esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Velocity Field Interpolation Based on Attention Mechanism
## 1 Introduction
[25] are dedicated to solving the same problem, using an improved fully convolutional network (FCN) [26] with fewer parameters, thus more efficient than DNN. [PERSON] et al. [27] used a large synthetic dataset and established velocity models directly from seismic trace sets interpolation based on the VGG (Visual Geometry Group) [28] network. These deep learning 3D interpolation methods typically rely on standard convolution operations with fixed kernel sizes and limited receptive fields, meaning the model can only capture local geological features and is unable to understand global spatial relationships effectively, presenting limitations for handling large-scale geological data. Most of the current deep learning interpolation methods are based on the training of large datasets or specialized training datasets, which is contrary to the reality of interpolation tasks. In real scenarios, the acquisition of velocity field data will consume a great amount of manpower and financial resources and is easily hindered by natural conditions, making the data very sparse [29]. Secondly, the network finds it difficult to transfer in the face of different underground features in various scenarios. More importantly, some methods are based on two-dimensional spatial processing, which does not conform to the three-dimensional features of underground data, and will also lose some inter-dimensional related information. For this reason, we have built a 3D velocity field intelligent interpolation method based on a hybrid triple attention mechanism, the JointA 3 DUnet network model. It selectively enhances information exchange between dimensions by introducing triple and channel attention mechanisms based on the traditional U-Net structure. In addition, dilated convolution is introduced to increase the receptive field of the convolution kernel. Through transfer learning, we have enhanced the performance of the algorithm in interpolation tasks. It is worth noting that our method operates in an unsupervised mode, learning only from input data, and can effectively interpolate missing velocity field data. We have verified our method using synthetic and real data. The JointA 3 DUnet network model demonstrates superiority in 3D velocity field interpolation, showing better performance compared with traditional intelligent interpolation methods.
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
###### Abstract
In the early stage of this study, we completed the construction of the data monitoring platform for private 5G networks and found that the abnormal phenomenon of AGVs driving is strongly related to high latency, but the current O&M is based on manual experience or simple data analysis, but the latency-sensitive scenario requires strong timeliness. Therefore, based on the China Telecom network data platform, this study proposes an anomaly detection and abnormal early warning models for AGV's abnormal driving scenarios in private 5G networks, which efficiently detects high latency using the proposed model (the ConvAE-Latency model). Based on this, the LstmAE-TL model is proposed to realize abnormal early warning with a 15 min interval using the characteristics of long short-term memory (LSTM). Simultaneously, transfer learning was used to solve the problem in which loss cannot converge in the training process of abnormal early warning detection owing to the small sample size. However, during data analysis, it was found that the percentage of abnormal samples was few, owing to private 5G networks running smoothly. Considering that the clustering of few samples cannot prove the correlation in the same class, we only studied anomaly detection and abnormal early warning, and anomaly location was not considered. The main contributions of this study are as follows:  
1. Instead of simulation data [4], the training data of proposed models are from the China Telecom network data platform, and the practicability of the ConvAE-Latency and LstmAE-TL models is verified. 2. Considering latency fluctuation as the important indicator for anomaly detection, compared to other methods [5; 6], the ConvAE-Latency model uses the latency fitting module to enhance the correlation between target indicators and hidden features. Transfer learning is applied to solve the problem of fewer abnormal data samples; because of the smooth running of private 5G networks, compared to related works [7,8], the LstmAE-TL model works better. The remainder of this paper is organized as follows: Section 2 introduces state-of-the-art background information and studies, Section 3 introduces the method used in this study, and Section 4 shows the dataset and experimental results. Finally, Section 5 concludes the paper and discusses future research directions.
## 4 Controller Design
### Triangular Wave Path-Tracking Control Experiment  
The experiments of the path tracking control are compared with using traditional PID controller and FSMC without additional loading and with a 2 kg loading mass added on the 500 mm length slider, respectively. The path-tracking control is set as the triangular wave path with different periods and the amplitude is set to \(\pm\)10 degrees.  
Figure 12: Comparison of experimental results for step input for the 1-DOF robotic lower limb system driven by a single-PAM with a torsion spring without loading between PID (Proportional–Integral–Derivative) controller and FSMC: (**a**) the step response; (**b**) the angular tracking error.  
