esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Crosstalk between Data Signals and Feed Light in Power-over-Fiber
## 4 Evaluation of Signal Degradation in PWoF Using MMFs
### Analog Signal Transmission  
Figure 7a shows the EVM penalties to the back-to-back signal of the transmitted data signal to the 2 km and 4 km MMF transmission. The back-to-back signal indicates direct detection between the two WDM couplers without MMF, as shown in Figure 6, and the EVM value was 0.98%. As the laser output power of the RFL increased, the EVM penalties increased. This trend has been also observed in PWoF using SMFs [3, 4]. In particular, in  
Figure 5: Example of 20 dB bandwidth and noise level of electrical spectra of RFL output after 2 km transmission while varying laser output power.  
Ref. [3], the different characteristics have been observed for a conventional SMF and non-zero dispersion shifted fiber with each different core diameter and refractive index profile. This means that the transmission performance also depends on these parameters of optical fibers. In our experiment, the values increased more steeply for the 4 km transmission, and the EVM could not be measured at laser powers above 6 W. Figure 7b-d show the electrical spectra of the transmitted analog data signal after 0 km, 2 km, and 4 km transmissions, when the output power of the RFL was set to 5 W or 10 W, respectively. In the 0 km transmission, no significant noise components were observed because the intensity noise transfer was not induced. However, as the transmission distance increased, the noise components increased, owing to the intensity noise transfer induced by the longitudinal mode beating and the mode partition noise of the RFL in the MMF. In the 2 km transmission, it can be seen that the signal spectrum included the 10 MHz comb component, which caused degradation of the signal quality. A larger amount of noise was transferred to the signal at a longer transmission distance. In the 4 km transmission, the 10 MHz comb component could be clearly observed. In addition, as the comb component increased, the signal spectral component decreased. This is the reason for the sharp and unmeasurable increase in the EVM penalty, as shown in Figure 7a. Although MMFs have a larger core area and lower nonlinearity than SMFs, it was clearly observed that the intensity noise transfer of the feed light strongly affects the analog data transmission performance in PWoF using single-core optical fibers.  
Figure 6: Experimental setup for simultaneous data signal and power transmission using an MMF. PPG: Pulse pattern generator, SG: Signal generator, LD: Laser-diode, PC: Polarization controller, LNM: LiNbO\({}_{3}\) modulator, ISO: Isolator, SOA: Semiconductor optical amplifier, BPF: Bandpass filter, RFL: Raman fiber laser, WDM coupler: Wavelength-division-multiplexing coupler, PD: Photodiode, ATT: Attenuator, SA: Signal analyzer, BERT: BER tester, OPM: Optical power meter.  
Figure 7: (**a**) EVM characteristics of data signal while changing laser output power after 2 km and 4 km MMF transmission. (**b–d**) Electrical signal spectra of data signal for various transmission lengths and laser output powers.
## 3 Results
### Devices Featuring Ni-ALD Dielectric-Ni Junctions
#### 3.2.4 SET Devices with Ni-SiN\({}_{x}\)-Ni Tunnel Junctions  
As mentioned earlier, two approaches can be used to fix the problem of native metal oxides forming during the ALD process: chemically reducing the oxide (as described in the previous section) or using a dielectric that does not contain oxygen. The devices studied in this section used the latter approach and were prepared using the half-damascene process flow described above with PEALD of 21 cycles of SiN\({}_{x}\), to form a dielectric barrier (with approximate thickness of 1.05 nm). Bis(diethylamino)silane (C\({}_{8}\)H\({}_{22}\)N\({}_{2}\)Si) and H\({}_{2}\) + N\({}_{2}\) plasma were the precursors used in the PEALD process in an Oxford FlexAL system. In contrast with previous sections, where oxygen-containing dielectrics were used (Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\)), no post-PEALD treatment was used in this process. An experimental diamond plot for a typical device is shown in Figure 17.  
