esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Of the ten main compounds identified above, 5% were \(\alpha\)-pinene 1x, \(\beta\)-pinene 1x, \(\beta\)-myrceen 1x, limonene 3x, \(\beta\)-caryophyllene--3x, and \(\alpha\)-humulene--3x. Altogether, 17 different terpen/ terpen/ terpenoids were found between the ten main compounds. The same 24 compounds were found in all three analyses. Altogether, 74 compounds were identified by HS (97.87% of total volatiles), 57 compounds by SPME (90.80%), and 38 compounds by liquid (80.02%) GC/MS, together comprising 94 different volatile compounds. A comparison of all three methods of analysis is presented in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Peak** & **RT** & **\% (Liq)** & **\% (HS)** & **\% (SPME)** & **Compounds** & **Type** & **RI** \\ \hline
40 & 26.462 & & traces & 0.21 & _cis_-\(\beta\)-caryophyllene & bicyclic sesquiterpene & 1406 \\ \hline
41 & 26.639 & 0.11 & 0.09 & 0.45 & _cis_-\(\alpha\)-bergamotene & bicyclic sesquiterpene & 1415 \\ \hline
42 & 26.791 & **10.23** & **6.23** & **13.63** & \(\beta\)**-caryophyllene** & bicyclic sesquiterpene & 1419 \\ \hline
43 & 26.879 & & traces & & \(\gamma\)-maaliene & tricyclic sesquiterpene & 1430 \\ \hline
44 & 27.024 & & traces & & \(\beta\)-copaene & tricyclic sesquiterpene & 1433 \\ \hline
45 & 27.144 & **7.87** & & 2.00 & \(\gamma\)-elemene & monocyclic sesquiterpene & 1434 \\ \hline
46 & 27.160 & 0.57 & 0.66 & 2.09 & \(\alpha\)-bergamotene & bicyclic sesquiterpene & 1436 \\ \hline
47 & 27.248 & **1.95** & 1.20 & **3.53** & \(\alpha\)-guaiene & bicyclic sesquiterpene & 1439 \\ \hline
48 & 27.376 & traces & traces & 0.19 & guaia-6,9-diene & bicyclic sesquiterpene & 1443 \\ \hline
49 & 27.585 & & & 0.32 & humulen-(v1) & bicyclic sesquiterpene & 1455 \\ \hline
50 & 27.649 & **5.64** & **2.72** & **8.35** & \(\alpha\)**-humulene** & monocyclic sesquiterpene & 1454 \\ \hline
51 & 28.042 & traces & traces & 0.12 & 4,5-di-epi-aristolochene & bicyclic sesquiterpene & 1467 \\ \hline
52 & 28.194 & 0.64 & 0.19 & 0.91 & \(\gamma\)-muurolene & bicyclic sesquiterpene & 1477 \\ \hline
53 & 28.282 & & traces & & \(\alpha\)-amorphene & bicyclic sesquiterpene & 1485 \\ \hline
54 & 28.370 & 0.44 & 0.18 & 0.77 & 4a,8-Dimethyl-2-(prop-1-), & & & \\  & & & & & & \\  & &  
Figure 5: Comparison of all three methods of analysis—sample **LOH LL1** chemotype. Counts vs. Acquisition Time (min). Figure 6: Comparison of all three methods of analysis—sample **LOH LL2** chemotype. Counts vs. Acquisition Time (min). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Peak** & **RT** & **\% (Liq)** & **\% (HS)** & **\% (SPME)** & **Compound** & **Type** & **RI** \\ \hline
1 & 2.966 & traces & 2-methylbutanal & saturated fatty aldehyde & 662 \\ \hline
2 & 4.578 & traces & 2-methyl-1-butanol & alcohol & 739 \\ \hline
3 & 6.373 & 0.22 & DL-2,3-butanediol & glycol & 773 \\ \hline
4 & 6.510 & traces & 2,3-butanediol & glycol & 788 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: GC/MS identification of the dry flowering tops—sample **LOH LL2** chemotype.
