esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 3 Receiver Comb Technologies
### Regeneration from a Single Pilot  
Apart from transmitting two lines as pilot signals, a single pilot can be used for regeneration as demonstrated in [92] and more recently [67]. However, the issue becomes the accuracy of the frequency spacing between the comb lines, which will be related to the stability of the electric clocks that generate the lines in the transmitter and receiver. We have measured that our 25-GHz RF clocks have frequency variations of about \(\pm 15\) kHz over several days, which can impact detection. These variations could be even larger when the two clocks are in different locations, since their temperature-dependent frequency-change is on the order of 0.1-1 kHz per degree change. In addition, the phase noise from the clock will impose a phase noise in each optical carrier whose variance increases with carrier number. While two extremely frequency stable combs would allow one to simplify the comb regeneration to just recovering a pilot tone, in most cases, the frequency spacing should also be tracked. As will be shown in Section 4, the phase noise of two independent RF clocks impairs performance, which increases with the number of transmitted carriers.  
Another possibility could be using the digital information to tune the RF clock in DSP. The effectiveness of this technique will depend on how fast the frequency spacing varies and the latency of the data processing and accuracy. This technique would be an intermediate step between the techniques presented here for comb regeneration in a self-homodyne system and joint processing that will be presented in Section 4 and has to our knowledge never been demonstrated.  
Provided the frequency stability of the regenerated lines is sufficient, the single-pilot scheme will lead to reduced DSP complexity by eliminating the need for frequency carrier recovery [67]. It also reduces phase noise and the risk of cycle slips. In addition, by performing carrier-phase estimation,effects from fiber nonlinearities are mitigated [93; 94]. Therefore, despite not reducing DSP complexity as much as dual-pilot comb regeneration, transmitting a single shared optical pilot tone is a technique of great interest, due to its lower overhead and reduced-complexity comb-regeneration. When combined with a digital pilot-symbol-based carrier recovery, single-pilot tone schemes can enable very low pilot overheads and record spectral efficiency [68].  
A related technique is so-called seed-light wave distribution, where the pilot tone is not multiplexed with the signal, but instead, transmitted on a separate fiber. This concept was demonstrated by [PERSON] et al. [95; 96] by transmitting 10-GBaud PM-64 QAM modulated on the lines of an EO-comb. The seed laser for the transmitter comb was transmitted on a separate fiber and used to seed the LO EO-comb, which enabled the signals to be received without a dedicated carrier-phase estimation stage in the DSP.
# Vibration Damage Analysis of Bottom Hole Assembly Under Axial Impact Based on Dynamic Analysis
## 2 Impact Simulation Model
### Geometric Models, Boundary Conditions, and Material Properties
#### 2.2.2 Materials and Boundary Conditions  
For the assembled model, in order to facilitate the analysis of the drill string stability under impact loading, two different local coordinate systems were established. Material sections were assigned accordingly. The material parameters of the PDC components are shown in Tables 2 and 3. The rock formation was defined as a hard formation--Ya'an granite. Considering the symmetry of the model about the YZ plane, an initial in situ stress field was defined using a prestress field. The overburden pressure was determined based on the formation properties, and the confining pressure in the shallow and intermediate formations is determined by the following equation:  
\[p_{0}=\lambda p \tag{1}\]  
\(\lambda\) is the geostatic lateral pressure coefficient (defined as \(\lambda=\frac{\mu}{1-\mu}\), where \(\mu\) is the Poisson's ratio of the formation); \(p\) is the overburden pressure, (calculated by \(p=gh\rho\), where \(\rho\) is the rock density, \(h\) is the rock density, and \(g\) is the rock density), and \(p_{0}\) denotes the lateral confining pressure.  
In the interaction module, normal stress loads and horizontal principal stresses are applied to the three outer boundaries of the model to achieve a mechanical balance between the internal and external forces of the rock formation. When defining boundary conditions, gravity loading is applied to the entire drill assembly. The drill pipe and drill bit are coupled using a tie constraint. The overall drill string assembly is released in terms of axial and tangential displacements as well as rotational degrees of freedom. The rock body is  
Figure 1: Meshing and boundary conditions of the drill string system–wellbore–rock formation model. (**a**) Wall-BHA-rock model; (**b**) meshing.  
fully constrained to fix its spatial coordinates. The borehole wall is modeled using a three-dimensional discrete rigid shell to constrain the motion of the drill string by simulating the fixed casing wall. Table 4 lists the contact property settings.
