esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Automated System for Dual-Aperture Common-Path Interferometer Phase-Shifting
## 2 Operation Principle of a DACPI  
A DACPI is a telecentric 4_f_-imaging system formed by a couple of windows in the object plane and a Ronchi ruling used as spatial filter in the Fourier plane, as it is shown in the schematic of Figure 1. One window acts as a reference arm where a phase object is placed, whereas the other one is the probe arm. In the first section (window stage), a collimated laser beam with wavelength \(\lambda\) propagates through the windows A and B with dimensions determined by \(a_{w}\) and \(b_{w}\) placed in the object plane. In order to match the interference condition, the distance between window centers is \(x_{0}\leq\lambda f/u_{p}\), where \(u_{p}\) is the ruling period and \(f\) is the focal length, determined by the lenses \(L_{1}\) and \(L_{2}\).  
Figure 1: Schematic of a DACPI; MO: Microscope Objective, CCD: Charge-Coupled Device camera.  
The field from the object plane is propagated through \(L_{1}\), then, in the ruling plane, the field is formed by the interfered diffraction orders from both window fields. Considering the impulse response of the spatial filter, the rectangular function for the windows at the image plane (capture stage) is expressed by \(w(x,y)=\text{rect}(x/a_{w})\text{rect}(y/b_{w})\), and the phase difference between the probe and the reference arm is \(\phi(x,y)=\phi(x,y)_{R}-\phi(x,y)_{P}\). The interference pattern due to the superposition of fields from the 0 th order of the probe arm and the 1 st order of reference arm can be described as in [18; 29]:  
\[I_{0j}(x,y)=\frac{1}{\pi^{2}}{A_{R}}^{2}+\frac{1}{4}{A_{P}}^{2}+\frac{1}{\pi} {A_{R}}{A_{P}}\cos\bigl{(}\phi(x,y)-\alpha_{j}\bigr{)}, \tag{1}\]  
where \(A_{R}\) and \(A_{P}\) are the output amplitudes of the beams from the windows and \(\alpha_{j}\) is the phase added to generate \(j\) known phase steps. Therefore, the phase-shifting interferometry (PSI) is based on the generation of additional phases \(\alpha_{j}\) and the capture of intensities for each corresponding phase. In a DACPI, this additional phase can be achieved by transversal ruling translation on the Fourier plane, where the amount of phase added is given by [16; 17]:  
\[\alpha_{j}=2\pi\frac{u_{d}}{u_{p}}, \tag{2}\]  
where \(u_{p}\) is the ruling period and \(u_{d}\) is the linear displacement of the ruling. As the values of \(u_{p}\) and \(u_{d}\) are known, it is possible to generate \(N\) number of steps to apply a self-calibrated algorithm in order to recover the phase \(\phi(x,y)\).
# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 5 Discussion
In GPP projections for the long-term future (such as 2050), earth system models can predict future surface and climate factor data under different emission scenarios and provide strong support for future research in various fields [88, 89, 90]. GPP simulation, on the other hand, also has its applicable situations, for example, the 500 m resolution MOD17A2H. In 006 GPP products ([[https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv006/]([https://lpdaac.usgs.gov/products/mod17a2](https://lpdaac.usgs.gov/products/mod17a2) hv006/), accessed on 15 April 2023), the time resolution is eight days. In 30 m resolution Landsat Gross Primary Production CONUS Products ([[https://www.umt.edu/numerical-terradynamic-simulation-group/project/landsat/landsat-productivity.php](https://www.umt.edu/numerical-terradynamic-simulation-group/project/landsat/landsat-productivity.php)]([https://www.umt.edu/numerical-terradynamic-simulation-group/project/landsat/landsat-productivity.php](https://www.umt.edu/numerical-terradynamic-simulation-group/project/landsat/landsat-productivity.php)), accessed on 15 April 2023), the time resolution is 16 days. These are currently the most commonly used GPP products, but the temporal resolution may still not meet the needs of actual use, and we may not get GPP data until 8 or 16 days later [58, 91]. In this way, we can simulate the latest GPP data using trained machine learning methods based on obtaining the latest impact factor data. In conclusion, this study can not only provide support for future GPP prediction but also provide a reference for historical GPP simulation. On the spatial scale, limited by flux monitoring data, this study was only carried out in the Mongolian Plateau region. The research conclusions are only applicable to the Mongolian Plateau and similar areas. Future research can be developed in two directions. One is to conduct large-scale (such as global, continental, and national) GPP prediction research, and the other is to conduct fine-scale (such as pasture, and farm) GPP prediction research. Large-scale GPP predicting research can better promote global sustainable development goals, achieve global carbon balance, and contribute to the monitoring and management of the global ecological environment [92, 93, 94]. Fine-scale GPP predicting is closer to actual production and can guide grazing, planting, and other industries [95]. Research in these two directions is of great significance and will promote the realization of smarter, greener, and more sustainable development of human society.
