esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Collision Avoidance Model  
### Circular Collision Avoidance Model  
In the process of collision avoidance, to facilitate the calculation, it is necessary to expand the obstacles and establish the motion collision avoidance model of the USV and  
Figure 1: The Lanxin USV at sea.  
obstacles in the coordinate system [36; 37], as shown in Figure 2. The velocity of the USV is \(v_{U}\) and \(Obs\) represents an obstacle with velocity \(v_{O}\). The relative velocity between the USV and the obstacle is \(v_{U\!O}\). \(\alpha=\angle(X,v_{U})\) is the course of the USV, \(\beta=\angle(X,v_{O})\) is the movement direction angle of the obstacle, and \(\varphi=\angle(v_{U\!O},v_{U})\) is the angle between the course of the USV and the direction angle \(v_{U\!O}\). The relative position vector between the USV and obstacle is \(L_{U\!O}\) with the direction angle \(\theta=\angle(X,L_{U\!O})\), \(\gamma=\angle(L_{U\!O},v_{U\!O})\) is the angle between the direction \(v_{U\!O}\) and the direction \(L_{U\!O}\). The collision risk area is determined according to \(L_{U\!O}\) and the tangent line \(L_{t}\) from the USV to the obstacle circle. The angle of the collision risk area is \(\mu=\angle(L_{t},L_{U\!O})\). All angles in Figure 2 are positive in the anticlockwise direction.  
When the direction of relative velocity \(v_{U\!O}\) is within \((\theta-\mu,\theta+\mu)\), it is considered that the USV will collide with the obstacles if it continues to sail. At this time, it is important to adjust the course and velocity of the USV in order to increase the value of \(\gamma\), so as to make the USV escape from the collision domain. In the collision avoidance process of the USV, \(v_{U\!O}\) is decomposed into \(v_{U\!O}^{  
It can be seen from (11) that \(\Delta\gamma\) can be changed by adjusting the velocity variation \(\Delta v_{U}\) and course variation \(\Delta\alpha\) of the USV. In order to ensure that the relative velocity direction is outside the collision domain \((\theta-\mu,\theta+\mu)\), the value of \(\Delta\gamma\) should satisfy  
\[\begin{cases}\Delta\gamma=\dfrac{\sin\varphi}{v_{\text{U}O}}\Delta v_{U}+ \dfrac{v_{U}\cos\varphi}{v_{\text{U}O}}\Delta\alpha\geq\mu-\gamma&\gamma\geq 0 \\ \Delta\gamma=\dfrac{\sin\varphi}{v_{\text{U}O}}\Delta v_{U}+\dfrac{v_{U}\cos \varphi}{v_{\text{U}O}}\Delta\alpha\leq-\mu-\gamma&\gamma<0\end{cases}. \tag{12}\]
## 4 Results  
To validate the effectiveness of the proposed PGD approach, its performance is quantitatively and qualitatively evaluated using the Seahawk waveform dataset, as illustrated in Figure 7. To evaluate its applicability to diverse and complex waveforms, the fitness of PGD is compared with that of CGD, which simply approximates the Gaussian mixture model based on peak detection. In addition, to evaluate the improvement in the seafloor measurement performance of the proposed approach, the extractability and geolocation accuracy of bottom points are assessed after point registration. The bottom point extractability (number of points) of the point cloud generated by PGD is compared with that of the point cloud generated by the LBASSD (Seahawk's processing software). Lastly, the geolocation accuracies of the bottom points extracted using PGD and LBASSD are compared by referring to echo-sounding data.
# Joint Iterative Decoding Design of Cooperative Downlink SCMA Systems
## 7 Conclusions  
This work provides a cooperative downlink SCMA system by using a relay to serve the cell edge users, which works in a decode-and-forward manner. To achieve better BER performance, we propose a joint iterative decoding design for the cooperative downlink SCMA system by means of a joint factor graph, which extends the original indicator matrix to the cooperative one. Moreover, the closed-form BER performance is also derived to enable us to perform analyses on the joint iterative MPA detector. Simulation results demonstrate the superiority of the downlink cooperative SCMA system over the non-cooperative one.  
