esa-sceva/satcom-chunk-collection huggingface dataset:
# Double Branch Attention Block for Discriminative Representation of Siamese Trackers
## 4 Results
### Algorithm Comparison
#### 4.2.1 UAV123 Benchmark  
UAV123 [17] contains 123 image sequences that are collected by a low-altitude unmanned aerial vehicle (UAV). The targets in these images have a tiny size which makes them challenging for tracking. Accuracy and Precision are used to evaluate the performance of the tracker. Accuracy measures the success rate by calculating the ratio of video frames whose Intersection over Union (IoU) between the ground truth and the predicted bounding box exceeds a given threshold. The larger the Accuracy, the better the tracker. Precision measures the location precision by calculating the ratio of the video frames whose distance between the center of the ground truth and the center of the predicted bounding box exceeds a given threshold. The larger the Precision, the more accurate the tracker. Typically, the threshold distance is set to 20 pixels.  
As shown in Table 1, among these advanced algorithms, DBA-Siam achieves top Accuracy. DBA-Siam achieves a Precision score of 0.792, which is close to the best score of DaSiamRPN, while outperforming the DaSiamRPN on Accuracy with a gain of 3%. Although the two methods have similar performance results, DBA-Siam needs no training negative instances from other fields, which saves training expenses. In addition, DBA-Siam surpasses the algorithm ARCF that is proposed for UAV images, with 28.5% and 18.2% improvement on Accuracy and Precision, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Accuracy**\(\uparrow\) & **Precision**\(\uparrow\) \\ \hline SiamFC [9] & 0.447 & 0.681 \\ ARCF [31] & 0.470 & 0.670 \\ STAPLE\_CA [32] & 0.425 & 0.597 \\ STRCF [33] & 0.457 & 0.627 \\ CCOT [34] & 0.409 & 0.659 \\ CFNet [35] & 0.428 & 0.680 \\ SiamRPN [12] & 0.527 & 0.748 \\ ECO [36] & 0.525 & 0.741 \\ ECOhc [36] & 0.472 & 0.660 \\ DaSiamRPN [11] & 0.586 & 0.796 \\ Ours & 0.604 & 0.792 \\ \hline \end{tabular}
\end{table}
Table 1: Results on UAV123 benchmark. Red fonts indicate the top tracker. \(\uparrow\) indicates that the larger the parameter value, the better the tracker performance.
# Technological Advancement in Tower-Based Canopy Reflectance Monitoring: The AMSPEC-III System
###### Abstract  
Understanding plant photosynthesis, or Gross Primary Production (GPP), is a crucial aspect of quantifying the terrestrial carbon cycle. Remote sensing approaches, in particular multi-angular spectroscopy, have proven successful for studying relationships between canopy-reflectance and plant-physiology processes, thus providing a mechanism to scale up. However, many different instrumentation designs exist and few cross-comparisons have been undertaken. This paper discusses the design evolution of the Automated Multiangular SPectro-radiometer for Estimation of Canopy reflectance (AMSPEC) series of instruments. Specifically, we assess the performance of the PP-Systems Unispec-DC and Ocean Optics JAZ-COMBO spectro-radiometers installed on an updated, tower-based AMSPEC-III system. We demonstrate the interoperability of these spectro-radiometers, and the results obtained suggest that JAZ-COMBO can successfully be used to substitute more expensive measurement units for detecting and investigating photosynthesis and canopy spectra. We demonstrate close correlations between JAZ-COMBO and Unispec-DC measured canopy radiance (\(0.75\leq\mathrm{R}^{2}\leq 0.85\)) and solar irradiance (\(0.95\leq\mathrm{R}^{2}\leq 0.96\)) over a three month time span. We also demonstrate close agreement between the bi-directional distribution functions obtained from each instrument. We conclude that cost effective alternatives may allow a network of AMSPEC-III systems to simultaneously monitor various vegetation types in different ecosystems. This will allow to scale and improve our understanding of the interactions between vegetation physiology and spectral characteristics, calibrate broad-scale observations to stand-level measurements, and ultimately lead to improved understanding of changing vegetation spectral features from satellite.  
gross primary production; photosynthesis; light use efficiency; remote sensing; PRI; multi-angle spectroscopy; spectro-radiometer +
Footnote †: journal: Sensors
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 6 Datasets and Tools
### Tools  
In this part, some widely adopted network simulators and software libraries are summarized to help readers implement network simulations and prototype new models. These tools are summarized in Table 7.  