## 2 Principle
### LS-Based MIMO Algorithm  
During signal transmission, there is polarization crosstalk between the two polarization directions in the fiber link. Since the received OFDM signals are the polarization mixing of two transmitted OFDM signals with various SOP in the PDM-OFDM fiber link, channel estimation is necessary for polarization demultiplexing and signal demodulation. Each subcarrier is a subchannel in the OFDM system. The PDM fiber link can be dealt with as the two-input-two-output (\(2\times 2\)) channel model, where the Jones matrix \(H\), consisting of \(H_{fiber}\) and \(H_{receiver}\), can be generally expressed as:  
Figure 1: Schematic diagram of PDM-DSB FASCD. (a–c) the optical spectra at corresponding points. AWG: arbitrary waveform generator; OC: optical coupler.  
\[H=\begin{bmatrix}H_{xx}&H_{xy}\\ H_{yx}&H_{yy}\end{bmatrix} \tag{2}\]  
Thus, the relationship between the transmitted signal and the received signal \((R_{H}R_{V})^{T}\) is established.  
\[\begin{bmatrix}R_{H}\\ R_{V}\end{bmatrix}=\begin{bmatrix}H_{xx}&H_{xy}\\ H_{yx}&H_{yy}\end{bmatrix}\begin{bmatrix}\text{S}_{x}\\ S_{y}\end{bmatrix}+\begin{bmatrix}N_{H}\\ N_{V}\end{bmatrix} \tag{3}\]  
Here, \((N_{H}N_{V})^{T}\) are channel noise. Since the variation of \(H\) is slow compared to the signal duration, periodical channel estimation with the train sequences can obtain the channel transmission matrix \(H\). Figure 2 demonstrates the distribution of the training sequences \(\left(T_{x}T_{y}\right)^{T}\) and their corresponding received signals. There is also  
\[\begin{bmatrix}R_{h1}&R_{h2}\\ R_{v1}&R_{v2}\end{bmatrix}=\begin{bmatrix}H_{xx}&H_{xy}\\ H_{yx}&H_{yy}\end{bmatrix}\begin{bmatrix}T_{x}&0\\ 0&T_{y}\end{bmatrix} \tag{4}\]  
Then, the channel matrix is obtained as  
\[H=\begin{bmatrix}R_{h1}/T_{x}&R_{h2}/T_{y}\\ R_{v1}/T_{x}&R_{v2}/T_{y}\end{bmatrix} \tag{5}\]
# Cross-Layer MAC/Routing Protocol for Reliability Improvement of the Internet of Things
## 3 Topological Structure Mesh Routing
Figure 3 shows the drawbacks of the cluster-tree-based routing protocol. In Figure 3, the source transmits a data frame through the base station, although the destination is in the communication range of the source device. Additionally, when the mobile device moves to another cluster, it transmits the reassociation frame to the base station and allocates a new address from the base station. To address these drawbacks, we proposed a new MAC layer-based cross-layer protocol for mesh routing.
## 2 Related Works
### Related Works on Using FastText for Authorship Attribution  
In NLP tasks, much attention is paid to the quality of word representation. The fastText library in the implementation of Facebook [33] is a significant step in the evolution of vector semantic models and ML in text processing. However, it has not been applied to determine the authorship of short Russian-language texts yet, and it was decided to pay special attention to this model in this study. The main advantage of fastText is the processing speed in comparison with other models. Skip-gram negative sampling is used to model vector representations of words. Some languages (including Russian) contain compound words. Therefore, a subword model is added to the basic model, allowing the representation of words in the form of \(n\)-grams. Hashing is used to fix the dimension of features.  
To solve the problem of identifying the author of a Bengali text, the authors of the study [34] applied the approach based on fastText due to the rapid training of the model. The dataset was obtained from three online public blogs. The total dataset consisted of 3 authors. 300 texts per author were used as the training dataset and 50 were used for testing. \(N\)-grams from one to five, as well as various combinations of them, were used as feature sets. Using fastText, an accuracy of 82.4% was achieved, surpassing the NB classifier's best accuracy of 69%, but inferior to SVM's 85%. Using \(n\)-grams of length 5, the training time of SVM was 2700 ms, of NB was 2235 ms, and of fastText was only 147 ms.  
The article [35] solved the problem of short texts authorship identification. The authors hypothesized that such research could be useful for improving chatbots and personal assistants. The study compared the effectiveness of LR training on TF-IDF text representation and fastText using the dataset consisted of subtitles of 236 episodes of the TV series \"Friends\". All punctuation marks, monosyllabic sentences, and replicas uttered by two or more actors at the same time, were removed. Train and test sets were obtained by 80:20 splitting. For quality estimation, 5-folds cross-validation was performed. Experiments were carried out for 2 to 30 word samples. The accuracy of TF-IDF was about 5% better than fastText's. The best accuracy for TF-IDF was obtained with 25 words--36% and for fastText--31%.