Conceptualization, H.C., M.Z. and R.S.; methodology, H.C.; writing--original draft preparation, H.C.; writing--review and editing, H.C., M.Z. and R.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under Grants 62301271 and 62401290, the open research fund of National Mobile Communications Research Laboratory, Southeast University (No. 2025D13), and the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications under Grants No. NY222083 and No. NY222082.  
Data are contained within this article.  
The authors declare no conflicts of interest.
# Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter
## 5 Conclusions  
We proposed a graded round-robin SoC estimation method that combines UT transformation and Stirling interpolation center difference. In the first cycle, the Sigma points of the UT transformation were used to approximate the posterior mean value of the state with third-order Taylor accuracy to quickly correct the initial error of the SoC. This was helpful for the estimated SoC to converge to the reference value more quickly. The second cycle adopted the first-order truncated Stirling interpolation to obtain the approximation form of the function and did not need to calculate the Jacobian matrix, which effectively improved the calculation efficiency. By analyzing five evaluation indicators of RMS Error-MAE SI MAPE and VAF, the test results showed that the new algorithm guaranteed an SoC error within 2% under the FUDS and US06 working conditions, and it rapidly converged to the reference value within 60 s when the initial SoC value was inaccurate (\(\pm\)10%). In addition, the new algorithm showed better robustness in three of the four working conditions. Therefore, the proposed method could reduce the time for the SoC estimate value to converge to the reference value and ensure the accuracy and feasibility of lithium-ion battery SoC estimation.  
Conceptualization D.G. and Z.Z.; methodology, D.G. and Z.Z.; software, D.G.; validation, D.G. and Z.Z.; formal analysis, D.G., X.K. and Z.Z.; investigation, D.G.; resources, D.G., Z.W. and Z.Z.; data curation, D.G.; writing--original draft preparation, D.G., X.K. and Z.Z.; writing--review and editing, D.G., X.K. and Z.Z.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.W. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the China Zhejiang Public Welfare Technology Application Research Project under Grant No. LGG19E070006, the National Natural Science Foundation of China under Grant No. 52107226 and the China Postdoctoral Science Foundation under Grant No. 2020 TQ0167.  
Not applicable.  
The authors would like to thank the reviewers for their comments and suggestions.  
The authors declare
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Classification Methods  
The classification performances of the two classification methods (the RF and SVM approaches) were tested in our study based on the spectral and textural characteristics of rubber plantations.  
RFs are machine learning techniques that were first proposed by [PERSON] and [PERSON] in 2001 [48] on the basis of decision tree algorithms. RF algorithms integrate multiple trees through the idea of ensemble learning and use decision trees as the basic units. RF algorithms have many advantages; for example, they have good noise tolerance, do not easily produce excessive fitting phenomena, have the capacity to deal with high-dimensional data, have strong adaptability to different datasets and have no strict demands regarding the forms of prediction variables that can be used (e.g., either quantitative variables or qualitative descriptions can be used). Therefore, RFs have been widely used in ground feature classification studies. In the RF construction process, there are two very important parameters: the number of trees in the forest and the number of random variables at each node. In this work, the number of trees was fixed at 100, and the number of random variables was set to the square root of the number of features.  
The other model used in this study was an SVM provided by [PERSON] and [PERSON] [49] based on the structural risk minimization principle. The success of SVMs is based on two key technologies: (1) designing optimal hyperplanes with maximum spacing through the use of established principles and (2) designing the above linear optimal classification surface in the high-dimensional feature space and obtaining a nonlinear learning algorithm in the input space by using a kernel function. Common kernel functions include linear kernels, polynomial kernels, radial basis function kernels, Laplacian kernels, and sigmoid kernels. In the present study, we adopted a radial basis function kernel.