Reduction of the conductance at the CBO peak to nearly \(G_{0}/2\) along with constant \(G(V_{ds})\)\(\sim\)\(G_{0}\) when \(\mid V_{ds}\mid>\) 2\(E_{c}/e\) (Figure 17a) is indicative of negligible presence of thermally activated NiO in the junctions and confirms that the presence of the oxidizing pulse (O\({}_{2}\), O\({}_{3}\), or H\({}_{2}\)O) in the ALD process is the main origin of the parasitic native oxide formation on the island. Moreover, since SiN\({}_{x}\) is hydrophobic and does not contain oxygen, the oxidation of the top metal while being deposited on the SiN\({}_{x}\) barrier is negligible. Experiments once again reveal a very large level of switching RTS noise similar to that observed for Pt-Al\({}_{2}\)O\({}_{3}\)-Pt and Ni-SiO\({}_{2}\)-Ni devices described in the previous sections. The noise analysis performed in [49] pinpoints the location of the traps within the junctions (either in the dielectric or at the metal-dielectric interfaces). Silicon nitride is known as a trap-rich material where the abundance of traps is generally associated with silicon atoms replacing nitrogen atoms, resulting in silicon dangling bonds, referred to as \(K\)-center defects [50]. Other defects can be associated with hydrogen, which is typically present in the SiN\({}_{x}\) matrix [51].
# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 4 Evaluation
### Performance Evaluation of STEAM
Another attention mechanism, CBAM, showed excellent training performance with 99.24% accuracy. However, the validation accuracy drops to 83.33% after Epoch 6, indicating that CBAM is effective for training but may not identify as effectively as STEAM in invisible data. Conversely, the ViT model performed much lower with an accuracy of 3.03% throughout the epochs. This may be due to the complexity of the ViT model and the possibility that the UAV trajectory data lacked sufficient context variability for effective learning in this model. In addition, the unique requirements of transformer-based models for pretraining on large datasets indicate that ViT is not suitable for real-time applications of UAV trajectory analysis. In summary, these results shown as Table 1 indicate that STEAM is a very suitable and effective choice for UAV trajectory analysis, especially in scenarios that require real-time processing and robust generalization capabilities for new data. Furthermore, STEAM's precision of 100% and recall of 93.75% during training, along with a precision of 98.31% and recall of 91.15% in validation, highlight its proficiency in correctly identifying relevant trajectory data while minimizing false positives and negatives. These metrics collectively underscore STEAM's effectiveness in providing reliable and accurate analysis of UAV trajectories, making it an excellent tool for applications requiring precise and dependable trajectory analysis. The excellence of STEAM in both training and verification phases highlights its potential as a reliable tool for UAV trajectory analysis in a variety of real-world applications. These model performance comparisons describe the underlying functions that contribute to the results, such as STEAM's robust attention mechanisms and generalization capabilities,  
Figure 7: Results of model learning and validation evaluation on UAV trajectories. which are pivotal to outstanding performance. Custom attention strategies for trajectory identification of STEAM, focusing on the most prominent trajectory functions, suggest the need for UAV pathways critical for real-time and reliable trajectory analysis in various operational contexts.
# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 2 Related Work
### Methods Based Multi-Modality Fusion  
Methods via visual imagery predominantly provide textural information, lacking depth insights, whereas approaches based on LiDAR point clouds offer spatial geometric data but miss textural details. Textural data plays a crucial role in object detection and classification, while depth information is instrumental in estimating the spatial positioning of objects. The integration of image and LiDAR point cloud data to enhance overall performance is a prominent research trend in 3D object detection. Charles et al. introduced F-PointNet [39], utilizing image data to create high-quality 2D candidate frames. These frames are then projected into 3D space for extracting point cloud features, leading to the generation of 3D candidate frames. Chen et al. developed the MV3D approach [40], employing point clouds to construct corresponding front views and bird's-eye views. These, combined with RGB images, serve as inputs to three distinct feature extraction networks. This methodology achieves 3D object detection, classification, and bounding box regression by integrating feature maps from all three sources. Diverging from the MV3D algorithm, Ku et al. introduced the AVOD algorithm [41], which utilizes only image data and bird's-eye views generated from encoded point cloud data as inputs for the network. MVX-Net [42] employs two straightforward yet effective early fusion methods, Point-Fusion and Voxel-Fusion, blending the textural information from visual imagery with the spatial geometric information from point clouds to facilitate high-precision object detection. The study ContFuse [10] introduces an innovative approach by identifyingpoints in camera images that correspond to the point cloud (and its nearest neighbors). It then merges these interpolated camera features with each point to achieve a point-wise correspondence. Crossfusion [43] employs a bidirectional fusion strategy, integrating point clouds and images at the anchor level. While ContFuse accomplishes point-level fusion, Crossfusion achieves anchor-level fusion, leading to potential issues with feature alignment. In this paper, the objective is to design a 3D object detection network for point clouds grounded in point feature representation, enhancing the feature representation capability of point clouds through the fusion of corresponding image area features with the point cloud.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 5 Orbiting Microgravity Facilities
### The International Space Station
#### 5.2.2 Environmental Requirements  
To ensure a successful experiment and, mostly important, the safety of crew members and of the ISS environment, the on-ground preparatory risk-assessment must include all safety requirements and compliance. Research groups are normally supported by their national Space Agencies in identifying the critical points, which can vary based on the subject of the experiments. The requirements can include, for example, double containment level, non-toxic fixative for cells, fireproof material, no gas exchange, and no outgassing of hardware material. Because of the differences in gas and liquid behaviour during flight and on board the ISS, reagents easily handled in the laboratory on Earth must be strictly controlled when launched or utilized in Space.