## 4 Numerical Examples
### Analysis and Discussion of Model Performance Comparison Using LSE Estimation Parameter Values
In general, compared with other models, the PM has better predictive performance and better fitting performance except for when using 85% of the data (DS1-2, DS2-1, DS2-2, and DS2-3). When CSS RMs, such as DSS, ISS, P-N-Z, and GGO, are used in OSS reliability assessment, their fitting and prediction performance is better. But no CSS RMs can adapt to all OSS development environments. This is because the OSS development process is complex, dynamic, and uncertain. Compared with other models, the fitting and prediction performances of the Wang model and Li model are at a standard level. Because the two RMs of OSS are based on perfect debugging without considering the introduction of faults in the development process of OSS, their fitting and prediction performance is of a standard level. Considering the complexity of fault introduction, i.e., stochastic changes in fault introduction, the PM shows a better fitting and prediction performance than other models. As a result, the PM can be used to assess the real reliability of OSS and can better adjust to the OSS development environment. In addition, from Tables 3-20, we can see that when 100% and 85% of the fault data are fitted, respectively, the PM parameters are very different. In general, the more fault data sets that are fitted, the better the fitting performance of the model is. However, the prediction performance of the model may not be good. This can also be seen from the above experiments. For instance, the PM has the best prediction performance when using 85% of the data (DS1-2, DS2-1, DS2-2, and DS2-3), but it does not have the best fitting performance. On the other hand, this illustrates the complexity of OSS RM, and the quality of OSS fault data collection is also an important factor affecting the performance of software RMs. On the other hand, we have conducted experiments on 95% confidence intervals of the fault data for the PM using the MLE method. From Figure 4, we can see that one point falls out of the 95% confidence intervals, but it will not have much impact on the estimation of model parameters. We argue that this phenomenon may be caused by the complexity and uncertainty of OSS development and the testing environment. Therefore, it is very difficult to establish an OSS RM. In summary, from Figure 4, we can clearly see that most of the fault data points that are estimated by the PM fall well within 95% confidence intervals. These results show that the PM simulates the FD and fault introduction processes in OSS development and testing processes well, and its parameter value estimation is guaranteed. Overall, using LSE and MLE to estimate the parameter values of the PM can effectively fit the fault data of OSS and can accurately estimate the number of remaining faults in the software. Thus, compared with other models, the PM has a better goodness-or-fit and predictive performance. Meanwhile, the PM has good stability, adaptability, and robustness when evaluating the reliability of OSS. Figure 4: _Cont._
# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 2 Related Work
the results show the enhancement overall system throughput as well as edge users throughput. In [44], the authors propose an ILP to coordinate the clusters of the cells as well as a Radio Coordination Controller (RCC) placement optimization problem in order to maximize the overall network's throughput. In [45], the authors discussed the cell edge users' throughput considering CoMP while minimizing the data exchange (X2 interface) between BBU pools by configuring the front-haul network. The authors proposed two heuristic algorithm graph-based lightpath reconfigurations (MCG-LR) and a BBU weight-based lightpath reconfiguration (BW-LR). The results show the improvement of the elastic radio resources. However, the authors did not take into consideration the BBU placement problem and delay requirements. Finally, to the best of our knowledge, this is the first study to solve the BBU placement problem by taking into consideration the minimization of the number of inter-CoMP users.
## 3 Influence of Inherent Anisotropy
### Microscopic Results
#### 3.2.3 Evolution of Anisotropy  
The evolutions of the scalar anisotropy parameter of contact normal \(a_{r}\) of different initial anisotropy specimens are illustrated in Figure 10. When \(\delta=0^{\circ}\) and \(30^{\circ}\), \(a_{r}\) initially showed an upward trend and then stabilized. However, when \(\delta=45^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\), \(a_{r}\) firstly decreased and then tended to be stable. This phenomenon can be explained in this way: under the action of increasing vertical stress, the contact is redistributed, and more contacts are formed in the vertical direction to withstand greater stress. From the previous initial contact normal distribution rose graph, vertical contacts will increase the anisotropy of contact normal for smaller \(\delta\) while decreasing it for larger \(\delta\). The stability of \(a_{r}\) in the subsequent stage indicates less contact redistribution and more frequent variations of contact force under the condition of larger vertical stress during \(K_{0}\) loading.  
