esa-sceva/satcom-chunk-collection huggingface dataset:
# Initial Assessment of the LEO Based Navigation Signal Augmentation System from Luojia-1A Satellite
## 4 In-Orbit Performance Evaluation Results
### Handling Error Sources of the Ranging Measurements
The precision of the residuals can be treated as the empirical user equivalent ranging error (UERE), which means that Luojia-1A satellite is capable of providing 3.62 m precision ranging signals in real-time for the navigation users. The carrier phase measurement achieves 0.22 m since standard temperature compensate oscillator (TCXO) with 0.05 ppm stability is used. With more precise receiver clock applied, e.g., the higher performing OCXO or chip-scale atomic clock (CSAC), the real carrier phase measurement accuracy can be assessed. There is no hardware biases presence in the carrier phase residuals since these biases have been calibrated along the ambiguities. The figure indicates that the carrier phase measurements are less noise than the pseudorange measurements, meaning that the adverse impact of multipath can be mitigated with the carrier phase smoothed technique for the LEO satellites. The figure also indicates that the pseudorange and the carrier phase measurements generated by Luojia-1A satellite are can be processed with current GNSS data processing theory. Therefore, they are suitable for navigation augmentation applications.
## 3 Results  
### Variations in Leaf Area Index (LAI) Measurements in 2015 and 2016  
As can be observed from the summary statistics of the field-measured LAI (Table 1), the LAI values in 2016 had a larger range in comparison to the measured LAI data from 2015. In 2015, the mean LAI was 2.58 m\({}^{2}\)/m\({}^{2}\) while the mean LAI for data obtained in 2016 was 3.31 m\({}^{2}\)/m\({}^{2}\). The measured LAI from both years showed relative high variability, while the sampled LAI in 2015 demonstrated slightly higher variation (coefficient of variation = 0.42) compared to those in 2016 (coefficient of variation = 0.40). Similar trends were observed for the measured chlorophyll, where the range was smaller in 2015 compared to those from 2016. The range of other measured variables from the two years showed relatively little variation (data not shown).  
The results of the sensitivity analysis of PROSAIL for LAI estimation using RapidEye and Sentinel-2 spectral band settings is presented in Figure 4. As was expected, LAI is exerting a strong influence on the simulated canopy reflectance for both sensors. Almost all RapidEye spectral bands demonstrated sensitivity to LAI variation. For Sentinel-2 also, the sensitivity of all spectral bands are underlined in the figure, although it seems that that LAI's influence on the visible and NIR spectral bands is more pronounced. The simulated canopy reflectance of both sensors was largely affected by all other model input parameters (not shown), except for the leaf water content (Cw) which displayed no influence on the simulated canopy reflectance spectra of RapidEye and only influenced the NIR (lightly) and mostly the SWIR spectral bands of Sentinel-2. For this reason, leaf water content was fixed to a constant value (Cw = 0.01, g cm\({}^{-2}\)) for the generation of RapidEye LUT only, but not for Sentinel-2.
## 3 Results and Discussion
### Raman Spectroscopy  
Figure 5 shows the comparison of the full spectra of PLA spool material and printed PLA. The spectra look very similar and show minimal intensity changes. Peak assignments were made using Socrates, Suzuki, and Kister [22; 23; 24], respectively. Peaks similar in all spectra include: CH\({}_{3}\) vibrations assigned to asymmetric stretching at 2995-3002 cm\({}^{-1}\) and, symmetric stretching 2944-2947, and 2878-2889 cm\({}^{-1}\). CH stretching/C=O stretching at 1769-1773 cm\({}^{-1}\), CH\({}_{3}\) symmetric deformation vibration at 1454-1456 cm\({}^{-1}\), CH\({}_{3}\) rocking vibration assigned to 1128 cm\({}^{-1}\), C-O-C stretching assigned to 1095-1098 cm\({}^{-1}\), C-CH\({}_{3}\) stretching at 1042-1046 cm\({}^{-1}\), C-CO vibration assigned to 873 cm\({}^{-1}\), (possibly due to the presence of CaCO\({}_{3}\)), C=O stretching assigned to 742 cm\({}^{-1}\), C-CO vibrational states assigned to 399-408 cm\({}^{-1}\), and C-O-C + C-CH\({}_{3}\) vibration assigned to 302-315 cm\({}^{-1}\).  