# A Multi-Dimension Spatial Method for Topology Awareness and Multipath Generating
## 6 Related Work
### Multipath Diversity  
Lower path correlation contributes to higher fault-tolerance capability, so that, when abnormal nodes or links occur, they would bring less impact on whole paths. High multipath diversity indicates low multipath correlation. The multipath diversity of MPTCP is likely to be improved between multi-homed hosts [3]; however, this scenario is not common. Path diversity is provided by deflecting packets to neighbors lying off the shortest-path in [16]. Multipath diversity is maximized using hybrid GA and k-shortest path algorithm in [15]. Multipath diversity is improved by avoiding shared bottlenecks between joint paths in [7]. In [32], partial sampling schemes are introduced to enhance path diversity for scaling overlay routing. In [27], natural diversity of wide-area networks is leveraged. The above solutions use feedback of probes or transmission to infer or estimate multipath diversity, whereas sometimes highly correlated paths could also exhibit uncorrelated states in appearance, but paths are tightly coupled inside. In addition, the solutions do not directly exploit the diversity among autonomous systems.
## 4 Results and Discussion
### Change Analysis  
To quantify the changing in an urban area, the LULC is the most important. A cross tabulation process was applied to identify the major changes between two LULC maps of the specified time periods. The LULC change maps together with a number of changes in each class from the five periods are displayed in Figure 5.  
It can illustrate evidently that, firstly, the conversion between agriculture and urban was the most distinctive change of the study period. The amount of change increased gradually from approximately 300 km\({}^{2}\) in 1988-1993 to about 850 km\({}^{2}\) in 2008-2011. The second was the transition from agriculture to miscellaneous, which could be found from the analysis. The percentage of its change was less than 10 percent from the first four periods. On the other hand, in the last period (2008-2011), a significant increase in this transition was observed. The third was the conversion from miscellaneous to agriculture, which could be detected from the change analysis with a lower rate of change when compared to the second. Moreover, the conversion between miscellaneous land and urban was another influence that could be seen in the LULC change. Nevertheless, the change analysis indicated other transitions in each LULC class, which also had effects on the urban growth and LULC changes in the BMR.  
\begin{table}
\begin{tabular}{c c c} \hline
**Years** & **Overall Accuracy** & **Kappa** \\ \hline
1988 & 85.19\% & 0.80 \\
1993 & 86.25\% & 0.82 \\
1998 & 90.12\% & 0.87 \\
2003 & 86.95\% & 0.83 \\
2008 & 87.65\% & 0.83 \\
2011 & 89.11\% & 0.86 \\ \hline \end{tabular}
\end{table}
Table 3: Accuracy Assessment of the LULC maps for the years 1988, 1993, 1998, 2003, 2008, and 2011.  
Figure 4: LULC classifications in: **(a)** 1988; **(b)** 1993; **(c)** 1998; **(d)** 2003; **(e)** 2008; and **(f)** 2011.  
Figure 5: LULC change during: (**a**) 1988–1993; (**b**) 1993–1998; (**c**) 1993–2003; (**d**) 2003–2008; and (**e**) 2008–2011; (**f**) The amount of change in each period.