Discrete-event packet-level network simulators are widely used in the literature for networking research with the purpose of simulating various real-world networks and evaluating different routing algorithms. Popular packet-level network simulators include ns-3, QualNet and OMNeT++ [133]. With the development of the SDN concept, more network simulators have been developed to facilitate integration with SDN protocols and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Topology** & **Type** & **Node** & **Link** & **Traffic Matrices** \\ \hline Abilene & Real & 12 & 30 & 48,096 \\ \hline CERNET & Real & 14 & 32 & 9999 \\ \hline GEANT & Real & 23 & 74 & 10,769 \\ \hline Nobel-Germany & Real & 17 & 52 & 288 \\ \hline Germany50 & Real & 50 & 176 & 288 \\ \hline EBONE (Europe) & Synthetic & 23 & 76 & 100 \\ \hline Sprintlink (US) & Synthetic & 44 & 166 & 100 \\ \hline Tiscali (Europe) & Synthetic & 49 & 172 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of network traffic matrices. tools, such as Mininet. Some network simulators have been developed with embedded reinforcement learning, making it convenient to implement RL-based routing algorithms, for example, PRISMA [134], which is a packet routing simulator for developing multi-agent reinforcement learning algorithms for the distributed routing problem. Some software libraries are available for the implementation of ML and DL algorithms, most of which are based on Python, for example, scikit-learn, TensorFlow, and PyTorch. They have been widely used for data preprocessing, model implementation and performance evaluation [135; 136]. Based on general DL libraries (e.g., TensorFlow and PyTorch), some GNN software libraries have been developed for the rapid development and training of GNN models, e.g., Deep Graph Library (DGL), PyTorch Geometric (PyG) and Spektral. IGNNITION [137] is another framework designed for fast prototyping of GNNs in communication networks and has been used in [19] to implement two example use cases: RouteNet [30] for performance evaluation in wired networks and WCGCN [31] for radio resource management in wireless networks. Finally, a collection of open-source routing algorithms is summarized in Table 8, which can be implemented as baselines in follow-up studies.
## 3 Autonomous Collision Avoidance Method  
### Collision Risk Index  
The CRI [32], is a physical quantity for evaluating the risk of a ship collision, used to determine the severity of the collision threat between ships, especially in the multi-ship encounter scenario. The two elements of the CRI are the time of collision risk index (TCRI) and the space of collision risk index (SCRI). The SCRI is the likelihood of two ships colliding in space, whereas the TCRI is the urgency of collision avoidance measures which must be taken by the two ships at risk of collision. The range of CRI is from 0 to 1, where the risk of collision is increasing with the value. The SCRI can be determined as follows:  
\[u_{st}=\begin{cases}1,\exists(x,y)^{t}\in Domain^{t}\cap\ t\in[0,TCPA]\\ 0,\forall(x,y)^{t}\
otin Domain^{t}\cap\ t\in[0,TCPA]\end{cases} \tag{13}\]  
where \(u_{st}\) denotes the function of SCRI and \(\left(x,y\right)^{t}\) denotes the position of TS at \(t\). The \(Domain^{t}\) denotes the ship domain of OS at \(t\). The expression of TCRI is given as follows:  
\[u_{tT}=\begin{cases}1,&TCS\leq 0\\ (1-\frac{TCS}{I_{0}})^{3,03},&0<TCS<t_{0}\\ 0,&TCS\geq t_{0}\end{cases} \tag{14}\]  
where \(u_{tT}\) means the function of TCRI. Time to the close-quarters situation (TCS) is the duration from the current moment to the first time-in-point of the close-quarters situation (FTCS), whereas the \(t_{0}\) is the duration from the first time-in-point of collision risk (FTCR) to FTCS. When the OS takes the most effective collision avoidance action to make other ships merely pass on the edge of the ship domain is referred to as the FTCS. Regardless of the OS's actions, if this time elapses, the TS will enter the ship domain of the OS. Thus, the CRI model can be established by combining the TCRI and SCRI as follows:  
\[u_{T}=u_{st}\cdot u_{tT} \tag{15}\]
# A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
## 5 Conclusions  
This study addresses critical challenges in the domain of 3D hand pose estimation, specifically focusing on discrepancies in root joint positions and variability in biomechanical features of training samples. The proposed concept of HPBM proves to be an effective method in overcoming these challenges.  
Through the normalization strategy, significant improvements in accuracy have been achieved, evidenced by evaluations on the RHD and STB datasets. Specifically, two benchmark models, VPose3D and ST-GCN, were assessed across both normalized and non-normalized datasets. The findings reveal a noteworthy decrease in PJ errors. When using GT 2D keypoints, substantial reductions of 45.1% and 43.4% in errors are evident for the STB and RHD datasets, respectively.  
