation function uses the ReLU6 function, which makes the gating unit activation sparse, allowing more unimportant historical information to be forgotten.
# MIMO Over-the-Air Computation for Distributed Estimation
## 3 The Proposed MIMO-AirComp Framework
### Source Encoder/Decoder  
Much of the existing AirComp research assumes that the source data of the nodes follow the standard normal distribution with zero mean and unit variance, \(\mathbf{d}_{k}\in\mathcal{N}(0,\mathbf{I})\)[15; 17; 18]. One of the simplest source encoders is to convert the source data with the standard normal distribution. Thus, the source encoder \(\boldsymbol{\eta}(\cdot)\) normalizes the random variable \(\mathbf{s}_{k}\) as  
\[\mathbf{d}_{k}=(\mathbf{s}_{k}-\overline{\mathbf{s}})\oslash\sqrt{\boldsymbol {\sigma}_{\mathrm{ed}}^{2}+\boldsymbol{\epsilon}}, \tag{6}\]  
where \(\overline{\mathbf{s}}\in\mathds{R}^{L_{d}}\) and \(\boldsymbol{\sigma}_{\mathrm{ed}}^{2}\in\mathds{R}^{L_{d}}\) are the element-wise mean and variance of \(\mathbf{s}_{k}\) of all nodes, respectively, \(\boldsymbol{\epsilon}\) is the small constant vector, and \(\oslash\) is the element-wise division. The constant vector \(\boldsymbol{\epsilon}\) prevents the numeric problem when the variance converges to 0, such as the consensus problem [10; 24]. Note that the state estimation application computes the mean \(\overline{\mathbf{s}}\) and variance \(\mathbf{\sigma}_{\text{ed}}^{2}\) of \(\mathbf{s}_{k}\) based on the specification or experimental measurements. By considering Equation (6), the source decoder \(\mathbf{\eta}^{-1}\) then becomes  
\[\hat{\mathbf{s}}=\sqrt{\sigma_{\text{ed}}^{2}+\mathbf{\epsilon}}\circ\hat{\mathbf{d} }+\mathbf{\
u}^{H}\left(\sum_{k=1}^{K}\mathbf{H}_{k}\mathbf{\mu}_{k}\overline{\mathbf{\xi}} \right), \tag{7}\]  
where \(\circ\) is the element-wise product.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 6 Challenges, Opportunities, and Research Directions
### Physical Layer Security Challenges  
Physical layer security (PLS) is an emerging concern in FL over wireless networks due to the open and shared nature of the communication medium [147]. FL training involves frequent transmission of model updates between the central server and user devices, making it susceptible to various eavesdropping and jamming attacks. PLS offers a promising solution by providing perfect security without the need for complex key distribution or encryption algorithms, thus reducing communication overhead and enhancing security without increasing energy consumption [148; 149].  
Innovative PLS methods tailored for FL over wireless systems will leverage wireless characteristics, such as secure beamforming and cooperative jamming, to safeguard FL transmissions without compromising performance. Recent studies [150; 151; 152; 153] have proposed various methods for integrating PLS into FL systems. However, several unresolved challenges persist in deploying PLS for FL over wireless networks. A primary challenge lies in designing lightweight, real-time, adaptive PLS techniques capable of responding to dynamic network conditions, such as fluctuating channel quality and varying user participation, while maintaining the robustness and reliability of FL systems. Additionally, balancing the overhead introduced by PLS mechanisms with the need for energy-efficient FL operations remains a critical issue. Moreover, addressing the trade-off between ensuring robust security and accommodating the resource constraints of edge devices, without compromising FL training efficiency, continues to be a significant area for further research and development.
## 3 Results
### Regional Scale Simulation
#### 3.2.3 Validation of Estimated NPP  
The NPP of the study area in 2020 obtained in this study was basically consistent with the MODIS NPP data products. The NPP of the study area estimated by BEPSHourly ranged from 13.14 to 1117.26 g C m\({}^{-2}\) a\({}^{-1}\), with an average of 564.71 g C m\({}^{-2}\) a\({}^{-1}\). MODIS NPP values ranged from 270 to 811 g C m\({}^{-2}\) a\({}^{-1}\), with an average of 503.96 g C m\({}^{-2}\) a\({}^{-1}\). As can be seen from Figure 11, the NPP estimated in this study had a good correlation with MODIS NPP. The \(R^{2}\) of the regression equation between MODIS NPP and estimated NPP was 0.44.