The evolution of contact normal force anisotropy parameters \(a_{n}\) and contact tangential force anisotropy parameter \(a_{t}\) are shown in Figures 11 and 12, respectively. In Figure 11, it is observed that the contact force anisotropy parameter \(a_{n}\) increases sharply at first and then stabilizes with the increase of vertical stress. It is worth noting that \(a_{n}\) value of the specimen  
Figure 10: Evolution of \(a_{r}\) in different initial anisotropy specimens.  
Figure 9: Coordination number \(CN\) versus vertical stress \(\sigma v\) along \(K_{0}\)-loading path for the different initial anisotropy samples.  
with \(\delta=90^{\circ}\) is significantly higher than that of other specimens, and the development of \(a_{n}\) value of other specimens is very similar. As for \(a_{t}\) value illustrated in Figure 12, \(a_{t}\) value is very small and fluctuates violently during \(K_{0}\) loading, but similar to \(a_{n}\), it also reaches a stable value at the end of the test. Interestingly, \(a_{t}\) values of the specimens with \(\delta=30^{\circ}\) and \(\delta=45^{\circ}\) in the stable phase are the same, while those of the specimens with \(\delta=60^{\circ}\) and \(\delta=90^{\circ}\) are the same.
# Subwavelength-Cavity High-Gain Circularly Polarized Antenna with Planar Metamaterials
## 3 Conclusions  
In this study, a subwavelength-cavity circularly polarized antenna was proposed using artificial permeability-negative metamaterials that can convert linear polarization into circular polarization. This antenna also has the advantages of adjustable polarization and a high gain. Compared with conventional high-gain circularly polarized antennas, the proposed antenna achieves the desired performance with a much lower profile, simpler design, and easier integration. This cavity antenna has good narrowband characteristics to engineer high-quality filtering devices in compacted radio systems, which is also conducive to the miniaturization of the radio systems. Good agreement between the measured and theoretically predicted behaviors ensured the validity of our design and prediction method. Such metamaterial antennas can be utilized in communication systems, satellite antennas, and spaceborne radio detectors.  
Conceptualization, W.W., G.L. and G.D.; methodology, G.L. and G.D.; software, W.W., G.L. and C.D.; validation, W.W., G.L. and C.D.; formal analysis, W.W., G.L. and J.L.; investigation, W.W., G.L., C.D. and J.L.; resources, G.L. and G.D.; data curation, G.L. and G.D.; writing--original draft preparation, W.W. and G.L.; writing--review and editing, J.L., F.L. and G.D.; visualization, W.W. and G.L.; supervision, G.L. and G.D.; project administration, G.L. and G.D.; funding acquisition, G.L. and G.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shandong Province (Nos. ZR2019 MA055 and ZR2020 QA071).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: (**a**) Photograph of the proposed RHCP metamaterial antenna; (**b**) measured axial ratio (\(AR\)) of the proposed RHCP metamaterial antenna. Comparison of the measured radiation patterns of the RHCP metamaterial antenna at 2.80 GHz on two mutually orthogonal planes: (**c**) \(\phi=0^{\circ}\); (**d**) \(\phi=90^{\circ}\).
## 4 Multi-Beam Array Generation and Optical Alignment Experiment  
### Multi-Beam Array Generation  
On the base of the single virtual OAP phase, the area of LCoS-SLM could be divided into sub-apertures. For each sub-aperture, the position and the focal length of the focus spot of the virtual OAP phase could be designed separately to get the whole phase pattern. Moreover, space three-dimensional position controllable light spots array could be got. The multi-beam array generation optical system configuration is shown in Figure 12.  
Figure 11: The focus spots produced by the (**a**) compensated phase and (**b**) uncompensated phase, and (**c**) light spot after moving Charge Coupled Device (CCD) camera.  
Figure 12: Multi-beam array generation optical system configuration.  
Figure 10: Wavefront after the iterative compensation.  
With the parameter of a tilt angle of \(6^{\circ}\) and a focal length of 500 mm, a \(2\times 2\) array phase loaded on LCoS-SLM is designed, and the image is captured by the CCD. Compared with a copper off-axis parabolic array specimen's focus spots image (1-inch diameter, manufactured by an ultra-precision machine tool, with eight effective light spots in a \(3\times 3\) array, the central one is defocused due to surface error [19]) in Figure 13a, the light spots (Figure 13b) generated by the LCoS-SLM could also achieve good quality and uniformity.  
Figure 14 shows an example of \(2\times 2\) OAP array with different focal length \(f_{1}=500\) mm and \(f_{2}=f_{1}+\Delta f\), where \(\Delta f=25\) mm, respectively. The phase and light spots marked No. 1 and 3 are set with the focal length of \(f_{1}\), while No. 2 and 4 are set with \(f_{2}\).
