esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Kinematic Assessment of Movement Performance
### Inertial Measurement Unit--IMU
#### 5.1.2 APDM by Opal  
In the field of clinical applications, the Opal system by APDM (Amulatory Parkinson's Disease Monitoring) Wearable Technologies Inc. is a popular generation of IMU systems in the clinical sports path due to capturing dynamic movements of the athletes or patients thanks to the accelerometer and stable gyroscope inside the device.  
In [149], the reliability of the APDM device is controlled by the MoCap system as a gold standard by focusing on the lower extremity by measuring the angle of the hip, trunk, knee, and ankle during specific movements. It has been employed as a digital biomarker to collect, measure, and quantify biological aspects for athletes and patients during rehabilitation or exercise. For example, in [150; 151], the Opal system was used in clinical trials of Parkinson's patients attached to their feet and lumbar region to record their daily physical activities. Opal is a digital biomarker that can collect, measure, and quantify biological aspects for athletes and patients during rehabilitation or exercise. In [150; 151], Opal contains a magnetometer and a 3D accelerometer. The 3D gyroscope was used in clinical trials of Parkinson's patients attached to their feet and lumbar region to record their daily physical activities; the outcomes measured mobility and logistic regression employed in the process of health control.  
In [152; 100], to analyze the upper arm propulsion in crawl swimming sport, the upper limb kinematic was monitored to observe 3D of the wrist's trajectory via Opal and a stereo photogrammetric system and by collecting some variables such as velocity and arm coordination. In this research, the lightweight Opal device as the IMU system was built by five units, including a 3D accelerometer and gyroscope (\(\pm\)2000 \({}^{\circ}\)/s), a 3D magnetometer (\(\pm\)6 gauss), and 128 Hz.
## 3 Controller Design
### Path Guidance  
As shown in Figure 4, set \((x_{1}^{n},y_{1}^{n}),\cdots,(x_{n}^{n},y_{n}^{n})\) as the planned waypoint sequence and adopt a straight segment and acceptance circle switching mechanism--that is, when the vessel enters the acceptance circle, it switches to the next straight segment. The switching condition is the following:  
\[\left(x_{i+1}^{n}-x^{n}\right)^{2}+\left(y_{i+1}^{n}-y^{n}\right)^{2}\leq R_{ i+1}^{2} \tag{45}\]  
where \((x^{n},y^{n})\) is the current vessel position, \((x_{i+1}^{n},y_{i+1}^{n})\) is the current tracking waypoint, and \(R_{i+1}\) is the radius of the acceptance circle.  
The path guidance module adopts the integral line-of-sight guidance law [15], which can be expressed as  
\[\psi_{\rm d}=\pi_{\rm p}-\arctan(K_{\rm fplos}y_{\rm e}^{p}+K_{\rm los}y_{\rm int }^{p}) \tag{46}\]  
\[\dot{y}_{\rm int}^{p}=\frac{\Delta y_{\rm e}^{p}}{\Delta^{2}+\left(y_{\rm e}^ {p}+\kappa y_{\rm int}^{p}\right)^{2}} \tag{47}\]  
\[K_{\rm plos}=1/\Delta \tag{48}\]  
Figure 4: Circle of acceptance.  
\[K_{\text{l}\text{o}s}=\kappa K_{\text{p}\text{l}\text{o}s} \tag{49}\]  
where \(\kappa>0\) is the design parameter, \(\pi_{\text{p}}\) is the angle between the expected straight path and north direction, \(y_{\text{e}}^{\text{p}}\) is the cross-track error, and \(\Delta\) is the forward-looking distance, as shown in Figure 5.  
Additionally, in the event of guidance signal loss, the heading controller will maintain the current heading, and the path-following mode will degrade to a heading-keeping mode.
