esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Mega Constellations on Geospace Safety
## 4 Impact of Starlink on Control Avoidance Strategies for low-orbiting Satellites in Geospace
### Impact on LEO Space Target Satellite Deorbit Capability
Orbital avoidance consumes propellant, and when satellites change orbit more often due to excessive avoidance, the lifetime of the satellite is affected [30; 31].Assuming a lifetime of 5 years for one satellite in the case of ACPL-\(10^{-4}\) when Starlink is not considered, the lifetime of the three satellites is shortened by 56.21%, 99.09%, and 99.82% when Starlink is considered without advance avoidance control; the lifetime of the three satellites is shortened by 10 revolutions of advance avoidance control without Starlink, when assuming that each warning event satellite can be detected in the case of avoiding control 10 revolutions in advance, without considering Starlink. Assuming that each warning event satellite can be detected and controlled in advance, the lifetime of the three target satellites can be extended by 380%, 502.6%, 493.12%. However, when considering Starlink, the lifetime of GAOEN is extended by 155.44%, and the other two target satellites are shortened by 92.166%, 91.99%, respectively. The data shows that the target satellites with orbital altitude around 500 km are more susceptible to Starlink's interference, while the inclination angle has little effect. Avoidance consumes propellant, but as previously stated, the same amount of propellant consumption does not achieve the same avoidance effect, and using more propellant to achieve the same ACPL value does not achieve the same level of risk. This effect can be reduced to a minimum if avoidance is carried out in advance. However, how to provide early warning of collision timing, which requires accurate orbit forecasting, is a challenge. The purpose of this paper is to assess the risk of future collision and its repercussions for the satellite's avoidance strategy. The objective of the paper is to assess the impact of future collision risk on the satellite avoidance strategy, and the findings are not encouraging. Because of the emergence of mega constellations, target satellites of all types are affected, and this impact is comprehensive, affecting not only the satellite's safety but also depleting the satellite's in - orbit lifetime; frequent avoidance will result in the satellite's premature loss of power and abandonment. Figure 10: Propellant coefficients required for orbit change at different ACPL values during the revolution. (**a,b**) Comparison of different PMF variations with and without Starlink for GAOFEN; (**c,d**) Comparison of different PMF variations with and without Starlink for CORVUS BCS; (**e,f**) Comparison of different PMF variations with and without Starlink for YAOGAN-35C.
## 2 Pentacyclic Triterpenoids (PCTTs)  
PCTTs consist of 30 carbon atoms (six isoprene units) distributed over five fused rings (named A, B, C, D and E). This ring arrangement yields five six-membered rings or four six-membered rings fused to a 5-membered ring, numbered as shown in Figure 1[24].  
As terpenes, the biosynthesis of PCTTs starts by the coupling of active isoprene units. Initially, there is an electrophilic condensation of IPP (isopentenyl diphosphate), with DMAPP (dimethylallyl diphosphate), yielding the precursor of monoterpenes, geranyl diphosphate (GPP). The addition of IPP to GPP generates famesyl diphosphate (FPP), which is the precursor of sesquiterpenes. Then a tail-tail condensation of two FPP molecules leads to squalene, after the release of a diphosphate unit and a 1,3-alkyl shift (Figure 2) [25].  
The biosynthesis of PCTTs continues with the oxidation of squalene, catalyzed by squalene epoxidase, forming 2,3-oxidosqualene. This intermediary assumes the \"chair-chair-boat\" conformation and after a sequence of cyclizations yields the dammarenyl cation, which then undergoes a rearrangement forming the bachenaryl cation. From the bachenaryl cation, the key step in PCTTs biosynthesis occurs, characterized by the formation of the lupanyl cation (Figure 3) [25,26]. Through a sequence of carbocation rearrangements (1,2-shifts), involving hydride, methyl, and ring-opening shifts, the lupanyl cation yields the different PCTTs skeletons, which then could oxidize, reduce, and isomerize, leading to the formation of the different currently known PCTs [25,26].  
The most powerful spectroscopic method in the structural elucidation of PCTTs is \({}^{13}\)C Nuclear Magnetic Resonance (NMR). Comparison of experimental \({}^{13}\)C NMR chemical shifts with literature data is a useful tool in identifying the basic skeleton of these compounds. Through this data, it is possible to make predictions about the influence of a functional group on the chemical displacement of carbons from its basic skeleton [27]. According  
Figure 1: Core rings A, B, C, D, and E found in PCTTs.  
to [PERSON] & [PERSON] [27], for example, the introduction of a hydroxyl group in the PCTT structure induces a deshielding of about 34-50 ppm of the \(\alpha\) carbon, 2-10 ppm of the \(\beta\) carbons and 0-9 ppm of the \(\gamma\) carbons. The effect of the hydroxyl presence on the \({}^{13}\)C NMR chemical shift of the \(\alpha\)-carbon, is related to its configuration, and with the number of \(\gamma\)-gauche-type, and 1,3-diaxial-type interactions with the carbon atoms of the triterpene skeleton [27].  