## 3 Method
### A Novel Permutation-Based Group Feature Importance Measure
After permuting the values of a group feature \(\mathbf{X}_{i}\), the predicted class of the same \(i\)-th observation is represented by \(\widehat{y}_{i,\pi_{l}}^{(t)}\). The group feature permutation importance \(w^{(t)}\) of \(\mathbf{X}_{l}\) in tree \(t\) is then defined as follows:  
\[w^{(t)}(\mathbf{X}_{l})=\frac{\sum_{i\in\beta^{(t)}}I\left(y_{i}=\widehat{y}_{i}^{ (t)}\right)}{\left|\beta^{(t)}\right|}-\frac{\sum_{i\in\beta^{(t)}}I\left(y_{i }=\widehat{y}_{i,\pi_{l}}^{(t)}\right)}{\left|\beta^{(t)}\right|} \tag{2}\]  
By using Equation (2), the group feature permutation importance \(w\) of \(\mathbf{X}_{l}\) over all trees is then computed as  
\[w(\mathbf{X}_{l})=\frac{\sum_{t=1}^{n_{tree}}w^{(t)}(\mathbf{X}_{l})}{n_{tree}} \tag{3}\]  
The importance score, described in Equation (3), quantifies the impact of the group features on the model's prediction performance. A higher importance score indicates that the group feature contributes significantly to the model accuracy. In contrast, a lower score suggests that the group feature has a lower impact on model performance. We gained valuable insight into the relative importance of each group feature by calculating the permutation importance, enabling us to rank and select the most influential group features for further analysis and model refinement. The pseudocode for this process is presented in Algorithm 1. ```
Input:\(X\) matrix with \(d\) individual features, \(X_{L}\) group features, and \(Y\) response variable Output: vector \(W\)
1 procedureSplit dataset \(X\) into training and testing datasets
2 Train the model on the training dataset and compute the baseline out-of-bag by using a random forest classifier
3 Initialize all group feature score \(w[\mathbf{X}_{l}]=[]\)
4 For\(l=1\) to \(L\) do
5 Compute the permutation importance of \(X_{l}\) in tree \(t\) \(w^{(t)}(\mathbf{X}_{l})=\frac{\sum_{i\in\beta^{(t)}}I\left(y_{i}=\widehat{y}_{i }^{(t)}\right)}{\left|\beta^{(t)}\right|}-\frac{\sum_{i\in\beta^{(t)}}I\left(y =\widehat{y}_{i,\pi_{l}}^{(t)}\right)}{\left|\beta^{(t)}\right|}\)
6 Calculate the overall permutation importance of \(\mathbf{X}_{l}\) \(w(\mathbf{X}_{l})=\frac{\sum_{i\in\pi_{l}}^{n_{tree}}w^{(t)}(\mathbf{X}_{l})}{n_{tree}}\)
7 end for
8 return vector \(W\) of group features score
9 endprocedure
```  
**Algorithm 1**Pseudocode of permutation group importance based on random forest
## 4 Integrated Fixed Time Sliding Mode Control for Motion and Vibration
### Fixed Time Sliding Mode Control Based on Hybrid Trajectory  
Since the fixed time sliding mode control designed in the previous section only ensures that the base and joints track the desired trajectory, but not the flexible links modes can be suppressed. Therefore, this section uses the virtual force concept to generate a hybrid trajectory \(q_{\text{h}}\) that reflects both the rigid desired trajectory and the modal vibration1 and designs a fixed time sliding mode control based on the hybrid trajectory to achieve control of the base joint motion and suppression of the links vibration. Let \(e_{\text{h}}=q_{\text{h}}-q_{\text{d}}\), virtual force \(F\in\mathbf{R}^{3\times 1}\) be generated by the  
Defining state variable \(\mathbf{q}_{\rm s}=\left[\delta^{\rm T},\mathbf{e}^{\rm T},\dot{\mathbf{\delta}}^{\rm T},\dot{ \mathbf{\epsilon}}^{\rm T}\right]^{\rm T}\), according to Equations (37)-(40), we have:  
\[\dot{\mathbf{q}}_{\rm s}=\mathbf{A}_{\rm s}\mathbf{q}_{\rm s}+\mathbf{B}_{\rm s}\mathbf{F}+\mathbf{L}_{ \rm s}. \tag{41}\]  
where \(\mathbf{A}_{\rm s},\mathbf{B}_{\rm s},\mathbf{L}_{\rm s}\) are the known coefficient matrices.  