The static biomechanical features of the estimated pose predicted by the model trained on normalized datasets also show more accurate results. On the STB dataset, the \(\{\mathbf{e}_{\ell},\mathbf{e_{\text{CTS}}},\mathbf{e_{\text{ARB}}}\}\) are reduced from 71.05, 0.245, 0.818 to 8.43, 0.0001, 0.036, respectively, indicating a significant improvement in hand structure accuracy for the target individual.  
Moreover, the deployment of a weakly supervised methodology is facilitated by the HPBM, allowing for dataset expansion and improved estimation capabilities. Evaluation results of the model trained on \(\mathcal{D}\) show that even without manual labeling, using MediaPipe 2D keypoints as input, the model obtains a PJ of 11.29, which is competitive with other advanced methods. Joint training with dataset \(\mathcal{D}_{n}\) yields even more accurate estimation results, further reducing from 11.29 to 10.03.  
The rigorous experiments reveal a substantial enhancement in model performance compared to baseline methods. It demonstrates that the proposed normalization strategy is effective in addressing existing non-normalized problems and enhancing the performance of the model. While this study encodes hand poses from a biomechanical perspective, it does not assess their validity within human biomechanical constraints. This aspect warrants further investigation in future research.  
Conceptualization, [PERSON]; methodology, [PERSON]; software, J.L.; validation, [PERSON] and J.L.; investigation, J.T.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under Grant 2018 YFC2001601 and in part by the National Natural Science Foundation of China under Grant U1913205 and Grant 62103180.  
Figure 7: Comparative analysis with state-of-the-art methods [13; 31; 35; 45; 46; 47] on the RHD dataset. The \(x\)-axis represents the threshold, while the \(y\)-axis denotes the PCK at the corresponding threshold.
# Rock Mass Structure Classification of Caves Based on the 3D Rock Block Index
## 4 Probability Model of Joint Parameters
### Probability Model of Joint Diameter
When the trace length distribution function follows the standard normal distribution, the probability density function is  
\[h(L)=\mu[1-\int_{0}^{L}f(L)dL] \tag{2}\]  
for which:  
\[f(L)=\frac{1}{\sqrt{2\pi\sigma}}e^{-\frac{[L-\frac{1}{2}]^{2}}{2\sigma^{2}}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Section**} & \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Dip Direction**} & \multicolumn{2}{c}{**Dip Angle**} & **Diameter** & **Linear** & **Volume** \\ \cline{3-10}  & & **Expectant** & **Standard** & **Expectant** & **Standard** & **Expectant** & **Density** & **Density** \\  & & **(\({}^{\circ}\))** & **Deviation** & **(\({}^{\circ}\))** & **Deviation** & **(m)** & **(Strip/m)** & **(Strip/m\({}^{3}\))** \\ \hline \multirow{4}{*}{**3**} & 1 & 257.6 & 5.10 & 74.2 & 6.59 & 2.19 & 0.86 & 0.26 \\  & 2 & 24.3 & 7.17 & 74.9 & 6.27 & 2.73 & 1.02 & 0.20 \\  & 3 & 195.0 & 4.97 & 63.3 & 5.67 & 1.50 & 0.36 & 0.23 \\  & 4 & 86.3 & 5.41 & 81.2 & 5.59 & 1.05 & 0.56 & 0.74 \\  & 5 & 340.7 & 8.77 & 70.6 & 7.73 & 4.90 & 0.26 & 0.15 \\ \hline \multirow{4}{*}{**4**} & 1 & 274.6 & 6.82 & 71.5 & 5.83 & 2.89 & 0.86 & 0.15 \\  & 2 & 31.0 & 1.57 & 70.8 & 5.99 & 1.67 & 0.14 & 0.07 \\  & 3 & 179.3 & 5.53 & 77.6 & 5.94 & 2.34 & 0.40 & 0.10 \\  & 4 & 95.8 & 8.55 & 73.2 & 6.37 & 0.68 & 0.28 & 0.88 \\  & 5 & 346.1 & 5.64 & 59.9 & 6.23 & 3.67 &where \(L\) is the trace length of the joint; \(\mu\) is the center point density of the joint trace, \(\mu=1/\overline{L}\); \(\overline{L}\) is the average trace length of the joint; \(\sigma^{2}\) is the variance of the overall distribution of joint trace length; and \(f(L)\) is the trace length probability distribution function. Assuming that joint diameter \(D\) follows \(f_{D}(D)\) distribution, then:  
\[f_{D}(D)=\frac{\pi}{4}f\Big{(}\frac{\pi}{4}D\Big{)} \tag{4}\]  
Then, the average joint diameter \(\overline{D}\) can be expressed as follows:  
\[\overline{D}=\int_{0}^{\infty}Df_{D}(D)dD=\pi\int_{0}^{\infty}Df\Big{(}\frac{ \pi}{4}D\Big{)}dD \tag{5}\]  
According to the above analysis, the trace length probability density function of different groups of each homogeneous differentiation section is brought into Formula (5) to calculate the joint diameter and determine the probability model parameters of each group of joint diameters (see Table 3).