## 1 Introduction
proposed a multibody model of a six-axis industrial robot that included flexible bodies, which were exported from the corresponding finite element model of the structural parts. This model was used to accurately describe the robot's dynamic behavior. Since it was built purely based on development and datasheet data, experimental methods were not needed for the parameter identification of the structural parts, making this approach especially relevant during the development of new manipulators [16]. To improve the robot's dynamic performance, the approaches in the abovementioned research can be divided into two categories: modeling approach and experimental approach. The modeling approach attempts to predict the dynamic behavior through building a model of the robot's structure. The common methods, which are kinematics analysis, MBS, and FEM, are adopted. The experimental approach directly measures the parameters that influence the robot's dynamic behavior. Based on the analysis of the parameters, strategies are developed to improve the robot's dynamic performance. The robot's transmission system is complex, and it mainly includes harmonic reducers, rotate vector (RV) reducers, motors, and bearings. This makes it difficult to obtain accurate modeling parameters. Therefore, experimental modal analysis is an important tool to identify key parameters of the robot's structure in both approaches. The dynamic stiffness is mainly obtained by experimental modal analysis and FEM. The studies of dynamic stiffness mainly focus on the vibration of robotic machining. Two aspects are investigated to reduce the vibration. First, the eigenfrequency distribution is analyzed to avoid self-excitation during the machining process. Second, robot pose configuration and machining direction are optimized based on the analysis of the variations in the dynamic stiffness. Although experimental modal analysis is a type of experimental method, the dynamic stiffness is obtained indirectly on the basis of FRFs (Equation (3)). There could therefore be deviations between the real value and measurement value. When industrial robots are adopted for machining applications, especially in finish machining, the machining quality can be sensitive to these deviations. Therefore, this paper analyzes the dynamic stiffness of an articulated industrial robot using an indirect approach and a direct approach. 1. The indirect approach adopts the FRFs based on the experimental modal analysis. 2. The direct approach measures the vibration displacement and the corresponding excitation force through a force sensor and high-speed camera system. This research thoroughly studies the dynamic stiffness of an industrial robot using the two approaches mentioned above. The following section describes the methods used to obtain the requireddata for the stiffness calculation. Through data processing, dynamic stiffness is obtained and evaluated. The difference in dynamic stiffness between these two approaches is discussed.
## 3 Science Data Product Performance
### The CrIS/ATMS Instruments and Science Data Products
Initial development of the microwave-only and microwave/infrared sounding products for the JPSS mission were based on pre-existing microwave/infrared sensor pairs, such as the AMSU-A and MHS paired with the IASI instrument, and the AMSU-A instrument paired with the Aqua-AIRS instrument. Details of these instruments and their channel characteristics have been described in many publications [69; 70; 71]. These developments led NOAA to define, develop, and operationalize two EDR operational product development systems. The NOAA Unique Combined Atmospheric Processing System (NUCAPS, [84]) uses hyper-spectral CrIS IR observations (aided by ATMS), while the Microwave Integrated Retrieval System (MiRS, [85]) relies solely on ATMS observations.
# SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios
## 3 Proposed Method
### Improve the Accuracy of SMYOLO through Scale Matching  
In low-altitude scenes, because the pedestrian targets captured by aerial photography are small, the practical features of the targets that can be extracted are limited, which is not conducive to detecting pedestrian targets in low-altitude scenes, which leads to low accuracy. In addition, SMYOLO also has a certain degree of accuracy loss while compressing, so this paper proposes to match the pre-training dataset with the detector learning dataset, which will improve the feature representation ability of the detector. SMYOLO can achieve more accurate pedestrian detection in low-altitude scenes. Scale Match matches the pre-training dataset with the detector learning dataset to improve feature representation and improve the detector's performance. The scale matching is essential to make the histogram distribution of the pre-training dataset and the detector learning dataset similar.  