## 3 Results and Discussion
### Far-Field Distance
#### 3.2.3 Results of 20 m Far-Field Distance  
Figure 27 demonstrates the distributions of \(\mathrm{P/P_{t}}\) and \(\mathrm{T/T_{t}}\), and Table 20 illustrates the mean error ratios. Figure 28 provides the optimal total mean error ratios under different configurations, which are highlighted in red. As for the NACA0012 model, the best results are 3.44%, 3.19%, 2.98%, 3.38%, 3.45%, and 2.45%. As for the turbulence model, the minimum results are 2.45% and 2.98%. As for the flux type, the smallest value of ROE is 2.45%, and that of AUSM is 3.38%. Through the results comparison among all the configurations, the optimal total mean error ratio is 2.45%, which is gained by the SST + 400  
Figure 24: Comparison of the simulation performances of 12 m far-field distance under different simulation configurations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Simulation Configurations**} & \multicolumn{5}{c}{**Mean Error Ratios under Different Configurations**} \\ \cline{2-7}  & \multicolumn{1}{c}{**ROE**} & \multicolumn{1}{c}{**AUSM**} \\ \cline{2-7}  & **P/P\({}_{t}\)** & **T/T\({}_{t}\)** & **U/U\({}_{t}\)** & **P/P\({}_{t}\)** & **T/T\({}_{t}\)** & **U/U\({}_{t}\)** \\ \hline SST + blunt & 1.97\% & 4.52\% & 1.6\% & 8.94\% & 5.91\% & 4.07\% \\ \hline SA + blunt & 24.04\% & 24.61\% & 2.39\% & 3.62\% & 5.56\% & 1.14\% \\ \hline
**SST + NACA4** & **2.28\%** & **3.96\%** & **1.04\%** & 10.41\% & 8.54\% & 4.38\% \\ \hline SA + NACA4 & 4.42\% & 2.16\% & 1.9\% & 8.52\% & 3.55\% & 1.4\% \\ \hline SST + 132 points & 16.13\% & 9.27\% & 2.95\% & 4.32\% & 2.88\% & 2.21\% \\ \hline SA + 132 points & 8.58\% & 9.70\% & 2.41\% & 8.91\% & 6.31\% & 3.91\% \\ \hline SST + 200 points & 4.86\% & 3.88\% & 0.96\% & 8.02\% & 7.94\% & 1.77\% \\ \hline SA + 200 points & 5.87\% & 1.18\% & 1.55\% & 7.83\% & 4.63\% & 1.76\% \\ \hline SST + 264 points & 3.44\% & 3.81\% & 1.23\% & 7.95\% & 2.81\% & 0.7\% \\ \hline SA + 264 points & 8.42\% & 3.39\% & 1.73\% & 5.19\% & 5.03\% & 4.08\% \\ \hline SST + 400 points & 6.02\% & 4.72\% & 1.66\% & 13.60\% & 22.17\% & 1.83\% \\ \hline SA + 400 points & 8.66\% & 6.36\% & 2.06\% & 12.65\% & 12.06\% & 0.95\% \\ \hline \hline \end{tabular}
\end{table}
Table 18: The mean error ratios of \(\mathrm{P/P_{t}}\), \(\mathrm{T/T_{t}}\), and \(\mathrm{U/U_{t}}\) of the 12 m far-field distance.
## 4 Results
### Evaluation Metrics  
A confusion matrix is a table utilized for delineating the efficacy of a model [55], as shown in Figure 1.  
Accuracy (\(A\)) represents the proportion of correctly classified instances out of the total instances and is a fundamental measure of overall model performance, as shown in Equation (6).  
\[A\ =\ \frac{TP+TN}{TP+TN+FP+FN} \tag{6}\]  
Precision (\(P\)), on the other hand, quantifies the proportion of true positive predictions out of all positive predictions made by the model. It is particularly relevant when the cost of false positives is high, and is denoted as Equation (7).  
\[P\ =\ \frac{TP}{TP+FP} \tag{7}\]  
Sensitivity (\(S\)) measures the proportion of true positive predictions out of all actual positive instances in the dataset. It is crucial for scenarios where the cost of false negatives is significant, and is shown as Equation (8).  
\[P\ =\ \frac{TP}{TP+FN} \tag{8}\]  
F1 score (\(F\)), which is the harmonic means of precision and recall, provides a balanced assessment of a model's performance, especially when dealing with imbalanced datasets, as seen in Equation (9).  
\[F\ =\ 2*\frac{P*S}{P+S} \tag{9}\]  
Figure 1: Confusion matrix.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter Name** & **Value** \\ \hline Learning rate & 0.001 \\ Loss & Sparse categorical entropy \\ Validation split & 0.1 \\ Epoch & 100 \\ Batch size & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._The Specificity (_SP_) score is a measure used in diagnostic tests to determine the proportion of true negative results among all individuals without the condition of interest, shown in Equation (10).  
\[S\ =\ \frac{TN}{TN+FP} \tag{10}\]
# Determination of Seed Volume Based on Selected Seed Dimensions
## 4 Conclusions  
The volume of an individual seed of a given plant species can be determined based on one, two, or three basic dimensions and the calculated seed volume coefficient. The seed volume coefficient was lowest when seed volume was calculated based on seed length  
Figure 3: Relationships between the sphericity index and seed volume coefficients.  
only (0.049 to 0.447), and it was highest when seed volume was determined based on seed thickness only (0.628 to 2.674).  
Seed volume can be most accurately determined with the use of the formula based on all three basic dimensions and a seed volume coefficient in the range of 0.376 (_Pseudotsuga menziesit_) to 0.537 (_Tilia cordata_). The volume coefficients of _Cormus macrophulla_ (0.525), _Pica abies_ (0.522), and _Cormus sanguinea_ (0.527) seeds are most similar to the volume coefficient of an ellipsoid (0.524).  