# Pursuing High Quality Phase-Only Liquid Crystal on Silicon (LCoS) Devices
## 7 Phase Accuracy  
Phase accuracy is determined by the projected image. The pixel-level spatially anamorphic phenomenon provides a way to appraise the phase accuracy from a linear phase calibrated panel [64; 65; 66; 67]. The phase error is quantified by employing various spatial frequencies and grating directions deviating from an ideal crenel-like binary grating. The deviation from the ideal binary grating is related to the non-desired LC fringing field and reverse-tilt effects [67]. To measure phase modulation, a 0 th gray level (GL-0) is defined as 100% of the intensity of the light reflected from the panel. The \(\pm\)1 st-order light intensity is determined by the spatial frequency and orientation of the binary grating. The binary-grating differences are created by changing the corresponding gray-level pairs, in which the gray level of the reference pixel is fixed as GL-0 and changed 5-255 gray levels (GL-5-255) in the counterpart. The PCM-2-01-633, PCU-3-01-633, and LETO LCoS-SLM panels, possessing the same backplane design, are compared regarding their pixel-level spatially anamorphic phenomenon at \(\lambda\) = 633 nm. In Figure 11, H and V represented horizontal and vertical grating, and P is the binary grating pairs at different periods: 1 + 1, 2 + 2, and 6 + 6 pixels/grating, which correspond to spatial frequencies of 76.8, 38.4, and 12.8 lines/mm, respectively. The average error of the pixel-level phase accuracy of the horizontal and vertical grating is 9% for PCM-2-01-633, 7.7% for PCU-3-01-633, and 12% for LETO. This result suggests that PCU-3-01-633 with a small cell gap can effectively suppress the pixel-level phase accuracy error. However, the 1 + 1 in vertical grating still has 10% error. Small average phase-accuracy errors in both horizontal and vertical grating directions are necessary at the same time to minimize the error for the application of 2D CGH patterns.  
Figure 11: The phase accuracy measurements of three different LCoS-SLMs at 633 nm.  
Figure 10: The phase stability measurements of PCU-3-01-633 with two different cell gap LCoS-SLMs at 633 nm.
## 4 The Preliminary Experiment of Atmospheric Density Inversion for SWARM-A
### Inversion of SWARM-A Atmospheric Density by Semi-Long-Axis Decay Method
#### 4.2.2 Orbital Extrapolation Calculations  
Density inversion was carried out using scientific orbit data from the SWARM-A satellite. Since the satellite is a low-orbit satellite with an entry altitude of 470 km, this altitude is subject to significant drag from the thermosphere, which makes it easy to analyze. At the same time, the satellite carries a high-precision GPS receiver, and the orbiting accuracy can reach the centimeter level [52]. The Keplerian orbital roots such as e,  
Figure 3: The product of SWARM-A satellite damping factor and effective area is usually expressed by B, the anti-ballistic coefficient. The anti-ballistic coefficient of SWARM-A fluctuates between 2.56 and 2.58.  
n, and f in Equation (2) can be calculated from the satellite position and velocity in the orbit data. The satellite mass m can be read directly from the accelerometer data, and the product of the atmospheric damping coefficient and the cross-section, \(C_{D}*A\), can be calculated from the model in Section 4.1 above. For the change amount of the half-length axis using the SGP4 integrator for orbital extrapolation, we solve out the instantaneous state quantity of SWARM-A under the effect of atmospheric drag force and use [PERSON]'s law to calculate the state quantity out of the half-length axis and then carry out the difference operation. The atmospheric density is obtained by inverting the ballistic coefficient \(B\), obtained by combining the calculations in Section 4.1.
