esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Surface Morphology and Chemical Composition
In this case, as seen from the SEM micrographs of the deposited material, the substrate detection indicates an increase in the porosity, thus, the electron beam not only reaches the film but also the substrate through the formed pores. In the studied conditions, all the films have approximately the same thickness, the deposition rate being similar. Therefore the increase in the Si peak can only be related to the exposure of the substrate through the porosity of the film. Relative values of the measured elements can be obtained from the EDS measurements. Before the analysis, the device is semi-automatically calibrated using a standard AlCu sample; the operating software collects a spectrum checking the relative intensity of Al K\(\alpha\) and Cu K\(\alpha\) peaks and their corresponding energies and makes adjustments for different amplification times for fine calibration using an automatic procedure. From the spectra presented in Figure 4, a quantification of the elements or oxide ratios can be performed automatically by the software (TEAMS ver. 4.1, EDAX Inc., Mahwah, NJ, USA). For the studied conditions, the average relative elemental concentrations estimated from the spectra are also shown in Figure 4.  
Figure 3: SEM images of ZnO thin films for different deposition temperatures.
## 2 Materials and Methods
### LC-MS/MS Apparatus and Conditions for the Simultaneous Quantification  
An ACQUITY UPLC H-Class (Waters, Milford, MA, USA) LC system coupled to TQ-S micro triple quadrupole mass spectrometer (Xevo, Milford, MA, USA) with an electrospray ionization (ESI) source was used for simultaneous analysis of the 14 marker components in SDGT. The ACQUITY UPLC BEH C\({}_{18}\) analytical column (2.1 \(\times\) 100 mm, 1.7 \(\upmu\)m) maintained at 40 \({}^{\circ}\)C was used for separation of the components. Mobile phases were (A) aqueous 0.1% (_v/v_) FA and (B) acetonitrile, with gradient elution: 10% B at start-1.0 min, 10-40% B at 1.0-6.0 min, 40-95% B at 6.0-10.0 min, 95% B at 10.0-15.0 min, 95-10% B at 15.0-15.1 min, and 10% B at 15.1-18.0 min. The flow rate for separation and detection was 0.3 mL/min and the injection volume was 2.0 \(\upmu\)L. The temperature of the prepared sample was maintained at 5 \({}^{\circ}\)C. The 14 marker components were detected in positive and negative ion modes of the LC-MS/MS multiple reaction monitoring (MRM) mode. The optimized LC-MS/MS MRM parameters (collision energy, cone voltage, and transition) are presented in Table S2. Other parameters were: capillary voltage, 1.2 kV; source temperature, 150 \({}^{\circ}\)C; desolvation temperature, 450 \({}^{\circ}\)C; desolvation gas flow, 800 L/h; cone gas flow, 50 L/h. All data were processed using Waters MassLynx software (Version 4.2; Waters, Milford, MA, USA).
# Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
## 3 Results and Validation  
The CYGNSS dataset became available in March 2017, and the experiments in this work used the V2.1 version of CYGNSS level 1 data downloaded from the Physical Oceanography Distributed Active Archive Center (PO.DAAC). In the process of forming GMF, the RCG of samples is required to  
Figure 3: Distribution of \(P_{wind}\) as a function of incidence angle (unlabeled colored lines indicate the probability density distribution of wind speed at each incident angle bins).  
be greater than 10 to ensure the quality of the GNSS-R observations. The training dataset used was from 1 July, 2017 to 30 November, 2017. Data collected in December 2017 were used as a test dataset. The inversed results were compared with the NWP-based and CCMP-based wind, also evaluated by the buoy data. Figure 4 shows the distribution of specular point tracks of the 8 CYGNSS satellite on 1 st December 2017 and the distribution of the used moored buoys, which are indicated by the red dot.
## 1 Introduction
if the reports of GSL components in these species are incomplete. This may be due to a lack of sensitivity in reported mass spectrometry methods or because there is a lack of analytical standards to confirm compound identities. Another possibility is that there is a post-hydrolysis modification of GHPs, either by enzymatic means or through reactions with other phytochemical components, or as part of thermolytic reactions during gas chromatography. Very few examples of such modifications have been reported within the literature [28], but this may explain the presence of some GHPs within profiles of species where the GSL precursor is absent. Other non-GSL derived compounds have also been reported to contribute significantly to the aromas of Brassicaaceae crops. 2-Isopropyl-3-methoxypyrazine has been found to produce a strong pea, or green, pepper-like aroma in horseradish, for example ([6] Figure 2). In rocket, 'green-leaf' volatiles such as 3-hexanal and 1-penten-3-one have also been highlighted as having high odour potency [12]. The role of these compounds in aroma generation in GSL-containing crops is often not fully appreciated, and there are many diverse compounds with equally high odour intensities to GHPs present within the volatile bouquet. Figure 2: Chemical structures of volatile compounds found in horseradish, rocket, wasabi, and watercress samples (numbers in parentheses refer to compound codes in Table 2); * two separate peaks present for this compound; * tentatively identified. The aims of this study were to (i) identify and describe in detail the key odorants of four Brassicaaceae species (salad rocket, horseradish, wasabi, and watercress) by gas chromatography-olfactometry (GC-O) and gas chromatography-mass spectrometry (GC-MS), and (ii) associate the observed GHPs with their respective GSL profiles by liquid chromatography-mass spectrometry (UPLC-MS/MS). Our goal was to improve upon existing compound characterisation and odour descriptors for compounds in salad rocket, horseradish, wasabi, and watercress. The contribution of pungency by GHPs to aroma profiles is well studied; however, other aroma traits generated by non-GHPs are not well described for these species, and they likely create distinctive and subtle sensory characteristics. Additionally, detailed GSL compositions and MS/MS spectra for rocket, wasabi, and watercress is presented, highlighting discrepancies with observed hydrolysis products.