Figure 2: Simplified biosynthetic route of 2,3—oxidosqualene, the direct precursor of triterpenes, from isoprene. DMAPP: dimethylallyl diphosphate; IPP: isopentenyl diphosphate; GPP: geranyl diphosphate; FPP: famesyl diphosphate; PP: diphosphate [26,28].  
Figure 3: Simplified terpenoid biosynthetic route for the formation of the main pentacyclic triterpene skeletons isolated from Celastraceae species. “a” and “b” indicate two possible biosynthetic pathways [26; 28].
# Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
## 3 Results
### Confusion Matrix  
A confusion matrix was constructed for the best-case scenario, in which all available features except for the HOG features were utilized, and the GBM was trained with 160 images per weed or plant species. For this scenario, 340 images per species were retained for testing. The results are detailed in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline \hline
**Test Image Number** & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline \hline
**SVM** & precision & 41.86 & 59.21 & 66.56 & 71.66 & 73.96 & 78.99 \\  & recall & 41.31 & 56.09 & 64.62 & 70.62 & 72.90 & 77.94 \\ \hline
**RF** & precision & 76.86 & 84.33 & 86.53 & 90.45 & 92.72 & 93.77 \\  & recall & 76.46 & 83.98 & 86.53 & 90.43 & 92.70 & 93.73 \\ \hline
**KNN** & precision & 33.63 & 44.38 & 46.40 & 52.02 & 54.37 & 58.60 \\  & recall & 36.26 & 43.06 & 43.26 & 50.43 & 53.53 & 57.79 \\ \hline
**ANN** & precision & 35.08 & 47.79 & 22.86 & 56.79 & 48.53 & 65.19 \\  & recall & 28.86 & 44.52 & 23.16 & 49.67 & 44.76 & 59.41 \\ \hline
**NB** & precision & 58.32 & 59.60 & 61.14 & 61.73 & 63.24 & 63.22 \\  & recall & 56.57 & 57.35 & 59.79 & 62.25 & 63.69 & 63.68 \\ \hline
**GBM** & precision & 66.57 & 74.58 & 81.99 & 90.30 & 92.75 & 94.35 \\  & recall & 67.00 & 72.99 & 81.28 & 90.04 & 92.62 & 94.26 \\ \hline \hline \end{tabular}
\end{table}
Table 16: The results of combining all features except HU moments and HOG features.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Training Image Number** & & **5** & **10** & **20** & **40** & **80** & **160** \\ \hline \hline
**Test Image Number** & & **495** & **490** & **480** & **460** & **420** & **340** \\ \hline \hline
**SVM** & precision & 41.86 & 59.21 & 66.54 & 70.91 & 74.26 & 78.75 \\  & recall & 41.31 & 56.09 & 64.51 & 69.89 & 72.98 & 77.84 \\ \hline
**RF** & precision & 75.43 & 83.04 & 86.16 & 91.20 & 92.85 & 93.71 \\  & recall & 75.29 & 82.18 & 86.18 & 91.16 & 92.82 & 93.68 \\ \hline
**KNN** & precision & 33.63 & 44.38 & 46.40 & 52.02 & 54.37 & 58.60 \\  & recall & 36.26 & 43.06 & 43.26 & 50.43 & 53.53 & 57.79 \\ \hline
**ANN** & precision & 39.09 & nan & 20.08 & 44.36 & 43.93 & 50.08 \\  & recall & 25.52 & 23.91 & 13.72 & 43.91 & 41.98 & 53.73 \\ \hline
**NB** & precision & 59.88 & 61.04 & 62.65 & 63.34 & 62.82 & 63.26 \\  & recall & 57.61 & 57.96 & 59.58 & 62.57 & 60.71 & 61.37 \\ \hline
**GBM** & precision & 66.22 & 74.03 & 83.00 & 90.46 & 92.81 & 94.56 \\  & recall & 39.70 & 64.42 & 71.77 & 89.28 & 91.51 & 93.38 \\ \hline \hline \end{tabular}
\end{table}
Table 15: The results of combining all features except HOG features.