First, calculate the scale distribution histogram of the target in the detector learning dataset. Then, calculate the average size \(s_{1}\) of the label box in any picture in the pre-training dataset, select a bin in the scale histogram of the detector learning dataset, and determine the used bin on the size \(s_{2}\) of the scale-matched label frame; the scale migration ratio \(s2/s1\) is obtained. Finally, scale matching is performed on the pictures in the pre-training dataset according to the scale migration ratio. The calculation formula for scale matching is shown in Equation (8). Among them, \(s_{0}\in[min(s_{1}),max(s_{1})]\), \(min(s_{1})\), and \(max(s_{1})\) represent the minimum and maximum size of the object, respectively. \(p_{size}\) represents the general density function, the abscissa of the probability density function is the size of the dataset label frame, and the ordinate is the probability density. After the pre-training dataset and the detector learning dataset are scale-matched, the performance of the SMYOLO target detector is improved.  
\[\int_{min(s_{1})}^{s_{0}}p_{size}(s_{2};E)ds_{1}=\int_{f(min(s_{1}))}^{f(s_{0})} p_{size}(s_{2};D_{train})ds_{2} \tag{8}\]  
This article first processes the VisDrone UAV dataset to extract pictures containing pedestrian targets. Secondly, we match the dataset COCO of the pre-trained model with the pedestrian dataset we extracted to enhance the feature representation ability of SMYOLO and increase the accuracy of the model.
# Block Recovery Rate-Based Unequal Error Protection for Three-Screen TV
## 3 BRR-Based UEP Algorithm and Scalable Video Layer-Switching  
### Performance Metric  
This section introduces a new performance metric to predict the video quality from packet losses. In general, the packet loss recovery procedure is as follows. Assuming that video packets of \(B(t,q)\) are encoded as \(N(t,q)\) packets on the server side where \(N(t,q)=B(t,q)+F(t,q)\), where \(F(t,q)\) represents FEC packets assigned in \(B(t,q)\). If the minimum number of successfully received packets is more than \(B(t,q)\), the original source packets can be reconstructed. The block recovery rate (BRR) of \(B(t,q)\) is calculated as  
\[R_{b}(t,q)=\sum_{i=B(t,q)}^{N(t,q)}C_{i}^{N(t,q)}.(1-p)^{i}.p^{N(t,q)-i} \tag{1}\]  
where \(p\) is the packet loss rate and \(C_{i}^{N(t,q)}\) represents the combinations of the number of packets \(i\) received successfully within \(N(t,q)\). To reflect error propagation effects of scalable video in (1), the prediction directions for each block are considered. Figure 6 shows the hierarchical prediction structure of scalable video.  
The block information of temporal layer \(t\) is referenced by that of the lower temporal layer (\(t-1\)). Also, the block information of quality layer \(q\) in temporal layer \(t\) is affected by the information from the lower quality block (\(q-1\)) in the same temporal layer (\(t\)). For example, \(R_{b}(1,1)\) is affected by both \(R_{b}(0,1)\) in the same quality layer of the lower temporal layer and \(R_{b}(1,0)\) in the lower quality layer of the current temporal layer. Based on the hierarchical prediction structure between the temporal and quality layer, (1) can be updated as  
\[\hat{R}_{b}(t,q)=\left\{\begin{array}{ll}R_{b}(t,q),&if\ t=0,q=0\\ R_{b}(t,q-1)\cdot R_{b}(t,q),&if\ t=0,q\
eq 0\\ R_{b}(t-1,q)\cdot R_{b}(t,q),&if\ t\
eq 0,q=0\\ R_{b}(t-1,q)\cdot R_{b}(t,q-1)\cdot R_{b}(t,q),&otherwise\end{array}\right. \tag{2}\]  
In (2), \(\hat{R}_{b}(t,q)\) is computed by the temporal and quality layer index in scalable prediction structure. If \(t=0\) and \(q=0\), since there are no reference blocks, \(\hat{R}_{b}(t,q)\) is the same as \(R_{b}(t,q)\). If \(t\) or \(q\) is not \(0\), it is influenced by the lower scalable block with non-zero temporal or quality layer index. Finally, when  
Figure 6: Prediction structure of \(R_{b}(t,q)\) in the scalable video coding structure.  
both \(t\) and \(q\) are not zero, \(\hat{R}_{b}(t,q)\) is affected by the lower temporal and quality blocks. Based on (2), the average block recovery rate (\(R_{avg}\)) in a group of pictures (GOP) is calculated as  
\[R_{avg}=\frac{\sum_{t=0}^{T-1}\sum_{q=0}^{Q-1}\hat{R}_{b}(t,q)}{T\cdot Q} \tag{3}\]  
where \(T\) and \(Q\) represent the maximum number of temporal and quality layers, respectively.