## 4 Instrument Design and Key Technologies
### EMIS and TSI Make Use of the Space Cryogenic Absolute Radiometer (SCAR)
#### 4.1.1 Earth-Moon Imaging Spectrometer  
Reflected solar spectrum radiance is measured by the Earth-Moon Imaging Spectrometer (EMIS). The optical design of the EMIS consists of a telescope and a hyperspectral imaging spectrometer. The telescope utilizes a four-mirror anastigmat (4 MA) to eliminate aberrations. The hyperspectral imaging spectrometer is based on an Offner design. A prism is used as the dispersion element. The telescope uses the image space telecentric structure to achieve matching conditions with the pupil of the spectrometer, which uses a telecentric design. By setting baffles at the intermediate image plane, the influence of stray light is reduced. The EMIS makes a trade-off between the spectral and spatial resolution, with spectral and spatial sampling better than 10 nm and 100 m, respectively. The swath width is about 50 km at nadir from a 600 km orbit. The EMIS selects lens parameters, such as thickness, curvature, etc., to achieve lower dispersion non-linearities and better spectral performance.  
The radiometric scale of the EMIS can be traced to SCAR. The measurement accuracy and long-term stability of the EMIS can be improved by the onboard hyperspectral calibration shown in Figure 3 and consists of the SCAR and Benchmark Transfer Chain (BTC). The SCAR will realize a long-term stable and highly accurate radiation measurement. The light power benchmark of SCAR is converted to a radiance benchmark by the Transfer Radiometer (TR) and multiple laser diodes. The multi-spectral calibration of the EMIS is realized by observing the reflected light of a diffuser. Based on the hyperspectral curve reconstruction technique, the EMIS is full-spectral calibrated by the TR and the tungsten halogen lamp inside an integrating sphere. In order to ascertain linearity over the large dynamic range, the Sun is used as the light source, with solar attenuations provided by attenuators.  
Figure 2: The design chart of Earth-Moon Imaging Spectrometer (EMIS) and Total Solar Irradiance (TSI).
# Status of Pure Electric Vehicle Power Train Technology and Future Prospects
## 4 Electric Propulsion
### Permanent Magnet Brushless DC Motor
#### 4.3.2 Application of PM BLDC Motor in Electric Vehicle  
PM BLDC motor has been the primary choice for use in electric vehicle applications. We can find most of the in-wheel hub BLDC motors in two-wheeler, fixed gear drive three-wheeler, and electric vehicle conversion kits. These days, BLDC motors are mostly commonly used in two-wheeler and three-wheeler vehicles.  
There are two different kind of BLDC motors:  
Out-Runner Type BLDC Motor  
In out-runner BLDC motors, the position of the rotor is outside while that of stator is inside. They are also known as 'hub motors' as their wheel is directly coupled to the exterior rotor. External gear systems have no presence, while some offer a planetary gear system. As the motor is directly coupled to the rotor, there is no need of space for mounting the motors. These types are mostly common in electric bicycle, scooters, and in-wheel drive electric vehicles.  
In-Runner Type BLDC Motor  
In-runner BLDC motors come with the opposite configuration of out-runner BLDC by placing the rotor inside and stator outside. In order to transfer the power to the wheels, these systems require external transmission systems like that of fixed gear or chain drives.
## 3 Experiment Results and Analysis
### Experimental Results and Comparison Analysis
#### 3.3.1. _Taxis GPS Data Set_
After the tenth generation, the SSE value of the proposed algorithm is slightly smaller than that of the K-means++ and GAK algorithms. In Figure 11, proposed converges from about the second generation and converges entirely in the fifth generation. The SSE value is lower than that of other algorithms, showing that the clustering effect is excellent and the separation between clusters is significant, consequently demonstrating the algorithm's effectiveness. In Figure 12, the proposed converges from the second generation and entirely at about the fifth generation, with a very smooth convergence curve. In the meantime, the figure demonstrates that the SSE value is less than that of other algorithms, indicating that the clustering center captured by the proposed algorithm is effective and does not cause k-means clustering to fall into local optimal solutions. In Figure 13, the proposed converges from about the second generation and ultimately converges at the fifth generation. In the beginning stage, the SSE value of the algorithm is substantially lower than that of other algorithms, and after convergence, it is extremely stable. Overall, experiments on various datasets indicate that the algorithm that combines the genetic algorithm, fussy system, and particle swarm algorithm virtually captures the optimum clustering centers more effectively.