When the volume of individual seeds was calculated with simplified formulas, the mean error ranged from 2.7% to 14.3% when two basic seed dimensions were used and from 5.6% to 25.9% when only one basic seed dimension was used. Seed volume can be most accurately determined with the formula based on the square of seed thickness and seed length when two dimensions are used, and with the formula based on the cube of seed thickness when only one dimension is used.  
Seed volume coefficients are most strongly correlated with the sphericity index; therefore, the sphericity index can be used to estimate volume coefficients when pycnometric measurements are not performed. In such a case, the sphericity index can be estimated based on the basic dimensions of a relatively small number of seeds. Seed volume coefficients can be most accurately determined with the use of equations based on the square of seed length and seed width, and the cube of seed length.  
Conceptualization, Z.K.; methodology, Z.K.; software, Z.K.; validation, Z.K.; formal analysis, Z.K., D.C. and A.L.; investigation, Z.K.; resources, Z.K.; data curation, Z.K.; writing--original draft preparation, Z.K.; writing--review and editing, Z.K., D.C. and A.L.; visualization, Z.K.; supervision, Z.K.; project administration, Z.K.; and funding acquisition, D.C. All authors have read and agreed to the published version of the manuscript.  
This research was financed by the Ministry of Science and Higher Education of the Republic of Poland.  
Not applicable.  
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.  
The authors would like to thank [PERSON] for the English-language editing and proofreading of this manuscript.  
The authors declare no conflict of interest.
# Unsupervised Image Classification Based on Fully Fuzzy Voronoi Tessellation
## 2 Related Work
### Fuzzy C-Means (FCM)  
FCM is the earliest fuzzy clustering algorithm. It borrowed the concept of membership degree to construct the clustering objective function for the first time, which expands the membership relationship from hard to fuzzy. The objective function of the subsequent algorithms are all evolved based on this. FCM's objective function is as follows:  
\[J_{\text{FCM}}(\mathbf{U},\mathbf{V})=\sum\limits_{i=1}^{n}\sum\limits_{l=1}^{k}u_{il }^{\lambda}\|\mathbf{X}_{i}-\mathbf{V}_{l}\|^{2} \tag{1}\]  
where \(\mathbf{X}=\{\mathbf{X}_{i}\), \(i=1\), \(\ldots\), \(n\}\) is the data set, \(\mathbf{X}_{i}\) is the feature vector of data \(i\), and \(n\) is the number of data. \(\mathbf{V}=\{\mathbf{V}_{l}\), \(l=1\), \(\ldots\), \(k\}\) is the cluster center set, \(l\) is the index of clusters, \(k\) is the number of clusters (i.e., classes), and \(\mathbf{V}_{l}\) is the feature vector of cluster \(l\). \(\mathbf{U}=\{u_{il}\), \(i=1,\ldots,n,l=1,\ldots,k\}\) is the fuzzy membership; \(u_{il}\) needs to be satisfied in the condition \(\sum\limits_{l=1}^{k}u_{il}=1,i=1,\ldots,n\). \(\lambda\) is the fuzzy factor, which controls the fuzzy degree of the algorithm.