The virtual force \(\mathbf{F}\)[24] is designed as follows:  
\[\mathbf{F}=-\mathbf{R}_{\rm s}^{\rm-1}\mathbf{B}_{\rm s}^{\rm T}\mathbf{P}_{\rm s}\mathbf{q}_{\rm s}, \tag{42}\]  
where \(\mathbf{R}_{\rm s}\) is the weighting matrix corresponding to \(\mathbf{F}\) in the performance index function, \(\mathbf{P}_{\rm s}\) is the solution of the corresponding Riccati equation.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 1 Introduction
Moreover, the IEEE 802.11p only allows the use of four pilot subcarriers (intended for residual frequency offset correction). These pilot subcarriers are not spaced closely enough to sample the variations of the channel in the frequency domain, which also contributes to performance degradation in V2V communications. The main motivation behind the proposed scheme is to track the channel variations in time and frequency domain. In [5], authors have investigated that channel estimation in the IEEE 802.11p standard can be improved by exploiting the correlation characteristics between the adjacent symbols of the orthogonal frequency-division multiplexing (OFDM) based data packet via constructing data pilots in the time domain. In this article, we have investigated that the adjacent subcarriers also have strong correlation characteristics in a data packet. We have employed these correlation characteristics in time and frequency domain to construct the reference data pilots for the channel estimation. In this way, an improved channel equalization can be achieved with a little complexity. The main contributions of this paper are listed here:  
* We have proposed an end-to-end channel estimation and equalization scheme for the IEEE 802.11p standard. It does not require modifications in the structure of the standard and keeps a balance between computational complexity and BER performance of the overall system. * In the channel estimation process, we have also utilized the correlation characteristics between adjacent subcarriers in the frequency domain, as well as between adjacent OFDM symbols in time domain. * The simulation results have demonstrated the performance improvement over CDP and spatial temporal-averaging (STA) schemes for V2V communications. * We have also presented an intuitive visualization of the V2V channel model which is used in the evaluation of the proposed scheme. The paper is organized as follows. Section 2 describes the current system model of the IEEE 802.11p transmitter and the channel. Section 3 gives an overview of the current channel estimation schemes for the IEEE 802.11p. Section 4 briefly describes the receiver and the integration of the proposed scheme. Section 5 presents the simulation results and analysis of the proposed iCDP and Section 6 discusses two important issues along with future work suggestions. Finally, Section 7 draws the main conclusions derived from this work.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 8 Results
### Performance on Uhotica CogniSAT-XE1
(Top) Cloudy images (**a–c**) are misclassified as _not cloudy_ before domain adaptation but are correctly classified as _cloudy_ after domain adaptation. (Bottom) Non-cloudy images (**d–f**) are misclassified as _cloudy_ before domain adaptation but are correctly classified as _not cloudy_ after domain adaptation. (**a**) FISH Mask, DUA and Tent. (**b**) Fish Mask and DUA. (**c**) Fish Mask and Tent. (**d**) FISH Mask. (**e**) DUA. (**f**) Tent.
## 2 Image Quality Evaluation Results  
### Image Quality Evaluation Metric  
We measured the modulation transfer function (MTF) to quantitatively evaluate the degradation of image quality when the camera lens surface is contaminated by single and multiple droplets. The MTF is a key metric for evaluating how well an imaging system reproduces details (spatial frequency) and represents the overall spatial resolution of an image, including sharpness, contrast, and resolution. Specifically, this study utilized the slanted-edge method to measure the MTF. This method, standardized under ISO 12233, is widely used for image quality assessment due to its straightforward setup and high accuracy [37].  
MTF50 is a commonly used metric for evaluating image sharpness, representing the spatial frequency (typically expressed in line pairs per millimeter or pixels per cycle) at which the image reaches 50% of its original contrast. A higher MTF50 indicates better image quality. To compare the image quality of a camera sensor before and after contamination, the difference in MTF50 between the uncontaminated and contaminated states serves as an effective measure. Therefore, we utilized the MTF50\({}_{\text{loss}}\) metric, as proposed by [PERSON] et al., to evaluate the impact of various contaminants on a camera lens surface [34]. MTF50\({}_{\text{loss}}\) is defined as the difference between MTF50\({}_{\text{clean}}\) (MTF50 when the lens is clean) and MTF50\({}_{\text{contamination}}\) (MTF50 when the lens is contaminated), as shown in Equation (1). The greater the MTF50\({}_{\text{loss}}\), the more severe the image quality degradation.  