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
## 4 Validation and Analysis  
### Settings  
The system was implemented on the Xilinx ZC706 evaluation board [23], featuring the Zynq-7000 XC7Z045-2 FFG900C SoC [24] with two ARM Cortex-A9 processors, 218,600 LUTs, 437,200 FFs, and 19.16 Mb of BRAM. The system was evaluated using five image datasets:  
* ImageNet64 [25]: A downsampled variant of the ImageNet dataset with 50,000 images at 64 \(\times\) 64 pixels.
* DIV2K [26]: Contains 100 high-resolution images from diverse scenes.
* CLIC.p [27]: Comprises 41 high-quality color images, mainly in 2K resolution.
* CLIC.m [27]: Includes 612K images captured with mobile devices, at predominantly 2K resolution.
* Kodak [28]: Contains 24 uncompressed color images at 768 \(\times\) 512 resolution.  
The dataset is initially written to FLASH. The ARM processor initializes the DDR3 controller to transfer data from FLASH to DDR3 SDRAM. After transfer, data are sent to the compression system via AXI DMA. The ARM processor manages the flow, sending one tile at a time and retrieving compressed data back to DDR3 SDRAM. If no new data are received, the compression process concludes and an interrupt is sent to the ARM processor with runtime information for efficiency calculations. The compressed data are then stored in FLASH for further analysis.
###### Abstract
[13] summarized the factors that trigger SMTs, including rock properties and mining conditions, and proposed that the fracture of super-thick strata under the influence of mining conditions promoted the occurrence of SMTs. SMTs are the result of the dynamic adjustment of macro energy fields and stress fields in super-thick strata. This process is relatively slow, but it is the key to revealing the occurrence mechanism of SMTs. [PERSON] et al. [14] established an energy index, EI, to analyze the characteristics of energy change; their results showed that the energy change presented a bimodal distribution before the occurrence of SMTs. [PERSON] et al. [15; 16] believe that roof deformation occurs before the SMT and the elastic energy accumulates quickly, and further analyzed the influence of SMTs on surface stability. [PERSON] et al. [17] studied the evolution characteristics of mine vibration energy and found that the remaining part of the elastic strain energy is the source of mine vibration energy. [PERSON] et al. [18] analyzed the distribution law of mining stress fields based on wave velocity CT inversion, and found that SMTs occurred after the stress increased sharply. [PERSON] et al. [19] tested the stress states of different strata and found that the stress states of sub-horizontal strata changed dramatically before the occurrence of SMTs. [PERSON] et al. [20] elaborated that the stress concentration in super-thick strata would lead to the accumulation of elastic energy, which provides the basis for triggering SMTs. The above research indicates that the fracture and movement of super-thick strata play an important role in the occurrence of SMTs in deep mining. The evolution characteristics of the stress field and energy field before the occurrence of SMTs are systematically studied, which provides great inspiration for the further investigation of the occurrence mechanism of SMTs. In order to explain the occurrence mechanism of SMTs triggered by goat mining with super-thick strata in the Ordos mining area, the characteristics of fracture movement of super-thick strata should be explored [21], which greatly depends on real-time observation of strata separation failure in super-thick strata, and analysis of the changing characteristics of internal fracture modes of different strata. This study was based on the SMTs induced by goaf mining in the longwall (LW) 1208 of Shilawusu Coal Mine, Ordos mining area. The techniques of strata separation detection and strata deformation monitoring were used. The temporal and spatial relationship between the fracture movement of the super-thick strata and the SMTs was established in this study. Based on the Reissner theory of thick plates, the fracture movement law of super-thick strata during goaf mining was analyzed. The focal mechanism of SMTs was calculated by the relative moment tensor inversion method. The patterns of evolution of fracture mode of the source of mining tremors triggered by step fracture of strata under the super-thick strata was analyzed. This analysis reveals the mechanism of the occurrence of SMTs triggered by the goaf mining with the super-thick strata.
# Seismic Multi-Parameter Full-Waveform Inversion Based on Rock Physical Constraints
## 3 Results
### Numerical Experiments of the Layered Model
#### 3.2.2 Numerical Experiments Based on Lithological Constraints
When there was no constraint, the inversion results of the model were rather scattered around the true model dataset, and it is impossible to clearly distinguish the lithology corresponding to the model parameters. However, after adding lithological constraints, the inversion results can move towards the clustering direction contained in the prior information. Most of the results fall within the standard deviation range provided by the prior information, and the number of main lithology types developed in the underground media of the research area can be more intuitively distinguished.