Figure 13: Comparison of experimental results for step input for the 1-DOF robotic lower limb system driven by a single-PAM with a torsion spring with a 2 kg loading mass between PID controller and FSMC: (**a**) the step response; (**b**) the angular tracking error.  
Figure 16: Experimental results for triangular wave path tracking control with a period of 10 s for the 1-DOF robotic lower limb system driven by a single-PAM with a torsion spring without loading by using the PID controller: (**a**) step response; (**b**) angular tracking error; (**c**) control signal.  
Figure 15: Experimental results for triangular wave path tracking control with a period of 20 s for the 1-DOF robotic lower limb system driven by a single-PAM with a torsion spring without loading by using the FSMC: (**a**) step response; (**b**) angular tracking error; (**c**) control signal.  
In order to analyze and compare the results in detail, the integral of absolute error (IAE) is used to illustrate the accumulative error of the PID controller and FSMC under various experimental conditions. In the case of no load, the PID controller has an IAE of 64.1752 in a period of 20 s and an IAE of 105.278 in a period of 10 s. However, the FSMC can achieve an IAE of 30.4597 in period of 20 s and an IAE of 40.5492 in period of 10 s. Thus, FSMC can perform much better than PID control in this case.
## 5 Experiments
### Rank Estimation  
In the previous experiments, we have considered channels with unitary rank, for which one can assume without loss of optimality a number of features in the LSTD parametrization equal to \(K=1\). In order to implement predictors for multi-antenna frequency-selective channels, one instead needs to first address the problem of estimating the number of features. Here, we evaluate the performance of the approach proposed in Section 4.7 for rank estimation. To this end, we set the number of antennas as \(N_{R}=8\) and \(N_{T}=8\), and consider the 19-clustered channel model with delay spread ratio 2. Figure 6 shows the NMSE evaluated on the meta-training, meta-validation, and meta-test data sets as a function of total number of features \(K\). The meta-training set contains 20 frames, the meta-test 200 frames, and the meta-validation set 20 frames. The meta-training loss is monotonically decreasing with \(K\), because a richer parametrization enables a closer fit of the training data. In contrast, both meta-test and meta-validation loss are optimized for an intermediate value of \(K\). The main point of the figure is that the meta-validation loss, while only containing 20 frames, provides useful information to choose a value of \(K\) that approximately minimizes the meta-test loss. In contrast, although we can see that \(K=3\) is a proper estimate of the channel rank for the considered set-up, AIC-based rank estimation gives the highly overestimated value \(K=200\), which deteriorates the prediction performance, as can be seen in Figure 6. Throughout the following experiments, we will follow the proposed procedure to select \(K\) for meta-learning, whereas for all the other schemes, we adopt AIC-based rank estimation to determine \(K\).  
Figure 4: Multi-antenna frequency-flat channel prediction performance as a function of the total number of antennas, \(N_{R}N_{T}\), under a single-clustered, single-tap (\(W=1\)), 3 GPP SCM channel model for a fast-varying environment with number of training samples \(L^{\text{new}}=1\) (\(K=1\)).  
Figure 5: Multi-antenna frequency-flat channel prediction performance as a function of the total number of antennas, \(N_{R}N_{T}\), under a single-clustered, single-tap (\(W=1\)), 3 GPP SCM channel model for a slow-varying environment with number of training samples \(L^{\text{new}}=1\) (\(K=1\)).