###### Abstract  
With the development of machine learning in recent years, the application of machine learning to machine fault diagnosis has become increasingly popular. Applying traditional feature extraction methods for complex systems will weaken the characterization capacity of features, which are not conducive to subsequent classification work. A reciprocating compressor is a complex system. In order to improve the fault diagnosis accuracy of complex systems, this paper does not use traditional fault diagnosis methods and applies deep convolutional neural networks (CNNs) to process this nonlinear and non-stationary fault signal. The valve fault data is obtained from the reciprocating compressor test bench of the Daqing Natural Gas Company. Firstly, the single-channel vibration signal is collected on the reciprocating compressor and the one-dimensional CNN (1-D CNN) is used for fault diagnosis and compared with the traditional model to verify the effectiveness of the 1-D CNN. Next, the collected eight channels signals (three channels of vibration signals, four channels of pressure signals, one channel key phase signal) are applied by 1-D CNN and 2-D CNN for fault diagnosis to verify the CNN that it is still suitable for multi-channel signal processing. Finally, further study on the influence of the input of different channel signal combinations on the model diagnosis accuracy is carried out. Experiments show that the seven-channel signal (three-channel vibration signal, four-channel pressure signal) with the key phase signal removed has the highest diagnostic accuracy in the 2-D CNN. Therefore, proper deletion of useless channels can not only speed up network operations but also improve diagnosis accuracy.  
reciprocating compressor; fault diagnosis; deep learning; CNN 2020 10, 6596; doi:10.3390/appj10186596
# Inertial-Navigation-Aided Single-Satellite Highly Dynamic Positioning Algorithm
## 4 Simulation and Result Analysis
### Real-Environment Testing  
In the real-environment test, we used a four-rotor unmanned aerial vehicle that was produced by the Dji company and a Hongyan satellite-enhancement system that provides a single-satellite navigation and positioning service to achieve real testing; testing time was 30 min.The test site was in the village of Dongda of the city of Xi'an which is shown in Figure 10. The blue line is the flight route of unmanned aerial vehicle, the red point is the ideal path passing point which obtained by GPS and wireless beacon, the real-environment test results are shown in Figure 11.  
Figure 8: Our algorithm error of the \(x,y,z\) axis under a comprehensive environment.  
Figure 9: Performance comparison of different algorithms under a comprehensive environment.  
Figure 11 shows the real-environment testing results for the different algorithms. The average error of our proposed algorithm, obtained by 100 independent repeated calculations, was only 46.6% of that of the EKF algorithm and far superior to LS algorithm.The actual test results were close to the comprehensive simulation results. The test results show that our proposed single-satellite positioning algorithm based on UKF can effectively eliminate orbit, satellite-velocity and pseudorange errors and that it significantly improves the accuracy of single-satellite dynamic positioning.
## 2 Materials and Methods
### Multi-Layer Formation Test  
To determine the optimal scan-line spacing, a multi-layer formation test (Figure 2) was performed. The powder layering was repeated 10 times to obtain a 3D object, using the optimal laser power and scan rate that had been determined using the single-line formation test. The scan-line spacing was set as 20, 60, 100, 150, and 200 \(\upmu\)m. The powder was deposited within an area measuring 5 mm \(\times\) 5 mm. The powder layer thickness was 50 \(\upmu\)m. Each layer was built with the laser scanning along a specific direction to reduce anisotropy [14]. Layer-by-layer the scanning direction was rotated by 90\({}^{\circ}\) with respect to the previous one. The effect of the scan-line spacing on the shape of the deposited layer was determined using the overlap ratio, as follows [15]:  
\[\text{Overlap ratio (\%)}=(1-\text{scan-line spacing ($\upmu$m)}/\text{deposited width ($\upmu$m)})\times 100 \tag{6}\]  
The surfaces were examined by scanning electron microscopy (SEM, JSM-6700F, Jeol, Tokyo, Japan). In addition, the 3D-surface profiles were obtained using phase-shift interferometry (Biolin Scientific Oy, Espoo, Finland). The average surface roughness, \(R_{\text{a}}\), was determined from the images. The Gaussian cutoff was 80 \(\upmu\)m. The \(R_{\text{a}}\) of each specimen was recorded using the average value of five readings.  