## 2 FM-ACSK Communication System
### Decision-Making Device (Bit Detector)  
The decision-making device uses the \(\Delta\)sync signal to decide the values of the received bits using the timing obtained by the symbol phase synchronization module. As Figure 10 shows, the \(\Delta\)sync signal (\"err_delta_in\") is integrated across the length of the whole symbol, according to the retrieved bit timing (\"sync_clk_in\"). The accuracy of the integration is of low importance here, as only the sign of the result is used for decision making. Positive integration results return bit \"1\", negative--bit \"0\".  
Figure 9: Simulink schematic of the designed early–late gate symbol phase synchronizer. Colored tags indicate the implied connections of the signals. The arrows at the clock inputs indicate which clock edges serve as a trigger condition.
# 3D Model Identification Using Weighted Implicit Shape Representation and Panoramic View
## 6 Conclusions  
In this paper, we have proposed a 3D model identification method, which consists of WISR-based 3D shape normalization and panoramic view for feature extraction. To achieve high precision of 3D model identification with 2D view-based approach, how to accurately normalize the shapes of the models has great significance in practice. The proposed 3D shape normalization clusters random points inside a model and defines the number of nearest neighbors within a specified radius from each cluster center as the weight. The weight is applied to ISR to produce WISR for reducing the influence caused by shape deformation and partial removal. A panoramic view is generated by projecting a 3D model onto the surface of a cylinder for extracting SIFT descriptors. The average feature size and matching time are 4.1 KB and 1.9 s. The precision of identification of original models is 92% and that of removed versions is 64%. The experimental results show the performance of the 3D model identification is significantly improved. In the future work, we will optimize the identification method and increase the precision of the identification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **PCAS** & **CPCA** & **DODE** & **Proposed** \\ \hline Size (KB) & 19.4 & 36 & 110.1 & 4.1 \\ Time (s) & 26.4 & 80.3 & 223.5 & 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average feature size and matching time; PCAS: combination of PCA and SIFT; CPCA: combination of continuous PCA, normal PCA, 2D discrete Fourier transform, and 2D discrete wavelet transform; DODE: combination of dodecahedron and SIFT.  
Figure 5: Precision of 3D model identification.  
**Acknowledgments:** This research project was supported by Ministry of Science, ICT and Future Planning in 2016.  
**Author Contributions:** Both authors contributed to the research work. Both authors designed the new method and planned the experiments. [PERSON] led and reviewed the research work. [PERSON] performed the experiments and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 4 Design and Implementation
### Main-Parsing Module
#### 4.2.5 Filtering  
The final step is to filter the packet types to eliminate duplicate packet types and invalid packet types. Duplication of packet types may occur because the same path may occur in the branch structure. For example, in the \"if/else if/else\" structure, if no field information is extracted under the \"if\", \"else if\" or \"else\" branch, that is, the branch does not generate a new packet type, then it will be expanding into three basic paths which will result in basic path duplication. In addition, the validity of the packet types is judged based on the dependencies of the extracted fields. If the dependencies of a certain field in the packet type are inconsistent or the dependencies between multiple fields are inconsistent, it means that the packet type is the wrong packet type and should be filtered.
## 4 Results and Discussion
### Network Topology Feature Analysis
In the developed interaction network of seafarers' unsafe acts and their influencing factors, the weighted clustering coefficient is used to reflect the aggregation of seafarers' unsafe acts and their influencing factors, as shown in Figure 7.  