## 7 Conclusions and Future Work  
In this paper, a keyword detection scheme was proposed based on deep convolutional neural networks for personal information protection in document images. The proposed scheme is composed of key character detection and lexicon analysis. The first part is key character detection developed based on RetinaNet and transfer learning. To find the key characters, RetinaNet, composed of convolutional layers, feature pyramid network, and two subnets, is exploited to detect key characters within the region of interest in a document image. After the key character detection, lexicon analysis is used where the detected key characters are analyzed, and it combines them to obtain the keywords.  
To train the model of RetinaNet, synthetic image generation and data augmentation are exploited to yield a large image dataset. To evaluate the proposed scheme, IoU (Intersection Over Union) and mAP (Mean Average Precision) are utilized as performance measurements. For performance evaluation, many document images with different types are selected for testing. The experimental results show that the proposed scheme can detect these key characters well even under situations of poor visual quality, different fonts, and different sizes. The mAP rates of the proposed scheme are 85.1% and 85.84% for key character detection and keyword detection, respectively. Furthermore, the proposed scheme is superior to Tesseract OCR (Optical Character Recognition) software for detecting the key characters in document images with Mandarin Chinese words. The experimental results demonstrate that the proposed method can effectively localize and recognize these keywords within document images with Mandarin Chinese words.  
Since the proposed scheme is developed for specific document images, such as building use permits in Taiwan, the future work should increase the number of keywords to deal with more kinds of document images in Taiwan. Furthermore, detecting and analyzing the keywords in document images can be utilized for document image analysis and document classification.  
Conceptualization, G.-S.L.; Data curation, J.-C.T.; Formal analysis, G.-S.L. and J.-Y.L.; Methodology, G.-S.L.; Software, J.-C.T.; Supervision, G.-S.L. All authors have read and approved the final manuscript. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported in part by Ministry of Science and Technology (MOST), Taiwan, under the Grants MOST 108-2622-E-167-012-CC3. In addition, the authors wish to acknowledge the help of SyncThreads Technology Inc., Taiwan, in this study. The authors declare no conflict of interest.
## 3 Energy-Efficient aDaptive hiEarchical and RobusT Architecture
### Operation
#### 3.1.1 Initialization Phase  
In this phase the network structure is built, normal nodes join their elected clusters and these clusters form a tree to deliver the data to the sink. This phase is divided in three sub-phases.  
In the first part, with duration of a half \(T_{Config}\), each node decides on its own if it is going to be a CH, based on the procedure explained before.  
When a node decides to be a CH it sends HEAD messages to announce its role to the rest of the network. At the same time a CH starts receiving HEAD messages from the others, it decides which CH is going to join in order to send its data to the sink. This decision is based on the signal strength of the HEAD messages. But a CH will only try to join another CH if the last one has established a path to the sink. That is, if it can communicate with CHs that can reach the sink directly or through others CHs.  
Meanwhile, nodes which have decided to be leaf nodes start receiving HEAD messages too. They store them to decide to which CH they might join in the second part of this phase. The selection of the CH for these nodes follows the same criteria as selection of parent CH.  
Figure 2: State Diagram of a Normal Node.  
If a CH does not receive any HEAD message, it sends a NEED_CH message. When a normal node receives it, it reruns the procedure, but with an increased value of \(\alpha\), to decide whether it is going to be a CH. When \(\alpha\) is increased the probability of a node to become a CH increases too. This probability is increased because the network needs to be built as fast as possible in order to save energy. This mechanism along with the first random distribution of CHs allows the protocol to adapt rapidly the population of CHs to the needs of the network.  
At the end of this sub-phase, the tree structure is built and normal nodes have the necessary information to decide to which clusters they are going to join.  
In the second sub-phase, with duration of a half \(T_{Config}\), normal nodes start to join their selected clusters. In the response message, their corresponding CH sends the TDMA schedule in which each node has to send its data. After that, the normal nodes enter the low-power state.  