Figure 1: Schematic illustration of the single-line formation test.
# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 2 Methods
### Navigation Based on Multiple Travel Modes and Predicted Pedestrians' Positions  
Due to changes in the distribution of pedestrians, the traffic situations in the area where they are located will change. To generate a safe and efficient traffic trajectory, the robot needs to formulate a corresponding traffic strategy according to the traffic state in the detection area. The detection area of the traffic state (DATS) is set to be a sector area with radius 5 m and circumferential angle \(90^{\circ}\) in front of the robot. The predicted position of pedestrians in this area based on the improved socially conscious model will be considered when planning the trajectory.  
The traffic states are divided into the five following categories according to the crowd density from low to high: Completely smooth, smooth, walkable, slow-moving, and congested. To guide the robot to find the optimal navigation strategy, five travel modes are defined, as follows: Free mode, high-speed-follow mode, travelable mode, low-speed-follow mode, and probing mode, as shown in Table 1. The priority of the five traffic states in DATS is sorted from high to low. If a state with a higher priority is detected, the robot preferentially enters the corresponding travel mode. In Section 2.3.1, we present the follow target detection and follow mode switching method for smooth and slow-moving states; in Section 2.3.2, the trajectory planning strategies for completely smooth and congested states are described. The trajectory detection and optimization for a travelable state is elaborated in in Section 2.2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Distribution of Pedestrians in DATS** & **Traffic State** & **Travel Modes** \\ \hline No pedestrians & completely smooth & free mode \\ \hline There are reachable pedestrians approaching the goal & smooth & high-speed-follow mode \\ point at a high speed & & \\ \hline A travelable trajectory exists & travelable & travelable mode \\ \hline There are reachable pedestrians approaching the goal & slow-moving & low-speed-follow mode \\ point at a low speed & & \\ \hline There is no feasible trajectory and no target can & congested & probing mode \\ be followed & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Travel modes for different traffic states.
## 2 Materials and Methods
### Image Processing Methods  
#### 2.2.1 Types of Concave Defects and Adhesion Targets  
As shown in Figure 3, a grayscale image I (a) of the adhesion target area is obtained. The binary image bw (a1) is accepted by the binarizing image I (a) by threshold segmentation, and the threshold is the minimum gray value of the background pixel. The minimum circumscribed convex hull of the image bw (a1) is calculated and filled. The filled pixel value is one so that the convex hull binary image bw1 (a2) is obtained. Subtracting the binary image bw1 (a2) from the binary image bw (a1) obtains concave defect binary image bw2 (a3). Then, a small white area is deleted from bw2 (a3) with a threshold of 50 pixels, and an image bw3 (a4) is obtained. The area distribution of concave defect areas is shown in Figure 4. Set that area threshold of the concave defect pixel to 50, and 99% of concave defect areas can be retained.  
Figure 4: Area distribution of concave defect pixel.  
Figure 3: Types of adhesion and concave defects: (**a**) Open-loop adhesion (OLA), (**a1**) Binary image, (**a2**) Convex hull binary image, (**a3**) Concave defect binary image, (**a4**) Binary image of concave defect after deleting small area. (**b**) Closed-loop adhesion (CLA), (**b1–b4**) Same as (**a1–a4**), (**c**) OLA, and CLA.  
As shown in Figure 3, concave defects are defined as two types:  
1. Concave defects at the edge of the convex hull (CDECH). Close to the edge of the bw (a1).
2. Concave defect at the edge of the non-convex hull (CDENCH). Not close to the edge of the bw (a1).  
Therefore, the concave defects in (a4) are CDECH. In (b4), the red dotted line box selects concave defects as CDENCH, and the rest are CDECH.  
Furthermore, all adhesion forms can be defined as two types:  
1. If the concave defects in the adhesion target are all CDECH, the adhesion is OLA.
2. If there is a CDENCH in the adhesion target, the adhesion is CLA.  
Figure 3c reflects that the blue and red dotted lines indicating OLA and CLA, respectively.