## 3 Results and Discussion
### Energy Conversion of Two T-Section Prisms in Tandem
#### 3.2.1 Typical Energy Conversion of Hard Galloping (HG)  
At \(4.75\leq U_{r}\leq 8.5\), the four active power curves exhibited an upward trend as the velocity increased, and the active power and efficiency of UTP were similar to those of STP which were higher than those of DTP. The local maximum active power of UTP + DTP was 4.42 W; the corresponding efficiency was 6.67%. For \(U_{r}\geq 9.125\), the active power and energy conversion efficiency of the system (UTP, DTP, STP) started to decrease; as the velocity continuously increased, the oscillation entered the VIV lower branch (\(10.375\leq U_{r}\leq 12.25\)), as shown in Figure 12.  
For \(U_{r}\geq 9.75\), DTP increased compared with UTP; the \(P_{harn}\) of DTP increased as the velocity increased and reached 2.25 W, which was much higher than in STP and UTP, as shown in Figure 13. For \(U_{r}=12.25\), the \(P_{harn}\) of the galloping branch was about 15.01 W higher than that of the WIM branch, and the voltage time-history curve of the WIM branch was more unstable. It can be concluded that the energy conversion capacity in the WIM branch was poor.  
In the galloping branch, the active power and efficiency of UTP and DTP jumped to about 15 W and 4.5%, respectively, at 12.25, and those of UTP were a little higher than those of DTP, as shown in Table 5. Although the values were lower than those of STP, the active power and efficiency of UTP + DTP were about 1.5 times higher than those of STP.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{\(U_{r}\)} & \multirow{2}{*}{\(U\) (m/s)} & \multicolumn{3}{c}{Active Power (W)} & \multirow{2}{*}{Ratio} & \multicolumn{2}{c}{Efficiency (\%)} & \multirow{2}{*}{Ratio} \\  & & **1 st Pri.** & & **2 nd Pir.** & & **Tandem** & & **Single** \\ \hline
11.625 & 1.26 & 13.91 & 11.99 & 25.90 & 16.72 & 1.55 & 9.62 & 6.13 & 1.57 \\
12.25 & 1.33 & 15.11 & 15.01 & 30.12 & 20.17 & 1.49 & 9.08 & 6.24 & 1.46 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of energy conversion for UTP+DTP of \(L\)/\(D=6\) to STP in HG.  
Figure 12: Energy conversion curves of UTP and DTP for \(L\)/\(D=6\), \(R_{L}=11\)\(\Omega\) (\(\zeta_{total,UTP}=0.246\), \(\zeta_{total,DTP}=0.251\)) compared to STP: (**a**) Active power; (**b**) Energy conversion efficiency.
## 3 Proposed Algorithm
### CIM-Based ART with Edge and Age  
CAEA [16] is an ART-based topological clustering algorithm capable of continual learning. In [16], CAEA and its hierarchical approach show comparable clustering performance to recently-proposed clustering algorithms without the difficulty of parameter specifications to each dataset. The learning procedure of CAEA is divided into four parts: (1) initialization process for nodes and a bandwidth of a kernel function in the CIM, (2) winner node selection, (3) vigilance test, and (4) node learning and edge construction. Each of them is explained in the following subsections.  
In this paper, we use the following notations: A set of training data points is \(\mathcal{X}=\{\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{n},\ldots\}\), where \(\mathbf{x}_{n}=(x_{n1},x_{n2},\ldots,x_{nd})\) is a \(d\)-dimensional feature vector. A set of prototype nodes in CAEA at the time of the presentation of a data point \(\mathbf{x}_{n}\) is \(\mathcal{Y}=\{\mathbf{y}_{1},\mathbf{y}_{2},\ldots,\mathbf{y}_{K}\}\) (\(K\in\mathbb{Z}^{+}\)), where a node \(\mathbf{y}_{k}=(y_{k1},y_{k2},\ldots,y_{kd})\) has the same dimension as \(\mathbf{x}_{n}\). Furthermore, each node \(\mathbf{y}_{k}\) has an individual bandwidth \(\sigma_{k}\) for the CIM, i.e., \(\mathcal{S}=\{\sigma_{1},\sigma_{2},\ldots,\sigma_{K}\}\).