## 2 Improved Homotopy Analysis Method
### Fundamentals of Improved Homotopy Analysis Method  
For quick reference, the new improved homotopy analysis method (IHAM) is briefed. The IHAM is essentially different from the HAM. Table 1 compares the formulations of IHAM and HAM.  
Substantial difference lies in the different construction methods. HAM constructs a higher order nonlinear iterative homotopy differential equation by the selected linear differential equation and the original non-linear differential equation. However, IHAM builds up a higher-order nonlinear iterative homotopy differential equation using the selected linear differential equation, the selected simple non-linear differential equation and the original non-linear differential equation (See Table 1).  
Generalized Equation (3b) or Equation (3c) have the form of  
\[\mathbb{N}[v(\xi)]=0:\,\,v^{\prime}(\xi)=f[w(\xi)],\xi\in[0,1] \tag{11}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Homotopy Analysis Method (HAM)** & **Improved Homotopy Analysis Method (IHAM)** \\ \hline \hline \multirow{4}{*}{Original N(\(\xi\))= 0 and Auxiliary L(\(\xi\))= 0  
where in \(f(w)\) is a derivable function in compliance with the boundary conditions of \(f_{w}(0)=c_{0}\), \(f^{\prime}_{w}(0)=c_{1}\), \(\frac{1}{2}f_{w}(0)=c_{2}\), \(\frac{1}{3!}f^{\prime\prime\prime}_{w}(0)=c_{3}\); \(w(\xi)\) is the selected polynomial function of an independent variable in Equations (2.9) and (2.10). The boundary condition in Equation (2.4b) or Equation (2.4c) can be expressed as  
\[v(0)=0 \tag{2.12}\]  
Note that it is unnecessary to assume the existence of any small or large parameters in Equation (2.11). Thus, the proposed approach is general.  
Linearizing original Equation (2.11) yields  
\[\text{L}[v(\xi)]=0\text{: }v^{\prime}(\xi)=c_{0}+c_{1}w(\xi),\xi\in[0,1] \tag{2.13}\]  
which complies to the boundary condition of  
\[v(0)=0 \tag{2.14}\]  
We choose the simplest auxiliary nonlinear equations of  
\[\text{N}_{0}[v(\xi)]=0\text{: }v^{\prime}(\xi)=c_{0}+c_{1}w(\xi)+c_{2}e_{2}w^{2 }(\xi)+c_{3}e_{1}w^{3}(\xi),\ \xi\in[0,1] \tag{2.15}\]  
herein, \(c_{0}\), \(c_{1}\), \(c_{2}\) and \(c_{3}\) are undetermined coefficients; and \(e_{1}\in[0,1]\), \(e_{2}\in[0,1]\), \(e_{1}^{2}+e_{2}^{2}\
eq 0\), which satisfies the boundary condition of  
\[v(0)=0 \tag{2.16}\]  
Detailed formulation for the improved homotopy analysis method could be found in our previous paper titled \"Explicit Solution to Large Deformation Cantilever Beam by Improved Homotopy Analysis Method I: ROTATION ANGLE\".
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 4 Results
### Thermocouple Data  
Heat is a major concern for the turboelectric system, primarily because of the delicate electronics. Thermocouples were used to measure temperatures inside the fuselage and on the rectifier. The results are shown in Figures 9 and 10. These data were not synchronized with the Pixhawk clock because it had a separate power source and microcontroller. The startup sequence and the wait for the engine to warm up can then be seen as the temperature initially increases in Figure 9. The flight was short, lasting less than 300 s total, and did not reach a steady state. Therefore, the actual flight sequence does not begin until around the 270 s mark. From the start of the engine around 75 s to the peak temperature at 545 s, a linear growth pattern (R\({}^{2}\) of 0.975 and 0.847 respectively) can be seen. In this case, a temperature rise to steady state should be a clear exponential function with an asymptote as a function of outside temperature. This means that the temperature had not leveled off and the system was undercooled. This was unexpected since the engine had been provided a large amount of cooling flow and heat sinks [18]. The sharp decline caused just after landing is not radiate cooling, but rather a forced-air blower used to cool the aircraft on landing. It is likely that the contribution of the radiate heat of engine was underestimated, and that the aircraft should have been better insulated against the thermal loads of the engine.  
Figure 10: Thermocouple data at the rectifier.  
Figure 9: Thermocouple data inside fuselage.