## 2 Results and Discussion
### Method Validation  
In the Supplemental Table S3 are presented the parameters of method validation. For all analyzed compounds good linearity could have been obtained. For the aldehydes LOQ was in the range from 2.21 up to 16.22 \(\upmu\)g/kg. The highest LOQ was calculated for acids with an average value of 669.87 \(\upmu\)g/kg. The RSD values obtained for intraday and interday precision did not exceed 15%. The method can be considered accurate because accuracy values for all analyzed compounds were within 15% of the nominal value.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Temperature (\({}^{\circ}\)C)** & **Incubation Time** & **Exposure Time** & **Predicted Value** & **Obtained Value** \\  & **(min)** & **(min)** & **(Feak Area \(\times\) 10\({}^{6}\))** & **(Feak Area \(\times\) 10\({}^{6}\), Mean \(\pm\) SD)** \\ \hline Alcohols & & & & 202.00 & 209.00 \(\pm\) 9.14 \\ Acids & & & & 49.30 & 49.90 \(\pm\) 1.35 \\ Carbonyls & 60 & 20 & 60 & 40.20 & 39.80 \(\pm\) 0.49 \\ Norisopenroids & & & & 1.53 & 1.47 \(\pm\) 0.15 \\ Monoterpenes & & & & 15.50 & 16.20 \(\pm\) 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal SPME-Arrow extraction conditions, predicted and experimentally obtained values for individual groups of bound volatile compounds.  
Figure 5: Response surface plot for interaction between extraction temperature and exposure time for bound (**A**) alcohols, (**B**) C13-norisopenoids, (**C**) monoterpenes and (**D**) carbonyls.
## 5 Numerical Analysis Results and Comparison of Strong Wind Analysis with Real Tornado Action
### Distributed Force Loading Based on Tangential Wind Speed  
The force of wind acting on a body can be computed by the following formula:  
\[F_{w}=\frac{1}{2}\times\rho\times S\times CD\times v_{w}^{2} \tag{13}\]  
where \(F_{w}\) is a wind force, \(\rho\) is the air density, \(S\) is the area under consideration (projected area), \(CD\) is the drag coefficient, and \(v_{w}\) is the wind speed.  
The assumed area was equal to half of the gable wall and the area of one spacing between the rafters (Figure 14).  
\[A=\frac{1}{2}\times\frac{12\;\text{m}\times 3.5\;\text{m}}{2}+0.4\;\text{m} \times 7.0\;\text{m}=13.3\;\text{m}^{2} \tag{14}\]  
Substituting the values into the formula, we obtain  
\[F_{w}=\frac{1}{2}\times 1.2\;\frac{\text{kg}}{\text{m}^{3}}\times 13.3\;\text{m} ^{2}\times 1\times 55.14\;\frac{\text{m}^{2}}{\text{s}} \tag{15}\]  
We arrive at the value of the force equal to:  
\[F_{w}=24.26\;\text{kN}\]  
Based on the obtained force values, the assembly connection was checked.