Intensity changes following printing are seen in the highlighted regions of Figure 3, including changes in the CH\({}_{3}\) deformation vibration at 1383-1387 cm\({}^{-1}\), and the CH deformation vibration peak at 1299-1305 cm\({}^{1}\). The light blue highlighted region of Figure 3 shows an increase in the intensity of the C=O deformation peak at 742 cm\({}^{-1}\) after printing the PLA. This suggests that the printing process of PLA increases the relative abundance of the C=O ligand, considered a primary active binding site in PLA. At the moment we suspect that calcium in the form of calcium carbonate may be responsible for the peaks assigned to 679 cm\({}^{-1}\), 873 cm\({}^{-1}\), and 1085 cm\({}^{-1}\), all of which are present in the spool  
Figure 5: Raman spectroscopic comparison of PLA spool material, printed PLA.  
material and printed PLA test samples. It will be shown below that spectral features associated with calcium-related groups are also present in the FTIR spectra of the spool material and the printed PLA.
# Machine Learning Sequential Methodology for Robot Inverse Kinematic Modelling
###### Abstract  
The application of robots is growing in most countries, occupying a relevant place in everyday environments. Robots are still affected by errors due to their limitations, which may compromise the final performance. Accurate trajectories and positionings are strict requirements that robots have to satisfy and may be studied by the inverse kinematic (IK) formulation. The IK conventional numerical techniques are computationally intensive procedures, focusing on the robot joint values simultaneously and increasing the complexity of the solution identification. In this scenario, Machine Learning strategies may be adopted to achieve effective and robust manipulator's IK formulation due to their computational efficiency and learning ability. This work proposes a machine learning (ML) sequential methodology for robot inverse kinematics modeling, iterating the model prediction at each joint. The method implements an automatic Denavit-Hartenberg (D-H) parameters formulation code to obtain the forward kinematic (FK) equations required to produce the robot dataset. Moreover, the artificial neural network (ANN) architecture is selected as a structure and the number of layers in combination with the hidden neurons per layer is defined by an offline optimization phase based on the genetic algorithm (GA) technique for each joint. The ANN is implemented with the following settings: scaled conjugate gradient as training function and the mean squared error as the loss function. Different network architectures are examined to validate the IK procedure, ranging from global to sequential and considering the computation direction (from end-effector or from basement). The method is validated in the simulated and experimental laboratory environment, considering articulated robots. The sequential method exhibits a reduction of the mean squared error index of 42.7-56.7%, compared to the global scheme. Results show the outstanding performance of the IK model in robot joint space prediction, with a residual mean absolute error of 0.370-0.699 mm in trajectory following 150.0-200.0 mm paths applied to a real robot.  