The weighted clustering coefficient of lack of route plan review (S1), failure to correct mistakes (S8), device failure (E1), failure to perform safety duties during berthing (U6), drinking/alcoholism (U7), violation of operational procedures (U9), failure to exhibit proper light and shape (U15), and failure to follow the rules in restricted visibility (U28) is 1. The results indicate that once these seafarers' unsafe acts or influencing factors change in the network, they are very likely to lead to chain reactions or even cause larger-scale catastrophes in the system, so it is necessary to strengthen the pre-control of these seafarers' unsafe acts and influencing factors to reduce the chain reactions brought by them. The average weighted clustering coefficient of the interaction network is the average of the clustering coefficient of all the seafarers' unsafe acts and their influencing factors, and its value is 0.6203, which indicates that the nodes in the network tend to surround the hub nodes with high clustering coefficient, and the risky interaction could be generated through fewer edges. In addition, the unweighted network clustering coefficient is 0.6042, which indicates that the relationship between seafarers' unsafe acts and influencing factors in the network tends to be more clustered based on the effect of weighting. (4) Betweenness  
Betweenness is the number of times all shortest paths in the network pass through the node. In the developed interaction network of seafarers' unsafe acts and their influencing factors, the betweenness can reflect the mediator role played by seafarers' unsafe acts and their influencing factors in the risk propagation process. The higher the betweenness value of a node, the greater the role of the node as a mediator in the network. The betweenness centrality of nodes refers to the normalized betweenness of nodes, and the results are shown in Figure 8.  
There are 23 nodes in the interaction network with a betweenness centrality of 0, indicating that these 23 nodes do not act as a mediator in the network. Lack of supervision and guidance (S2) has the largest value of betweenness centrality, about 0.0370, which indicates that the number of shortest paths through this node in the network is the largest. This is followed by the lack of safety management (S4) and insufficient manning on bridge (U4), with betweenness centrality values of 0.0230 and 0.0204, respectively. Figure 7: Clustering coefficient of the nodes. In the interaction network, preventing the appearance of these seafarers' unsafe acts and influencing factors would effectively increase the average path length and diameter of the network, reduce the connectivity between seafarers' unsafe acts and influencing factors, and help reduce the risk propagation rate.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 5 Conclusions  
Current methods to map land use in Queensland are based on manual image interpretation, which are time- and resource-intensive. Land use information is fundamental for informing the response to biosecurity incidents, such as the detection of Panama TR4 in the Tully River catchment. Advances in big data and imagery availability have created an opportunity to develop methods to automatically and efficiently classify land use features over large geographical areas. This allows for higher spatial and temporal resolutions, and a more detailed classification for commodity-level observations.  
In this paper we have presented an automated and efficient classification technique for detecting the location and extent of banana plantations in the Wet Tropics, which we have shown is an improvement on the existing mapping methodology. The new classification approach used a refined version of the existing QLUMP mapping to train a CNN using the U-Net architecture [33].  
Conceptualization, A.C.; methodology, A.C.; software, A.C.; validation, A.C.; formal analysis, A.C.; investigation, A.C. and J.M.; data curation, A.C.; writing--original draft preparation, A.C. and J.M.; writing--review and editing, A.C. and J.M.; visualization, A.C. and J.M.; supervision, A.C.; project administration, A.C. and J.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank [PERSON] for introducing us to the U-Net architecture. The authors acknowledge [PERSON] (Joint Remote Sensing Research Program) who contributed to this research by providing advice on the accuracy assessment. The authors also appreciate the reviewers' constructive feedback and suggestions, namely [PERSON] (Queensland Department of Environment and Science), [PERSON] (The University of Queensland), and [PERSON] (Joint Remote Sensing Research Program). The authors would also like to acknowledge the Queensland Government Department of Environment and Science for providing access to the HPC infrastructure and supporting this research.  
The authors declare no conflict of interest.