###### Abstract  
Numerous remediation strategies exist for cyanobacterial harmful algal blooms (cyanoHABs); however, most are limited by challenges of scalability and adverse off-target effects on the surrounding ecosystem. Germicidal ultraviolet light (UV-C) has emerged as a promising method for suppressing cyanoHABs in a sustainable, chemical-free manner that is both scalable and results in limited off-target ecological effects in the surrounding area. In this study, the US Army Engineer Research and Development Center's (ERDC)'s CyanoSTUN(tm) (Cyanobacterial Suppression Through Ultraviolet-Light-C Neutralization) vessel was deployed to a cyanoHAB as part of a field trial to determine whether UV-C could effectively suppress cellular growth, degrade associated cyanotoxins, and inhibit harmful phytoplankton species more readily than beneficial species without the addition of chemicals. The cyanoHAB exhibited an average cyanobacteria abundance of 3.75 \(\times\) 10\({}^{5}\) cells/mL (\(n\) = 5, SD = 6.76 \(\times\) 10\({}^{4}\) cells/mL) and average total microcystin concentration of 3.5 ug/L (\(n\) = 5; SD = 0.24 ug/L). Pre- and post-treatment samples were collected and re-grown for 9 days in the laboratory to observe differences in microcystin, chlorophyll \(a\), and phycocyanin concentrations, optical density, cell density, and community composition. The results of the field trial showed that the CyanoSTUN UV-C treatment effectively suppressed the growth of the cyanobacteria community for approximately two days at the three tested UV-C doses. The CyanoSTUN UV-C treatment also demonstrated a sustained, dose-dependent effect on microcystin concentration; the average reduction in microcystin concentration for 15, 30, and 45 mJ/cm\({}^{2}\) treatment doses was 31.6% (\(n\) = 10, SD = 20.1%; 1.3 ug/L reduced), 45.7% (\(n\) = 10, SD = 10.8%; 1.9 ug/L reduced), and 49.9% (\(n\) = 10, SD = 8.2%; 1.7 ug/L reduced), respectively, over the 9-day regrowth period. Non-cyanobacteria were too scarce in this CyanoHAB to conclude whether the CyanoSTUN UV-C inhibits harmful phytoplankton species more readily than beneficial species. Further field studies with the CyanoSTUN(tm) are required to validate performance under more severe cyanoHAB conditions, however the results reported herein from the first field trial with the CyanoSTUN(tm) suggest that this treatment method may offer water managers confronted with a CyanoHAB the ability to rapidly and safely pause a bloom for multiple days and reduce the risks posed by its associated cyanotoxins without adding chemicals.  
germicidal ultraviolet light; UV-C; cyanobacteria; cyanotoxin; remediation; sublethal
# Comparative Analysis of Different Model-Based Controllers Using Active Vehicle Suspension System
## 5 Conclusions  
In this paper, active damping control strategies such as FOSMC, ISMC and HOSMC are developed for the quarter-car model to improve the performance for ride ease. The designed sliding mode strategies are compared with LQR, PID and traditional passive suspension system. The performance of each active control is analyzed in terms of vertical displacement, suspension travel and wheel deflection by the execution of simulations using MATLAB/Simulink on the active vehicle suspension system. Also, the performance indices-based performance of controllers are calculated. The quantitative and qualitative analysis verified that the STA-based HOSMC has the ability to diminish the external road disturbances that effects the vehicle stability and provides better control performance to FOSMC, ISMC, PID, LQR and passive suspension system. In future work, this research study can be expanded to design real time optimal nonlinear state feedback control of vehicle suspension to improve the ride ease and vehicle stability.  
Data curation, Y.S.; Investigation, Y.S.; Methodology, Y.S.; Software, Y.S.; Supervision, M.W.; Validation, Y.S.; Writing--original draft, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 51775268 and Jiangsu Innovation Program for Graduate Education grant number KYLX16-0328.  
This work was supported by National Natural Science Foundation of China (Grant No. 51775268), Funding of Jiangsu Innovation Program for Graduate Education (Grant No. KYLX16-0328).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sprung Mass** & **Active Controller** & \(PI\) & \(H_{p}^{RMS}\) & \(\dot{H}_{p}^{RMS}\) \\ \hline \multirow{6}{*}{Body displacement} & Passive & 0.00075 & 0.0222 & 0.7583 \\  & PID & 0.00060 & 0.0209 & 0.6556 \\ \cline{1-1}  & LQR & 0.00054 & 0.01989 & 0.5799 \\ \cline{1-1}  & FOSMC & 0.00043 & 0.01792 & 0.4478 \\ \cline{1-1}  & ISMC & 0.00021 & 0.01298 & 0.3007 \\ \cline{1-1}  & HOSMC & 0.00019 & 0.01156 & 0.2883 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance indices of different proposed active controllers.