\[\text{MTF50}_{\text{loss}}=\text{MTF50}_{\text{clean}}-\text{MTF50}_{\text{ contamination}} \tag{1}\]
## 6 Conclusions  
In this paper, we have demonstrated the proof-of-principle of a new technique to determine the isotopic ratios of hydrogen isotopes of interest for magnetic fusion research. In this technique, we combined simple spectral characteristics of the Balmer-\(\alpha\) line emitted by hydrogen-deuterium gas mixtures with a deep learning regression algorithm adopted from the open-access machine-learning platform TensorFlow with Keras as the Python API. The demonstration was based on results obtained with H\(\alpha\)/D\(\alpha\) line profiles which we have computed varying a number of parameters such as the strength of the magnetic-filed B, the temperatures and fractions of the cold and warm populations of neutrals, reflecting the recycling mechanisms in tokamak divertors, and the concentration of hydrogen (in the range 1-25%). These parameters were varied to represent typical conditions of tokamak divertors. Instead of fitting whole spectra, only three parameters of each synthetic profile were used and transmitted to the regression algorithm for training: a wavelength separation between the unshifted wavelength of the H\(\alpha\) line and the blue-shifted \(\sigma^{-}\) of the D\(\alpha\) line, and dip-to-peak and peak-to-peak line intensity ratios. It has been shown that when the analyzed profiles don't contain noise, the regression algorithm predicts very accurately the expected isotopic ratios. However, when noise was introduced in the synthetized profiles, the predicted values were less accurate. The dispersion of the predictions around the expected values is not due to the deep learning treatment of the features provided to it but is attributed to the uncertainty in the determination of the spectral minima allowing to calculate the ML features. In other words, the error in the determination of the feature concerning the wavelength separation impacts the predictions of the deep learning algorithm. The next step of this work will be its application to available experimental data from tokamaks operating with H-D mixtures, and later for D-T plasmas.  
[PERSON]. ([PERSON]) developed the code to synthetize Zeeman-Doppler broadened H\(\alpha\)/Da line profiles; [PERSON]. ([PERSON]) developed the preprocessing program to extract the spectral features and adapted the deep-learning regression algorithm from TensorFlow-Keras to predict hydrogen isotopic ratios. [PERSON]. ([PERSON]) supervised the work, wrote the manuscript. [PERSON]. ([PERSON]), reviewed the manuscript before its submission. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the A*Midex IDEX, PIA AMX-19-IET-013 (Master's internship of [PERSON]) as well the French Federation of Research on Magnetic Fusion (FR-FCM), Project 3 IPP_AAPFR2022_[PERSON].  
The authors declare no conflict of interest.
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 3 Results and Discussion
#### 3.1.1 Phenolic Acids
Moreover, by considering the mass differences of 18 and 86 Da from that of 16, the nodes with precursor ions [M \(-\) H]\({}^{-}\) at \(m/z\) 383.059 and 487.146 were proposed to be crotonylated (C\({}_{4}\)H\({}_{4}\)O, 68 Da) (**9**) and dimalonylated (C\({}_{6}\)H\({}_{4}\)O\({}_{6}\), 172 Da) (**46**) derivatives of dihydroxybenzoic acid hexoside, respectively. Examination of their MS/MS spectral data showed that they possessed fragmentation patterns that are compatible with the characteristic fragment ion of dihydroxybenzoic acid acyl hexoside (Figures S4 and S5); these were based on the fragment ions at \(m/z\) 315 for [(M\(-\)H)-acyl]\({}^{-}\), 153 for [(M\(-\)H)-acyl-162]\({}^{-}\), 152 for [(M\(-\)H)-acyl-162-H]\({}^{-}\), and 109 for [(M\(-\)H)-acyl-162-CO\({}_{2}\)]\({}^{-}\), resulting from the loss of the acyl hexoside moiety, followed by decarboxylation of the carboxylic acid group.