## 4 Congestion Mitigation Algorithms for WSNs
### Previous Work on WSN Congestion Mitigation Schemes
#### 4.1.3 Game Theory-Based Algorithms for Congestion Mitigation in WSN
For selecting the parent for traffic load balancing, the aforementioned technique uses a queue utilization factor [73]. The authors in [41] proposed the mechanism for congestion control for 6 LoWPAN networks, called Game Theory Congestion Control (GTCC). This algorithm senses congestion via the flow rate of the packet, also called a packet generation rate, deducted from the service rate of the packet. When any parent node senses congestion by the Data Information Object (DIO) control packet, it transmits the message of congestion to all affiliated children. After receiving the DIO message all affiliated children start changing their parents. In this method, the nodes utilize game theory for deciding whether to change the parent or not. When the parent change is completed, the new DIO message is transmitted to inform the other nodes about the update. In [66], the hybrid congestion mitigation mechanism was proposed for 6 LoWPAN, named Optimization-based Hybrid Congestion Alleviation (OHCA). This employs both resource control and traffic regulation approaches. During congestion, OHCA utilizes Grey Relational Analysis (GRA) [65] to find a congestion-free alternate path. If an alternate path is not available, then OHCA applies a traffic reduction mechanism via a game theory-based congestion control framework (GTCCF)\"[38] and the adjustment of sending data rates of the related leaf nodes is carried out. Game theory-based algorithms control the traffic [23]. For solving the congestion issue, the work in [42] and the algorithms employed in [41] overlap. They used game theory for routing the proposed algorithm to be aware of application priorities as well as node priorities, although, game theory based on no cooperation supports a framework for analysis and characterizing the decisions and interactions among various players having a conflict of interest [74]. In [67], the authors proposed a congestion mitigation mechanism based on game theory through resource control and traffic control strategies. However, this mechanism does not consider the parameter of energy. This method also does not consider the environment of the 6 LoWPAN network and has not been tested on Cooja. One of the major concerns is energy consumption. Hence, the efficiency of energy is the prima facie benchmark in limited-resource networks and in energy like WSNs [75]. Yet, the currently proposed solution for congestion mitigation only emphasizes the delay or network throughput and does not consider the aforementioned issue [76]. In [77], the authors proposed a novel algorithm, Non-cooperative Gaming for Energy-efficient Congestion Control (NGECC). This work was the first time the energy parameter was considered for the 6 LoWPAN network. Moreover, this work was tested on the real-time Cooja simulator and Contiki operating system.
# Review of Advancement in Variable Valve Actuation of Internal Combustion Engines
## 3 Variable Valve Duration (VVD) System  
In 1993, MG Rover developed a 1.4 L K-series engine with a variable valve control (VVC) system, which was the first production continuous VVD (CVVD) system. It is based on an eccentric rotating disc to drive the inlet valves of every two cylinders. Since eccentric shape creates nonlinear rotation, the opening period of the valves can be varied by controlling the eccentric position of the disc. The basic concept was developed by [PERSON] and it was published and patented back in 1973 [49]. In this design, the control is purely to vary the valve duration, with the valve lift fixed, thus differing from various DVVL or CVVL designs.  
In 2019, Hyundai Motor Group announced that it developed CVVD technology to be in the Smartstream G1.6 T-GDi for future Hyundai and Kia vehicles [50; 51; 52]. Their design is based on a concept disclosed by [PERSON] et al. [53]. It also involves utilization of some pins and slots to create eccentric alignment. With the duration variation in accordance to driving conditions, it is able to deliver a 4% increase in performance along with a 5% boost in fuel efficiency. The CVVD technology also helps reduce tailpipe emissions by 12% [50].