# A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys
## 2 Materials and Methods
### Sampling and Treatment  
The proposed method was applied to 25 honey samples. According to the botanical origin (certified by qualitative and quantitative pollen analysis and organoleptic tests performed by different laboratories appointed directly by beekeepers), they were classified as chestnut (_Castanea sativa_ Mill.), acacia (_Robinia pseudoacacia_), citrus (_Citrus_ L), eucalyptus, and sulla (_Hedysarum coronarium_) honeys. 8 citrus, 4 chestnut, 4 eucalyptus, 4 acacia, and 5 sulla honeys were supplied directly by local beekeepers. All the samples were obtained through manual processes involving centrifugation and remained unpasteurized. These samples originated from the 2022 production, and all analyses were conducted within the same year. The assessment of the aromatic profile was executed immediately after procuring the honey samples. Subsequently, the samples were stored in a lightless environment at a temperature of 20 \({}^{\circ}\)C until further determinations were carried out. For the determination of the amino acid profile, representative samples from the honey lot were selected, homogenized, and weighed (1 g). These samples were then dissolved in a solution of 5 mL H\({}_{2}\)O: MeOH (80:20) with 0.1% formic acid. The resulting solution was quantitatively transferred to a 10 mL volumetric flask and diluted with the same solution. After vigorous vortexing for 5 min, the solution was subjected to centrifugation at 5000 rpm for 10 min. A 1 mL aliquot of the supernatant was filtered using a 0.22 um filter (Millipore, Bedford, MA, USA) and analyzed using HILIC-UPLC-ESI-MS/MS [32]. For the determination of the aromatic profile, the volatile compounds from the honeys were extracted using the HS-SPME method. A Divinylbenzene/Carboxene/Polydimethylsiloxane (DVB/CAR/PDMS) fiber (Sigma Aldrich Supelco, St. Louis, MO, USA) with dimensions 50/30 um was employed. Before use, the SPME fiber was conditioned in the GC-MS injection port at 250 \({}^{\circ}\)C for 10 min to prevent contamination and carry-over from prior samples. Portions of 3 g of honey were placed within a 15 mL SPME headspace vial along with a magnetic stirrer and 1.5 mL of water. The vial was promptly sealed and heated to 60 \({}^{\circ}\)C for 15 min using a magnetic hot plate stirrer. Following equilibration, the headspace extraction step was performed by inserting the fiber into the vial for 30 min at 60 \({}^{\circ}\)C with a stirring rate of 600 stirs per minute. The extracted analytes were desorbed in the GC-MS for 10 min while maintaining the injection port at 250 \({}^{\circ}\)C. Each sample underwent three separate extractions, with each extraction corresponding to a single SPME analysis.
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 3 Materials and Methods
### The Processing Layer  
The primary datastore for the anomaly detection system is provided by the software company Whysor (Netherlands), who specialises in Internet of Things solutions and BigData architecture. As visualised in the architecture diagram in Figure 1, the Whysor database is synchronised in real time from Digital Matter's cloud using an in-house developed database connector which handles the data transformation. The Whysor datastore was a PostgreSQL database.  
To allow the analytics researchers on the project to perform data analysis on the collected data, a backup data store was created. This served two purposes. Firstly, it allowed the REAMIT project to own the data and give GDPR assurances to pilot companies that their data were being stored on a secure university server dedicated to the project. Secondly, each researcher had their own private instance of the database, allowing them to manipulate the data without affecting the historical records saved to the Whysor DB. The backup database was a Microsoft SQL Server database and was synchronised with the Whysor database in real time using an in-house developed API.  
The SMS alerting system runs in the processing layer and allows the user to build rule-based algorithms to send alerts if particular criteria are met. Further details on the algorithm and parameters chosen will be presented in the next section. The SMS alerting was provided by Amazon's Simple Notification Service (SNS). The algorithm's parameters are configurable from the dashboard, which is part of the application layer.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 3 Mobile Ad Hoc Networks
### Routing in MANETs
#### 3.1.1 Proactive Routing Protocol  
Commonly known as Table-Driven routing, this method involves each node in the network keeping a table of all possible destinations. As the network topology continuously changes, periodic updates are used to maintain these tables. The primary advantage of this routing approach is its ability to reduce end-to-end delay [17].
## 3 Results and Discussions  
### Chemical Structure of GO-H202  
To investigate the chemical structure and constitution of H202, GO and GO-H202, the measurements of XPS, FT-IR, and TGA were performed, as shown in Figure 1. From Figure 1a, the XPS spectra and the fractions of O and C of the samples were calculated, as shown in Table 1.  