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 5 Discussion
### Analysis of the Time Series
Figure 23: Example of the parcel with inclusions of a neighbouring field (cid = 516): (**a**) the most of the parcel is not harvested; (**b**) both parts are harvested. Figure 22: Example of the parcel with nonagricultural vegetation (cid = 163): (**a**) before harvest; (**b**) partial harvest; (**c**) completely harvested parcel. A sugarcane field is homogeneous if the plants are planted roughly at the same time. As a rule, harvest of such a field takes not more than a month; the next harvest will be only after a long period. All the _NDVI_ jumps or SAR algorithm responses during that period are possible due to the interferences in observations or due to the agrotechnical non-harvest operations within the field. The field homogeneity is good support while identifying the harvest dates. Plants in a nonhomogeneous field, planted during different periods, are at different development stages; it means that they differ considerably in terms of _NDVI_ value and backscattering of the SAR signal. On such a field, _NDVI_ drop and jump in the coherence growth may be poorly expressed as the harvest covers only some part of the parcel. Differences in sowing dates result in harvest different dates for field parts. The optical algorithm demonstrates the premature false response caused by cloudiness for the cell shown in Figures 24 and 25. The SAR algorithm omits harvest in April: due to the harvest of a part of the parcel, coherence growth does not reach its threshold. Instead, there are false responses in June and August. The October harvest date is identified correctly by the SAR algorithm. Thus, the proposed algorithms will function correctly for the homogeneous fields not including certain parts of nonagricultural lands. It is desirable to divide the nonhomogeneous lands into  
Figure 14:0 Example of the harvest date omission and false response of the SAR algorithm for a nonhomogeneous parcel (cid = 29). Actual harvest dates (red lines): 8 April 2018, 13 April 2018, and 10 October 2018, 7 July 2018; harvest dates according to the optical algorithm (blue lines): 29 March 2018 and 15 September 2018; harvest dates according to the SAR algorithm (green line): 11 June 2018, 22 August 2018, and 21 October 2018. Figure 14:1 (a,b) Example of a nonhomogeneous parcel (cid = 29). homogeneous parts. The latter is of special importance for the proper functioning of the algorithm based on coherences data.
# Estimation of the Block Adjustment Error in UAV Photogrammetric Flights in Flat Areas
## 3 Results
### Photogrammetric Flight Results
#### 3.2.1 Block 1 Results  
In Scenario A, 168 photographs were processed, containing the forward strips in the block (Figure 7A); 268 photographs were processed in Scenario B (Figure 7B); 200 photographs were processed in Scenario C (Figure 7C); and 334 photographs were processed in Scenario D (Figure 7D). The workflow was processed in Agisoft Metashape [46]. The high-precision configuration was used to orient the images, meaning that the position and orientation of the camera were calculated for each image, and the tie points were extracted in the form of disperse point clouds [28]. The GCPs and ChPs were then marked, as shown in [33]. The points were measured with a minimum of 13 and a maximum of 22 photos, which is a favourable amount, as the larger the number of photos marked, the greater the redundancy. The accuracy of the projection centres was set at 10 m, camera rotations at 10 deg, the accuracy of the GCPs was set at 0.007 mm, the markers at 0.5 pix, and the tie points at 1 pix. The results of the RMSE of the GCPs for Block 1 are shown in Table 5, leaving points 1, 2, 3, and 4 at the corners of the block as GCPs, and points 18 and 19 as ChPs. The GSD indicates that the mean size on the terrain is 5.18 cm. The distribution of the points is shown in Figure 8, together with the orthomosaic obtained from Scenario C.
# Design and Analysis of Systematic Batched Network Codes
## 3 Systematic Outer Codes
### Random Design
For \(M=16\), we perform some experiments using the BATS code implementation in [45] with the parameters in Appendix B. Inactivation decoding is applied to achieve a lower coding overhead. To limit the computation cost of inactivation decoding, the number of inactive packets is limited to 150. In the experiments, we use the rank distribution \(\mathbf{h}\) with \(\mathbb{E}[\mathbf{h}]=M\), which is also called the rank-\(M\) distribution. The experimental results are summarized in Table 1. We observe that when \(K\) is up to \(400M\), a consistent instance with \(n_{s}=K/M\) can be found. However, the larger the value of \(K\), the lower the probability of a code with zero coding overhead. For example, when \(K=10M\), most instances have zero overhead. Meanwhile, when \(K=400M\), only four instances have zero coding overhead. However, when \(K\) is \(600M\), no instance is found with zero coding overhead.