inverse kinematics solution; machine learning; Denavit-Hartenberg modeling; sequential algorithms; robotics +
Footnote †: journal: Applied Sciences
# Development and Prospect of UAV-Based Aerial Electrostatic Spray Technology in China
## 3 Rapid Progress of UAV-Based Electrostatic Spray Technology
### Spray Efficiency Experiments of UAV-Based Electrostatic Spray Technology  
[PERSON] et al. [45] designed a bipolar contact oil-powered single rotor aerial electrostatic spray system for plant protection UAVs. The static electricity system was used to spray the static electricity oil agent and the conventional water-based chemical agent. Besides, the spray droplet deposition distribution and the control effect of wheat aphid and rust were tested. The result showed that the deposition per unit area was 0.0486 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{c}\mathrm{m}^{2}\), the standard deviation of deposition amounts was 0.015 \(\mathrm{\SIUnitSymbolMicro}\mathrm{g}\mathrm{/}\mathrm{c}\mathrm{m}^{2}\), and the coefficient of variation was 30.43%. The distribution uniformity of droplet deposition is obviously better than the other two treatments, and the prevention and control of diseases and insect pests in the wheat field showed a good control effect. The aphid control effect was 87.92% on the seventh day after spraying, which was significantly higher than that of the conventional spray treatment (76.43% with the electrostatic oil agent and 66.47% with the conventional water-based agent). [PERSON] [46] designed a high-voltage electrostatic generator with smaller volume and mass based on the optimized structure parameters of the cone-shaped electrode and the determined dosage form of the special electrostatic liquid agent for aviation. It was more suitable for plant protection UAVs, aimed at the current problems such as the poor charging effect of droplets, poor applicability of aerial spraying agents, complex high-voltage electrostatic generator system, and heavy high-voltage electrostatic generator. In addition, a set of electrostatic spray systems that can be applied to six-rotor and single-rotor plant protection UAVs was developed, as shown in Figure 5. The system was mounted on the plant protection UAV and carried out the experiment in the cotton and rice-growing areas of Changji, Xinjing, Shihezi, Xinjing, and Ledong, Hainan. The results of spraying operation of hybrid breeding rice growth regulator and defoliating cotton agent by plant protection UAV showed that when spraying rice growth regulator by plant protection UAV, the effect of electrostatic spraying could be up to 20% higher than that of non-static spraying, and the effects of spraying different chemicals were different. When sprayed the cotton defoliant, the coverage rate of droplet increased by 140%, the defoliation efficacy increased by 12.22%, and the cotton boll opening rate increased by 18.55%.
## 4 FWA Design Considerations, Challenges, and Solutions
### Lessons Learned  
FWA networks are envisaged to leverage the 5G technologies for delivering multi-gigabit internet speeds. Nonetheless, 5G FWA implementation will not only present a number of technical challenges, but will also pose performance issues that will demand concerted effort and considerable research attention. The operators have to consider the system requirements for different deployment scenarios to choose between the lower and higher bands for transmission. Furthermore, a scheme like a channel aggregation can be exploited to enhance the system throughput accordingly. The lower bands can be employed for applications such as backup connections [13], M2M connectivity, and smart metering, in which high data rates are not important for effective system operation [4]. Moreover, innovative technologies should be adopted to address the limitations of mm-wave employment [5; 9; 69]. Therefore, for effective enhancement of the QoE, schemes such as spatial multiplexing, diversity, and wireless multihop network cooperative transmission can be implemented. This will not only help in enhancing the signal robustness to the propagation environments [284], but will also offer an improved reliability [285; 286; 287; 288].  
In addition, cell densification can also be used to improve network penetration, efficiency, and reliability. The associated cost for densification can be alleviated with the implementation of advanced antenna technologies like AAS and massive MIMO in which BF techniques and advanced signal processing are exploited. Note that a hybrid BF approach seems to be more promising [264; 307]. An optical beamforming architecture with some salient features such as huge bandwidth, potential fast-switching, and immunity to EMI can also be employed for fixed and mobile broadband access networks that operate at the mm-wave band [93].  
Furthermore, for flexible deployment and optimal implementation of 5G FWA solution, hybrid terminal antenna placement (rooftop, wall-mounted and indoor) is very imperative [5; 69; 261]. Moreover, the roll-out success and realization of 5G FWA depend on favorable policies from the governments and regulatory bodies [9; 327; 328]. It is remarkable that, apart from the required improvement in the system reliability and scalability, interoperability between the 5G FWA and the existing network such as 4G is another essential factor for cost-effective deployment and wide adoption of 5G FWA systems. These factors can be facilitated with the adoption of the 3 GPP specifications [5].
## 5 Application
### Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & \(\mathbf{C_{c1}}\)**-\(\mathbf{C_{a1}}\) & \(\mathbf{C_{c1}}\)**-\(\mathbf{C_{a2}}\) & \(\mathbf{C_{c1}}\)**-\(\mathbf{C_{a3}}\) & \(\mathbf{C_{c1}}\)**-\(\mathbf{C_{a4}}\) & \(\mathbf{C_{c1}}\)**-\(\mathbf{C_{a5}}\)** \\ \hline Gray correlation & \(0.6959\) & \(0.6058\) & \(0.3800\) & \(0.3542\) & \(0.4591\) \\ New gray absolute correlation & \(-0.7511\) & \(-0.3119\) & \(-0.3048\) & \(0.0489\) & \(-0.7379\) \\ Gray H-convex correlation & \(-0.7016\) & \(-0.4138\) & \(-0.3599\) & \(-0.2024\) & \(-0.8546\) \\ Improved gray H-convex correlation & \(-0.9394\) & \(-0.4928\) & \(-0.4976\) & \(-0.4617\) & \(-0.9831\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results from different models, taking the calculation of the correlation between fixation duration and interface layout aesthetics as an example. with simplicity. These results are consistent with the trends identified in the analyses of fixation duration and mean pupil diameter discussed previously.