# Along-Track Multistatic Synthetic Aperture Radar Formations of Miniatellites
## Appendix A Probabilistic Approach to Matrix Inversion
In Figure A2, a cube is shown corresponding to the distribution of the positions, relative to the transmitter, of three other receivers (the case \(N=4\) is then shown). All the points in the cube have the same probability to occur in order to explore the full space of random positions among the four satellites. The optimal positions of the receivers are indicated with black spheres. There are six, corresponding to the six permutations of the receivers in the positions \(\delta_{m}/4;\delta_{m}/2;3\delta_{m}/4\). The surface identifying the part of the volume of the cube where the determinant of the matrix \(\mathbf{H}^{*}\mathbf{H}\) is between the maximum and \(-10\) dB below (which is just an indicative bound for the ambiguity increasing) is indicated with cyano (\(R=4\)), light-orange (\(R=3\)), or red (\(R=2\)) isosurfaces. The probability of a good conditioning is acceptable only for \(R=2\), i.e., when the reconstructed replicas are consistently lower than the number of sensors. In practice, the figure visually provides a way to evaluate the precision needed for the positioning, which is approximately \(10\%\) of the sampling interval. From the figure, it is possible to appreciate the advantages that can be obtained by letting the antennas to slide along the orbit, or equivalently changing the sampling interval, namely adapting the PRF to the actual positions of the sensors.
## 3 Methodology
### TPL Detetction
#### 3.4.2 TPL Detection Based on Function Features
Firstly, we obtain the candidate reused TPLs and anchor functions based on the vectors including the target binary (line 2-9). For each vector \(q\in V_{B}\) in the target binary \(B\), we find its top-k similar function vectors (line 5). For each similar function vector \(lib_{v}\), we consider the library including it as a candidate library (line 7-8) and take \((q,lib_{v})\) as an anchor function (line 9). Then, we calculate the matching score between \(B\) and a candidate library to judge whether the vector similarity values are greater than the threshold and determine whether this library is reused by the target binary (line 10-18). Then, we determine whether there are multiple versions of a library in the matching result. We first judge the number of a library (regardless of the specific version) exists in the matching result. If the number is greater than one (line 21), we select the version of this library with the highest similarity score (line 22) and take this version as the final matching result (line 23). If there is only one version in the matching result, we simply take the version as the final matching result (line 24-25). The similarity score between \(B\) and a candidate library is the average of the sum of the similarity scores of the anchor functions they contain. ```
1:functionTPL_detection_based_on_function_features(\(V_{B}\)) // get candidate libraries and the anchor functions
2:candidate_matched_libs \(\leftarrow\) []
3:lib_anchor_functions \(\leftarrow\) {}
4:for\(q\) in \(V_{B}\)do
5:similar_lib_vs \(\leftarrow\) Get_top_k_similar_vector(\(q,k\))
6:forlib_v in similar_lib_vsdo
7:lib \(\leftarrow\) Get_libs(\(lib_{v}\))
8:candidate_matched_libs.append(lib)
9:lib_anchor_functions[lib].append([q,lib_v]) //feature match
10:matched_libs \(\leftarrow\) []
11:forlib in candidate_matched_libs do
12:anchor_functions \(\leftarrow\) Get_lib_anchor_functions(\(lib\))
13:foranchor_function in anchor_functions do
14:similarity \(\leftarrow\) Calculate_similarity(\(anchor\_function\))
15:similarity_scores \(\leftarrow\) similarity scores + similarity
16:ave_similarity_scores \(\leftarrow\) Average(\(similarity\_scores\))
17:ifave_similarity_scores \(\geq\) threshold then
18:matched_libs.append(lib)
19:function_matched_libs \(\leftarrow\) []
20:forlib_without_version in matched_libs do
21:iflen(\(lib\_without\_version\)) \(>\) 1 then
22:matched_version \(\leftarrow\) Select_Max_Similarity_Score_Version(\(lib\_name\))
23:function_matched_libs.append(matched_version)
24:iflen(\(lib\_without\_version\)) == 1 then
25:function_matched_libs.append(lib_name_version)
26:return\(function\_matched\_libs\)
```  
**Algorithm 2** TPL detection based on function features