# Investigation into the Fire Hazards of Lithium-Ion Batteries under Overcharging
###### Abstract  
Numerous lithium-ion battery (LIB) fires and explosions have raised serious concerns about the safety issued associated with LIBs; some of these incidents were mainly caused by overcharging of LIBs. Therefore, to have a better understanding of the fire hazards caused by LIB overcharging, two widely used commercial LIBs, **nickel manganese cobalt oxide** (NMC) and **lithium iron phosphate** (LFP), with different cut-off voltages (4.2 V, 4.5 V, 4.8 V and 5.0 V), were tested in this work. Some parameters including the surface temperature, the flame temperature, voltage, and radiative heat flux were measured and analyzed. The results indicate that the initial discharging voltage increases with the growth of charge cut-off voltage. Moreover, the higher the cut-off voltage, the longer the discharging time to reach 2.5 V. An overcharged LIB will undergo a more violent combustion process and has lower stability than a normal one, and the increasing cut-off voltage aggravates the severity. In addition, it is also revealed that the NMC fails earlier than the LFP under the same condition. The temperatures for safety vent cracking, ignition, and thermal runaway of LIBs exhibit similar values for the same condition, which demonstrates that the LIB will fail at a certain temperature. Finally, the peak heat flux, total radiative heat flux, and total radiative heat will rise with the increase in voltage.  
Keywords:lithium-ion battery; fire hazard; overcharge; cut-off voltage; temperature +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### The Second High-Speed Cruise Stage of Flying Back to the Airport
When the trailing edge deflects upward, the attack angle of variable-camber airfoil required to reach the same \(L/W_{0}\) decreases due to the increase in camber, which shortens the acceleration region of the incoming flow at the head of the upper airfoil and reduces the peak value of negative pressure that is leading-edge suction peak value. This demonstrates that leading-edge suction peak value is related to the attack angle. At the same angle of downward deflection, the attack angle of VCaCh airfoil is smaller than that of VCa airfoil, which causes the leading-edge suction peak value decrement of VCaCh airfoil is larger. When the trailing edge deflects upward, the camber of the airfoil decreases along with the required attack angle increases, which causes the leading-edge suction peak value to increase. The VCa airfoil needs a larger attack angle, so the leading-edge suction peak value increment is larger. Figure 11: Pressure coefficient distribution of the baseline airfoil under different \(L/W_{0}\) at \(Ma=0.734\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Airfoil** & \(D_{\text{friction}}\) & \(D_{\text{pressure}}\) & \(D_{\text{total}}\) & \(L/D\) & \(L/D\) \\  & **(N)** & **(N)** & **(N)** & **Increment (\%)** \\ \hline BA & 52.837902 & 33.098545 & 85.936446 & 44.195 & 0 \\ VCaCh\({}_{-}\)\(-\)0.8 & 55.075342 & 31.153174 & 86.228517 & 44.046 & \(-\)0.34 \\ VCaCh\({}_{-}\)\(-\)0.4 & 55.141862 & 31.132482 & 86.274344 & 44.022 & \(-\)0.39 \\ VCaCh\({}_{-}\)0.4 & 55.294519 & 31.376331 & 86.62585 & 43.844 & \(-\)0.79 \\ VCaCh\({}_{0.8}\) & 55.295188 & 31.590941 & 86.886129 & 43.712 & \(-\)1.09 \\ VCa\({}_{-}\)\(-\)0.8 & 52.70671 & 32.96936 & 85.676647 & 44.329 & 0.30 \\ VCa\({}_{-}\)\(-\)0.4 & 52.772106 & 32.983469 & 85.755575 & 44.289 & 0.21 \\ VCa\({}_{-}\)0.4 & 52.856851 & 33.41362 & 86.270472 & 44.024 & \(-\)0.39 \\ VCa\({}_{-}\)0.8 & 52.869419 & 33.960176 & 86.829596 & 43.741 & \(-\)1.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The drag decomposition and lift–drag ratio of morphing airfoil at \(Ma=0.734\), \(L/W_{0}=0.83\).
# A Simplification Method for Point Cloud of T-Profile Steel Plate for Shipbuilding
## 4 Point Cloud Simplification Algorithm Based on Curvature Feature
### Reduction Algorithm Based on the Random Sampling Method  
It is considered that the point cloud data in the flat region of the T-profile steel plate is required to reserve the complete contour features, and the demand for detailed features is not very large. As a result, the random sampling method is adopted for simplification in the flat region [32].  
First, a random function is established; the function covers the original point cloud data, then generates random numbers according to the random function and deletes the corresponding points until the number of remaining point cloud data reaches the preset number determined by the planned reduction rate. The random sampling algorithm is simple and easy to implement, and the processing speed is also high. However, the randomness of the random function is very large. After the original point cloud data are reduced, some important geometric features are possibly lost.