# Optical Wireless Communications Using Signal Space Diversity with Spatial Modulation
## 1 Introduction
### 2 \(\times\) 1 MISO OWC System Configuration and Spatial Modulation Implementation  
Before introducing the SSD scheme, a brief description of the MISO indoor OWC system configuration and the SM implementation is provided here as the background.  
The 2 \(\times\) 1 MISO OWC system configuration was investigated in this paper to achieve transmitter diversity along with SM in the indoor scenario, as illustrated in Figure 1a. Optical signals from two transmitters (\"multiple inputs\") are conveyed over independent OWC channels at different time slots using the SM. Due to the optical beam spread, part of the transmitted optical power is received at a single receiver (\"single output\"). Beam steering and transmitter activation are centralized-controlled via the fiber distribution network to ensure optical signals from both transmitters can reach the receiver at different time slots.  
For the SM implementation, the spatial bit for SM, as shown in Figure 1b, is encoded into the index of a transmitter, which controls the transmitter activation. Here, as two transmitters are deployed, one bit is used as the spatial bit, where the bit \"0\" and the bit \"1\" represent the activation of Transmitter 1 (T \(\times\) 1) and Transmitter 2 (T \(\times\) 2), respectively.
## 3 Mathematical Modeling and Design Methodology
### Modeling of a PV Array Output Power Generation
#### 3.1.1 ISD Model  
This model is the most simplified form of an ideal PV module as shown in Figure 4. The output current as a function of the voltage is expressed by (1) [28]:  
\[I=I_{Ph}-I_{o}\left(e^{(\frac{V}{AV_{T}})}-1\right) \tag{1}\]  
Figure 4: Equivalent circuit of a photovoltaic (PV) panel using ideal single diode (ISD).  
However, this model does not provide accurate and realistic I-V and P-V characteristics. This is because Equation (1) does not represent the actual characteristics due to neglecting the two diode losses. Actual PV modules are composed of several connected PV cells, in series with losses associated with leakage currents to the ground. These losses are modelled by a series resistance (\(R_{s}\)) and a shunt resistance (\(R_{sh}\)), which will be presented in Section 3.1.2.
## 3 Proposed De-Noising Algorithm and Evaluation Method
### Performance Evaluation Method  
The performance of this algorithm was evaluated in the time domain and frequency domain. Four well-known de-noising algorithms were used for comparison with our algorithm in the experiments: wavelet transform, VMD, RPCA, and WRPCA, introduced in the previous section. In this paper, the maximum number of iterations was set to 1000.  
Time domain analysis for extracted signals focuses on comparing low-rank matrices before and after de-noising by different de-noising algorithms. Four examples in channels with different energy (maximum, medium, low, and minimum) are given in this section. We also employ time domain analysis for extracted noise, which gives us an intuitive impression of the noise amplitude.  