# Preliminary Examination of Emergent Threat and Risk Landscapes in Intelligent Harvesting Robots
## 3 Intelligent Harvesting Machines
* **Intelligent robot cloud service provider**: The harvesting robot uses cloud services where most of the AI modules and intelligent features reside for further analysis to improve the tasks it performs. * **Base station**: A GPS receiver that collects GPS measurements of the field. It consists of an antenna, a GPS receiver, and a device (often a personal computer) to which the GPS data is logged. Note that the robot also has a GPS module that collects GPS coordinates from the satellite in order to control its movement, but the difference is that a base station provides reference data that can be used to increase the accuracy (to within a few centimeters or less) of GPS data collected in the field. The Operator in Figure 1 can interact with the IHR and the AMB by accessing their physical interfaces (this communication is depicted in Figure 1 with dashed lines and labelled (C1) and (C1)) or remotely by sending commands through a client application or a smartphone app (C2 + C3). The IHR essentially executes two tasks in parallel: it collects localisation data from the base station and the GPS satellite (C5 + C6) and also uses image processing to identify crops that are ready for harvesting using the Automatic Crop Recognition task. As soon as a mature plant is detected, access to GPS coordinates is immediately cut off. The robot then moves to the crop to start the harvesting process. Harvesting is conducted by sending a signal to the motor controlling the harvesting blades using the Eye-Hand Coordination task. The blades start as well as the conveyor belt. When the crops are harvested, they are also being transferred into a basket simultaneously. The AMB follows the harvesting robot and continuously checks the IHR's basket (C4). Once the basket is full, the AMB removes it and provides an empty basket. Finally, the AMB will transfer the full basket to the warehouse. Most of the IHR's intelligent features are developed and maintained in the cloud. For example, the performance of the Automatic Crop Recognition task mentioned before is improved by sending crop images to the Machine Vision System (C12 + C10) in the cloud to train the AI-based model that the robot is using, which then generates the model weights and sends them back to the IHR for inference. The intelligent cloud service provider could also use third party cloud services to enhance the performance of its intelligent modules (C1). An example could be sending the images collected by the IHR to a third-party cloud tool to improve their quality or perform data labeling before sending them to the Machine Vision System.
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 3 Evaluation Methodology
### Video Multi-Method Assessment Fusion (VMAF)  
Developed by Netflix [26], VMAF is another FR perceptual image/video quality assessment metric that combines human vision modeling with machine learning. This metric outputs a single score by fusing multiple objective quality features. The fusion model is based on Support Vector Machine (SVM) regression, and it is derived using machine learning over a set of subjective test results. VMAF scores range from 0 to 100, with 100 indicating the best possible quality. A good way to think about a VMAF score is to linearly map it to the human opinion scale.
###### Abstract  
Phlogopite is a crucial indicator for effectively constraining the magmatic evolution and emplacement mechanism of kimberlite. In this study, samples were collected from the No. 110 kimberlite pipe within diamond belt I in the southern Liaoning diamond melting area in the eastern North China Craton (NCC). Zonation is highly developed in the phlogopite; the major and trace element compositions of the phlogopite zonation in the samples were analyzed. In this study, phlogopite from the No. 50 pipe kimberlite (#50 phlogopite) zonation is divided into the following components: (1) The cores, low Ti-Cr xenocryst, average Mg\({}^{\#}\) = 90.6, has a resorption structure, the presence of serpentine and talc minerals in low Ti-Cr cores (xenocrysts) can be used as evidence for hydrothermal metasomatis; (2) cores/inner rims (between core and outer rim), high Ti-Cr, it is thought to be related to the assimilation of mantle materials by deep kimberlite magma, average Mg\({}^{\#}\) = 88.2; (3) outer rims, low-Cr/Cr-poor, average Mg\({}^{\#}\) = 82.4, Fe, Al and Ba contents increased, and there was a trend of evolution to biotic composition believed to be related to the metasomatic metamorphism of melt and wall rock during the late magmatic evolution or ascent; (4) rinds, it is characterized by re-enrichment of Mg, rind I (low-Ti-Cr, average Mg\({}^{\#}\) = 88.4), rind II (high-Ti-Cr, Mg\({}^{\#}\) = 88.6), rind II may be formed earlier than rind I. Rind is very rare and has been reported for the first time in southern Liaoning kimberlite. This study was only accidentally found in the outermost part of #50 phlogopite, the Mg-rich feature represents an environment in which oxygen fugacity has increased. The phlogopite in samples from pipe No. 110 (#110 phlogopite) exhibits relatively homogeneous characteristics across different zones and is more enriched in Al and Ba, which is likely the result of mantle metasomatis. Due to its eucheral characteristics and composition variation, it is considered that #110 phlogopite is more likely to be derived from direct crystallization from magma than from xenocrysts. In addition, based on the simultaneous enrichment of Al and Fe in phlogopite from the core to the outer rim, pipe No. 50 was determined to be a micaceous kimberlite, while pipe No. 110 more closely resembles group I kimberlite. This paper proposes that successive pulses of kimberlite magma emplacement gradually metasomatized the conduit, and subsequent kimberlite magma ascended along the metasomatized conduit, thereby minimizing the interaction between the later magma and the surrounding mantle lithosphere.  
phlogopite zone of kimberlite; magmatic evolution; emplacement mechanism +
Footnote †: journal: Applied Sciences