## Appendix E Probability of No Outage at User 2  
\[P\bigg{(}\alpha_{1}\geq\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa},\alpha_{2}\geq\frac{D_{2}}{\mu_{2}\ \kappa}\bigg{)}=\int_{\frac{D_{2}}{\mu_{2}\ \kappa}}^{\infty}\overline{F}_{A_{1}}\bigg{(}\frac{D_{1}(1+\mu_{2}\kappa \alpha_{2})}{\mu_{1}\ \kappa}\bigg{)}f_{A_{2}}(\alpha_{2})d\alpha_{2} \tag{115}\]  
\[\overline{F}_{A_{1}}\bigg{(}\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa}\bigg{)}=P\bigg{(}\alpha_{1}\geq\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{ \mu_{1}\ \kappa}\bigg{)}=\int_{\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa}}^{\infty}f_{A_{1}}(\alpha_{1})d\alpha_{1} \tag{116}\]  
Substituting (7) in (116) gives  
\[\overline{F}_{A_{1}}\bigg{(}\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1} \ \kappa}\bigg{)}=\frac{1}{N\
u_{1}{}^{2}}\int_{\frac{D_{1}(1+\mu_{2}\kappa \alpha_{2})}{\mu_{1}\ \kappa}}^{\infty}exp\bigg{(}-\frac{\alpha_{1}}{N\
u_{1}{}^{2}}\bigg{)}d \alpha_{1} \tag{117}\]  
Simplifying (117) results in  
\[\overline{F}_{A_{1}}\bigg{(}\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1} \ \kappa}\bigg{)}=exp\bigg{\{}-\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa N\
u_{1}{}^{2}}\bigg{\}} \tag{118}\]  
Substituting (118) in (115)  
\[P\bigg{(}\alpha_{1}\geq\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa},\alpha_{2}\geq\frac{D_{2}}{\mu_{2}\ \kappa}\bigg{)}=\int_{\frac{D_{2}}{\mu_{2}\ \kappa}}^{\infty}exp\bigg{\{}-\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1} \ \kappa N\
u_{1}{}^{2}}\bigg{\}}f_{A_{2}}(\alpha_{2})d\alpha_{2} \tag{119}\]  
Substituting (8) in (119)  
\[P\bigg{(}\alpha_{1}\geq\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa},\alpha_{2}\geq\frac{D_{2}}{\mu_{2}\ \kappa}\bigg{)}=\int_{\frac{D_{2}}{\mu_{2}\ \kappa}}^{\infty}exp\bigg{\{}-\frac{D_{1}\mu_{2}\kappa\alpha_{2}}{\mu_{1} \ \kappa N\
u_{1}{}^{2}}\bigg{\}}\cdot\frac{1}{N\
u_{2}{}^{2}}exp\bigg{(}-\frac{ \alpha_{2}}{N\
u_{2}{}^{2}}\bigg{)}\ d\alpha_{2} \tag{120}\]  
\[=\frac{1}{N\
u_{2}{}^{2}}\ exp\bigg{\{}-\frac{D_{1}}{\mu_{1}\ \kappa N\
u_{1}{}^{2}}\bigg{\}}\int_{\frac{D_{2}}{\mu_{2}\ \kappa}}^{\infty}exp\bigg{\{}-\frac{D_{1}\mu_{2}\kappa\alpha_{2}}{\mu_{1} \ \kappa N\
u_{1}{}^{2}}\bigg{\}}\cdot exp\bigg{(}-\frac{\alpha_{2}}{N\
u_{2}{}^{2 }}\bigg{)}\ d\alpha_{2} \tag{121}\]  
\[=\frac{\frac{1}{N\
u_{2}{}^{2}}\ exp\bigg{\{}-\frac{D_{1}}{\mu_{1}\ \kappa N\
u_{1}{}^{2}}\bigg{\}}}{\bigg{(}\frac{D_{1}\mu_{2}}{\mu_{1}\ \!N\
u_{1}{}^{2}}+\frac{1}{N\
u_{2}{}^{2}}\bigg{)}}\bigg{[}0-exp\bigg{\{}-\frac {D_{2}}{\mu_{2}\ \kappa}\bigg{(}\frac{D_{1}\mu_{2}}{\mu_{1}\ \!N\
u_{1}{}^{2}}+\frac{1}{N\
u_{2}{}^{2}}\bigg{)}\bigg{\}} \bigg{]} \tag{123}\]  
\[=\frac{\frac{1}{N\
u_{2}{}^{2}}\ exp\bigg{\{}-\frac{D_{1}}{\mu_{1}\ \!N\
u_{1}{}^{2}}\bigg{\}}}{\bigg{(}\frac{D_{1}\mu_{2}}{\mu_{1}\ \!N\
u_{1}{}^{2}}+\frac{1}{N\
u_{2}{}^{2}}\bigg{)}}\cdot exp\bigg{\{}-\frac{D _{1}\mu_{2}D_{2}}{\mu_{1}\!\!N\
u_{1}{}^{2}\mu_{2}}\bigg{\}}\cdot exp\bigg{\{}- \frac{D_{2}}{\mu_{2}\ \kappa N\
u_{2}{}^{2}}\bigg{\}} \tag{124}\]  
Simplifying and rearranging (124) results in (21).
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 4 Bearing Capacity under Eccentric Load
### Parameter Analysis of Bearing Capacity  
#### 4.2.1 Influence of Eccentricity  
Figure 11a shows the load-displacement curves of the DC joints under different eccentricities. The bearing capacities of the DC joints decrease with eccentricity, and the decline rate gradually increases. Figure 11b shows the effect of eccentricity on the initial compression stiffness and yield load of the DC joints. The results show that the initial compression stiffness and yield load are inversely correlated with eccentricity, and the correlation gradually increases. While the eccentricity increases from 0 mm to 36.5 mm, the initial compression stiffness and yield load decrease about 20 kN/mm and 70 kN, respectively. When the eccentricity exceeds 73 mm, the initial compression stiffness and yield load decrease by about 84 kN/mm and 550 kN, respectively, for each increase of eccentricity by 36.5 mm.