Then, non-deviation errors of key physiological points are evaluated. Figure 4 shows the positions of the key physiological points of the radial pulse wave in a complete heartbeat cycle. In the beginning, the aortic valve opens (onset) as the ventricular pressure rises. The ventricle continues to contract, and the systolic pressure rises to a peak (P1) during ejection [37]. Due to the diastole of the ventricle and backward wave from periphery branches, the attenuated arterial pressure reaches the second peak (P2) of the pulse wave [38]. The closure and opening of the arterial valve cause the dicrotic notch (P3) and diastolic wave (P4) [27]. In clinics, the augmentation index (Aix) is formulated as \(h_{2}/h_{1}\), which is an important metric of arterial stiffness estimation [39]. The positions and amplitudes of these key points should not be affected after de-noising, which is the primary premise of all de-noising algorithms. Thus, the time absolute error (TAE) and amplitude relative error (ARE) at key points P1 and P2 are used to evaluate the non-deviation performance, defined as follows:  
\[TAE=|t_{i}-t_{oi}|, \tag{9}\]  
\[ARE=\frac{|h_{i}-h_{oi}|}{|h_{oi}|}\times 100\%, \tag{10}\]  
where \(t_{i}\) or \(t_{oi}\) is the sampling number of processed or original \(i\)-th key points, and \(h_{i}\) or \(h_{oi}\) is the amplitude of processed or original \(i\)-th key points.  
Frequency-domain analysis for extracted noises uses power spectral density (PSD) to show the distribution of noises. A reference noise is recorded using a PPS 5 \(\times\) 5 tactile sensor. Noise-dominant frequency bands of PPS tactile sensors are compared with extracted noises by five de-noising algorithms.  
Furthermore, run-time complexities are tested in this paper. The total calculation amount and run time required by our algorithm must be at acceptable levels for practical de-noising applications. A host PC can be the recommended experimental environment for our study. The host PC was equipped with four Intel Xeon Platinum 2.10 GHz CPUs, 384 GB memory, and an Inventec KQ80G4 Motherboard (C620 series chipset running Windows Server 2016 Datacenter). All programs were run in Matlab2020b.
###### Abstract  
Compared with traditional liquid crystal and organic light emitting diode (OLED), micro light emitting diode (\(\mu\)LED) has advantages in brightness, power consumption, and response speed. It has important applications in microelectronics, micro-electro-mechanical systems, biomedicine, and sensor systems. \(\mu\)LED massive transfer method plays an important role in these applications. However, the existing \(\mu\)LED massive transfer method is faced with the problem of low yield. To better transfer the \(\mu\)LED, the force value detached from the substrate needs to be measured. Atomic force microscope (AFM) was used to measure the force of a single \(\mu\)LED when it detached from the substrate. The \(\mu\)LED was glued to the front of the cantilever. When a single \(\mu\)LED was in contact with or detached from the Polydimethylsiloxane (PDMS), the maximum pull-off force can be obtained. The force at different peel speeds and preload was measured, and the experimental results show that the separation force between a single \(\mu\)LED and PDMS substrate is not only related to the peel speeds, but also related to the preload. The force values under different peel speeds and preload were measured to lay a theoretical foundation for better design of \(\mu\)LED massive transfer system.  
AFM; \(\mu\)LED; mass transfer; adhesive force measurement +
Footnote †: journal: Applied  
[PERSON] developed an instrument based on capacitive force sensor to measure the dynamic adhesion between rough surfaces [12]. [PERSON] proposed a new instrument to measure the dynamic adhesion of the interaction surface on the flexible substrate, and proposed an optimal adhesion control strategy based on the analysis of adhesion [13]. In 2017, [PERSON] used strain gauge load cell to measure the normal adhesive force of OLED [14]. [PERSON] used a high-resolution, high-dynamic bandwidth capacitive force transducer and two piezoelectric actuators to measure adhesive pull-off forces between nominally flat rough silicon surfaces under various dynamic conditions [15]. [PERSON] used Atomic Force Microscope (AFM) to measure the adhesion between the colloidal probe and silicon wafer. Two spherical colloids made of silicon dioxide and gold that were attached to an AFM cantilever were approached to and retracted from a silicon wafer specimen [16].  
However, at present, none has been found in the literature regarding adhesion test between a single \(\mu\)LED and the substrate and most of the experiments demonstrate the adhesion between a large area of PDMS and \(\mu\)LED array, since it is hard to attach a single \(\mu\)LED to the force sensor. In this paper, based on AFM, the adhesion between a single \(\mu\)LED and the substrate was measured using cantilever, and the relationship between peel speeds, preloads, and adhesion was evaluated [17; 18]. In Section 2, the theoretical relationship between pull-off forces to peel speeds and preload will be deduced. In Section 3, the theoretical results will be verified experimentally.