## 1 Introduction
The obtained results were put into the context of the communication requirements of the most popular V2I C-ITS services. In [17], the authors benchmarked the IEEE 802.11p and LTE-based C-V2X (mode 3 and 4) in the context of the V2V communication and cooperative awareness in a realistic highway scenario from the packet reception ratio and beacon update delay perspective. In [18], the benchmark of the ITS-G5 and LTE-based C-V2X (mode 4) in terms of the packet error rate versus signal-to-noise ratio (a physical layer perspective) and in terms of the packet error rate, range, latency and network load (a medium access control layer perspective) was presented. The authors in [19] conducted an exhaustive and fair evaluation of the LTE-based C-V2X (mode 4) and ITS-G5 in a real-life highway environment under the identical conditions for the V2I and V2V communications and different C-ITS services. In [20], the authors compared the IEEE 802.11p and 4G-based C-V2X (mode 4) in the highway scenarios through a system-level simulation in terms of the packet reception ratio, packet delivery ratio, signal to interference plus noise ratio and data packet delay during resource selection for the V2V communication. Finally, the IEEE 802.11p and LTE-based C-V2X (mode 4) were benchmarked from periodic and aperiodic messages of constant and variable sizes perspectives in [21]. To sum up, the above mentioned studies mostly focused on the performance of the LTE-based C-V2X, as well as its performance comparison with the IEEE 802.11p in the context of the V2V communication, and just two of them considered the V2I communication. To the best of our knowledge, no work so far has benchmarked the performance of the 4G-based and 5G-based C-V2X in the context of the V2I communication. It should be noted here that V2I communication is very important in the context of traffic management. Thatis the main reason why we have decided to extend our previous work published in [16] towards 5G-based C-V2X and by doing so to benchmark the 4G-based and 5G-based C-V2X in the corresponding communication context. So, in this paper, we benchmark both C-V2X technologies operating either in the infrastructural mode or in the device-to-device mode (mode 3) from the E2E latency and Packet Delivery Ratio (PDR) perspective in terms of the traffic intensity, communication perimeter and message generation frequency in the context of urban scenarios and naturally the V2I communication. Moreover, fixed and varying infrastructure density scenarios are considered in this study in order to investigate the impact of the infrastructure density on the performance of both C-V2X technologies from the deployed context and metrics perspective. The achieved results are put into the context of the communication requirements of the most popular V2I C-ITS services from the E2E latency and PDR perspective. The remaining parts of this paper are organized as follows. A simulation environment as well as test setup are described in Section 2. Section 3 presents and discusses the experimental results. Section 4 concludes the paper and discusses future work.
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 4 Algorithm Design
### Link Weight Calculation Using Multi-Attribute Decision Making  
In this paper, we comprehensively consider three factors that affect inter-satellite link communication: link propagation delay, link bandwidth, and link lifetime. We propose a multi-attribute decision-making weight calculation method for potential links between satellite nodes, which assigns a multi-attribute decision-making weight as the basis for link selection. The larger the multi-attribute decision-making weight, the better the link performance, and the higher the priority for selection. The algorithm of weight calculation is as follows.
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 2 Review of Related Work
### DMP-Based Optimisation  
DMP has been widely used in LfD. [PERSON] et al. [23] and [PERSON] et al. [24] refined the classic DMP to accommodate demonstrations with the same start and target, as well as with obstacle avoidance, which provides a basis for further optimisation. The force item in DMP is constructed based on RBF kernels and weights which play a crucial role in determining the learning effect on demonstration. Consequently, the optimisation of the force item has received substantial attention. [PERSON] et al. [25] integrated the Takagi-Sugeno fuzzy system with DMP, indicating that the fuzzy inference system can estimate the force item. To better learn the non-linear demonstrations, various researchers used neural networks to optimise the force item. For instance, [PERSON] et al. [15] adopted radial basis function neural networks (RBFNNs) to model the forcing item in DMP, enabling the learning of the position and orientation of demonstrations. To further improve the quality of DMP-generated trajectories, both [PERSON] et al. [16] and [PERSON] et al. [17] not only replaced the force item with neural networks but also applied reinforcement learning for model training. The searching mechanisms in RL can improve the quality of trajectories generated by DMP. Moreover, [PERSON] et al. [6] introduced a residual correction policy aimed at improving the generalisation of DMP in peg-in-hole applications, thereby refining the quality of trajectories generated by DMP. Notably, the aforementioned methods are mainly focused on the improvement of force item and the DMP training process. Due to a lack of incremental learning capacity, when confronted with a new demonstration, the DMP has to be retrained.  
To enable incremental learning within DMP, several scholars have made preliminary explorations. [PERSON] et al. [18; 19] attempted to combine a BLS with the force item of DMP, aiming to equip DMP with incremental learning capability. The BLS has a flat network structure and rapid update capability [20]. This allows the DMP to be iteratively updated efficiently. However, this approach is complex in force item construction. Simplifying the approach for combining BLS and DMP and reducing the computational complexity are necessary. In conclusion, optimising and enhancing the force item to endow DMP with incremental learning capabilities are crucial in real applications.  
Therefore, the key research gaps of the related works can be summarised as follows:  
* Mimicking the geometric features of the original demonstration proves challenging. There is a lack of research in designing an efficient sampling strategy and similarity measurement to avoid sharp turns in generated trajectories and enhance their resemblance to the demonstrations.
* These approaches neglect integrating the incremental learning function within DMP. When presented with a new demonstration, the DMP model has to be retrained. Meanwhile, there is a shortage of industrial applications leveraging incremental learning.