Figure 1a and Table 1 reveal that after grafting H202, the O content of GO-H202 (27.7%) is obviously higher than that of GO (16.2%). The O/C ratio, \(n\)(O)/\(n\)(C), of GO-H202 is twice compared to GO, indicating that H202 has been successfully grafted onto GO.  
On the other hand, Figure 1b showing the FT-IR reveals that for hyperbranched polyester H202, the wide peak at 3400 cm\({}^{-1}\) represents the vibration of -OH group, while signals at the wavenumbers of 2918 cm\({}^{-1}\) and 2888 cm\({}^{-1}\) represent the asymmetric and symmetric vibrations of -CH\({}_{3}\) and -CH\({}_{2}\), respectively. The peak at the wavenumber of 1734 cm\({}^{-1}\) is the stretching vibration peak of carbonyl from the polyester [35]. From the FT-IR spectrum of GO-H202, the extra peak at the wavenumber of 1734 cm\({}^{-1}\) can be seen compared with that of neat GO, suggesting that H202 has been successfully grafted onto GO.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **O 1s (\%)** & **C 1s (\%)** & \(n\)**(O)/\(n\)(C)** \\ \hline H202 & 60.5 & 39.5 & 1.53 \\ GO & 16.2 & 83.8 & 0.19 \\ GO-H202 & 27.7 & 72.3 & 0.38 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fractions of C and O of H202, GO, and GO-H202.  
Figure 1: XPS (**a**), FT-IR spectrum (**b**) and TGA curves (**c**) of hyperbranched polyester H202, graphene oxide (GO), and GO-H202.  
The TGA curves of H202, GO, and GO-H202 in Figure 1c show that the degradation of H202 initiates at about 245 \({}^{\circ}\)C and finishes at about 410 \({}^{\circ}\)C. Interestingly, a significant weight loss of GO-H202 is observed at the same temperature range, which cannot be observed from the TGA curve of neat GO, suggesting that the grafting of H202 onto GO is successful. Moreover, the grafting weight ratio of H202 is about 35 wt % of GO.  
To directly observe the morphology of the fillers, TEM was performed, as shown in Figure 2. Clearly, compared with neat GO, many dark dots can be observed from the TEM image of GO-H202, which might be evidence for the successful grafting of H202 onto GO.
## 4 The Polydisperse Drag Model
### Adaptive Advantages of OCRA  
To verify that the OCRA can find the most valuable training samples adaptively under the given number of samples, we first randomly select 1000, 1300, and 1600 samples without OCRA for ANN training. These samples are named random training samples, and then 1000 training samples by using the OCRA, which are named adaptive training samples, are employed to train the ANN. The conditions of the numerical experiment are the same as in Section 4.1. The prediction accuracy and CPU time of different training samples in ANN prediction for \(\varepsilon_{g}=0.35\) are demonstrated in Figure 5 and Table 7.  
As shown in Figure 5, the training error of ANN at \(Re_{p}\in[0.008,1100]\) decreases obviously when the number of training samples increases from 1000 to 1300. However, when the training samples increase from 1300 to 1600, the prediction error of the ANN drag model decreases a little. It can be concluded that the effective samples account for a large proportion in the training samples from 1000 to 1300, so it makes the training error of ANN decrease rapidly. However, in the training samples from 1300 to 1600, the ineffective samples, which are redundant and singular samples, account for the mainstream; these samples have no obvious effect on the improvement of the training accuracy and even reduce the training accuracy of the ANN. The results demonstrate that a large original training sample set will inevitably have some redundant and singular samples, and these  
Figure 4: Dimensionless drag force prediction without OCRA.  
\"unusual samples\" will cause repeat training and even over-fitting problems of the ANN in a certain prediction zone. It still can be inferred from Figure 5 that the prediction accuracy of 1000 samples by using the OCRA is close to that of 1300 random samples, which is much higher than the accuracy of 1000 random samples. Yet, the CPU consumed time for ANN training by 1000 adaptive samples, as shown in Table 7, is 21% less than that of 1300 random training samples. The conclusion can be drawn that the OCRA can find the most valuable training samples for the improvement of training efficiency in the ANN drag model. Simultaneously, a certain number of low-contribution-rate training samples are excluded by this adaptive algorithm. As a matter of fact, the computational efficiency of the ANN drag model can be improved obviously. In general, the OCRA is an effective tool to exclude the redundant and singular samples in ANN training.