## 1 Introduction
[37] used optimal wavelet packet transform (OWPT) to construct MI-EEG feature vectors, which were used to train long short-term memory (LSTM) based on a recurrent neural network (RNN). The algorithm performs well on dataset III of the BCI Competition 2003; however, its structure is overly complex. [PERSON] et al. [32] used a new CNN structure to classify P300 signals. The algorithm performs well on the BCI competition P300 datasets. Although the classification results of the above deep learning methods perform well, these networks are commonly complex and have massive parameters. In this paper, we propose a new neural network that not only simplifies the network structure and reduces the parameters but also improves the classification performance. In the study, a new CWT-simplified convolutional neural network (SCNN) algorithm is proposed, based on using deep learning to identify MI-EEGs. First, the CWT is used to map the MI-EEG data into time-frequency image signals, which contain time and frequency domain features. Second, we propose a convolutional neural network without pooling layers, named SCNN. There are two convolutional layers in SCNN to extract the time and frequency domain features, and finally, we use softmax to classify the MI-EEG data. The above method is validated on the BCI Competition IV Dataset 2b. The experimental results show that the performance of our algorithm is improved, compared with other algorithms. In addition, when using the same MI-EEG signal and SCNN, compared with common spatial pattern (CSP), FFT, and STFT, the test results show that the performance of CWT is better.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 5 Experiments and Results
### Tests Results
#### 5.3.2 Positioning
When using raw UWB ranging estimates for correcting the vehicle's position, the RMSE of the positioning distance error is reduced to 41.4 cm. In this case, the 95% of the computed positions have a positioning distance error of 61.4 cm or less. Finally, the results improve considerably when post-processing is applied. These results show a RMSE of the positioning distance error of 18.6 cm. Furthermore, the 95% of the computed positions have a positioning distance error of 27.1 cm or less. Figure 16 shows the cumulative distribution function (CDF) of the RC car positioning distance error when computing its position from its on-board data alone and when combining these data with UWB ranging estimates, both with and without post-processing. For the first solution (IMU-Odometry), only 20% of the computed positions have a positioning distance error up to 40 cm. However, these positions are the ones corresponding with the initial phase of the trajectory when the sensors have not accumulated a big error. This is reflected in the fact that the 50% and 99.9% of the computed positions have a positioning distance error up to 200 cm and 410 cm respectively. For the second case (UWB-IMU-Odometry (raw)), the 20% of the computed positions have a positioning distance error up to 24.2 cm, improving considerably the results of the first solution. Furthermore, the 50% and 99.9% of the computed positions present a positioning distance error up to 34.3 cm and 169.6 cm respectively. For the third solution (UWB-IMU-Odometry (processed)), the 20% of the computed positions have a positioning distance error up to 10.6 cm. Additionally, the 50% and 99.9% of the computed positions have a positioning distance error up to 17.6 cm and 44.1 cm respectively, reinforcing again that this last approach achieves the best results in comparison with the previous solutions.
# Seamless Indoor-Outdoor Localization Through Transition Detection
## 3 Handover Strategy Based on Three State Density Functions
### Position-Based Model  
The position-based distribution model takes the estimated position as the input and returns the probabilities of states. The position-based probability of states at the \(k\)th location \(P_{k}^{position}(s)\) is an arbitrarily designed nonlinear function, which takes \((\rho_{k},p^{transition})\) as inputs, where \(\hat{p}_{k}\) is the estimated position at the \(k\)th location, and \(p^{transition}\) is the position of the transition point nearest to the estimated position, as shown below.  
\[P_{k}^{position}(s=int)=\frac{1-||\hat{p}_{k}-p^{transition}||}{2-||\hat{p}_{k}- p^{transition}||} \tag{7}\]  
\[P_{k}^{position}(s=transition)=\frac{||\hat{p}_{k}-p^{transition}||}{2-||\hat{p}_{k }-p^{transition}||} \tag{8}\]  
\[P_{k}^{position}(s=out)=\frac{1-||\hat{p}_{k}-p^{transition}||}{2-||\hat{p}_{k}- p^{transition}||} \tag{9}\]  
The probability of being in a transition state increases as the estimated position approaches the transition point, reaching a value of 1 when the estimated position coincides with the transition point. The remaining probability, after accounting for the transition state, is equally distributed between the indoor and outdoor states.