## 2 Algorithm
### Double Difference Observation Model in the Pseudolite System  
Based on the above analyses of positioning error sources, the error of clock synchronization and phase center deviation can be eliminated or weakened by the DD model [20]. The DD observation equations [21; 22; 23] can be formulated as:  
\[\begin{array}{l}\
abla\Delta p^{ks}_{br}=\
abla\Delta\rho^{ks}_{br}+\
abla \Delta e^{ks}_{br}+\
abla\Delta M^{ks}_{br}+\
abla\Delta T^{ks}_{br}\\ \
abla\Delta\rho^{ks}_{br}=\
abla\Delta p^{ks}_{br}+\lambda\
abla\Delta N^{ks}_ {br}+\
abla\Delta e^{ks}_{br}+\
abla\Delta m^{ks}_{br}+\
abla\Delta T^{ks}_{br} \end{array} \tag{1}\]  
Figure 1: The procedure of the whole algorithm.  
where \(\
abla\Delta\) is a DD operator; \(r\) and \(b\) represent the rover and base stations, respectively; \(k\) and \(s\) represent pseudolites. The pseudorange and carrier phase observations are represented by \(P\) and \(\phi\); \(\rho\) represents the geometric distance of pseudolites to the receivers; \(e\) and \(\varepsilon\) denote the noise of the pseudorange and carrier phase observation, respectively; and \(T\) represents the tropospheric term. Considering the indoor environment, the height difference is small and the baseline is short, which causes the tropospheric term to have little effect on indoor positioning; \(M\) and \(m\) represent the multipath effects of pseudorange and carrier phase observation; \(N\) indicates the integer ambiguities of carrier phase observations; and \(\lambda\) indicates the wavelength.  
From Equation (1), the structure of the pseudorange and carrier phase observation is similar, with the latter containing an ambiguous term. This structure implies that as long as the ambiguities can be resolved correctly, the carrier phase observation will be transformed to a high-accuracy receiver-pseudolite pseudorange [24].
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 4 Patch Antenna Design  
This section explains the design procedure of the transmission and reception patch antennas. First, the substrate is selected and then its dimensions are calculated. Finally, the antenna feeding point is determined.  
The most important parameters of the substrate are its dielectric constant (\(\varepsilon_{r}\)) and its thickness (\(h\)), which are determined considering the operating frequency (\(f\)). Later, using (4)-(7) [35] the length (\(L\)) and width (\(W\)) of the antenna can be calculated, where \(L_{eff}\) is the effective length, \(\Delta L\) is the length extension, and \(\varepsilon_{eff}\) is the effective dielectric constant of the substrate.  
\[W=\frac{c}{2f}\sqrt{\frac{2}{\varepsilon_{r}+1}} \tag{4}\]  
\[L=L_{eff}-2\Delta L \tag{5}\]  
\[L_{eff}=\frac{c}{2f\sqrt{\varepsilon_{eff}}} \tag{6}\]  
\[\Delta L=0.412h\frac{(\varepsilon_{eff}+0.3)(\frac{W}{h}+0.264)}{(\varepsilon_ {eff}-0.258)(\frac{W}{h}+0.8)} \tag{7}\]  
Next, the feeding point was calculated. In this case, the beamformer chips are placed in a different layer than the patch antenna. For a large patch antenna array, using this feeding approach we can reduce the beamforming. To choose our feeding point, the patch antenna impedance distribution must be considered. As shown in Figure 5, the impedance is at the maximum at the edges and minimum at the centre. By applying (8) and (9) the exact position (\(Xf\) and \(Yf\)) of the 50 \(\Omega\) impedance point to feed the patch antenna can be calculated [35; 36]. Once this point was calculated, it was adjusted by simulations.  
\[X_{f}=\frac{L}{\sqrt{\varepsilon_{eff}}} \tag{8}\]  
\[Y_{f}=\frac{W}{2} \tag{9}\]