A CH can only allow a limited number of nodes to join in. This number is given by \(MAX_{Soft}\) and \(MAX_{Hard}\). A CH accepts all the join request petitions until it reaches its \(MAX_{Soft}\). After that, it will only accept join petitions that have activated a last resort bit. When a CH reaches the \(MAX_{Hard}\) threshold, it will no longer allow new joins.  
Finally, in the third sub-phase, with duration of one \(T_{Config}\), each leaf CH in the tree sends to its parent the amount of time needed to have all the data recollected from his nodes. The parent collects this information and decides the time schedule in which its children can send it the data. After that, the parent node repeats the process with its own parent, sending the amount of time needed to collect all the data from its nodes and its children. This process continues until the entire tree is scheduled.
# MC_MoveAbsolute(0) 4 th Order Real-Time Trajectory Generation Function Algorithm and Implementation
## 4 Generation of Profile
### Generation of Motion Profile Step  
To output the motion profile of all the derivatives and the position itself, the following steps need to be executed in a real-time control platform:  
* Reference signal \(d_{k}\) (derivative of jerk/snap) is the zero-order-hold sampled value of \(p_{d_{j\
eq k}}\) variable.
* Reference signal \(j_{k}\) (jerk) is the numerically integrated value of signal \(d_{k}\) with respect to the sampling time (\(T_{s}\)): \[j_{k}=T_{s}d_{k-1}+j_{k-1}\] (72)
* Reference signal \(a_{k}\) (acceleration) is the numerically integrated value of signal \(j_{k}\) with respect to the the sampling time (\(T_{s}\)): \[a_{k}=T_{s}j_{k-1}+a_{k-1}\] (73)
* Reference signal \(v_{k}\) (velocity) is the numerically integrated value of signal \(a_{k}\) with respect to the sampling time (\(T_{s}\)): \[v_{k}=T_{s}a_{k-1}+v_{k-1}\] (74)
* Reference signal \(x_{k}\) (position) is the numerically integrated value of signal \(v_{k}\) with respect to the sampling time (\(T_{s}\)): \[x_{k}=T_{s}v_{k-1}+x_{k-1}\] (75)  
In Figure 7, two examples of generated trajectories are shown:  
Figure 7: Two examples of generated trajectory profiles: (**a**,**b**) derivative of jerk, (**c**,**d**) jerk, (**e**,**f**) acceleration, (**g**,**h**) velocity, (**i**,**j**) position.  
The function blocks code for the 4 th order trajectory generator is presented in Figure 8, with the Simulink model presented in Figure 6 serving as a base for this conversion. Structured text code generated with a Simulink PLC Coder is about 900 lines long, and the total memory allocated for this function block in PLC is 3 kB. This means that the devised solution is implementable in the majority of PLC/PAC controllers which are IEC-61131-3 [11; 12] standard compliant.
## 3 Proposed BICORN-RNN
### Bidirectional Transmission  
The RNN is often considered to be a causal real-time system. Nevertheless, we can exploit anti-causal information of data to improve performance in non-immediate response issues, such as sentence translation and music style classification, which is the origin of bidirectional transmission of RNNs.  
Bidirectional RNNs offer an elegant solution to make full use of both past and future data information in temporal order [38]. BI-RNNs transmit each point of a training sequence to both backward and forward two symmetry directions through by incorporating an extra hidden layer, called backward hidden layer, to distinguish from the original forward hidden layer.  
Importantly, these two hidden layers are separated in space, so that they will not extend the parameter exponentially, but rather, will require twice the computation instead. We extend symbols by adding the arrows \(\overleftarrow{\bullet}\), \(\overleftarrow{\bullet}\), \(\overleftarrow{\bullet}\) to the original representation, including variables and parameters, to refer to different transfer directions. So, the formulas of BIRNNs can be revised as,  
\[\begin{array}{l}a_{j}^{\leftarrow}(t)=\sum\limits_{i=1}^{\text{I}}\mathbf{W }_{ji}^{\leftarrow}x_{i}(t)+\sum\limits_{i=1}^{\text{H}}\mathbf{W}_{ji}^{ \leftarrow}h_{i}(\overleftarrow{t}-1)+\mathbf{B}_{j}^{\text{H}}\\ a_{j}^{\rightarrow}(t)=\sum\limits_{i=1}^{\text{I}}\mathbf{W}_{ji}^{ \text{HI}}x_{i}(t)+\sum\limits_{i=1}^{\text{H}}\mathbf{W}_{ji}^{\text{HH}}h_{ i}(\overleftarrow{t}-1)+\mathbf{B}_{j}^{\text{H}}\\ \leftarrow{\leftarrow{\bullet}}\\ h_{j}(t)=f(a_{j}(t)),h_{j}(t)=f(a_{j}(t))\\ b_{j}^{\leftarrow}(t)=\sum\limits_{i=1}^{\text{H}}\mathbf{W}_{ji}^{\text{OH}}h_{ i}^{\leftarrow}(t)+\mathbf{B}_{j}^{\leftarrow},b_{i}^{\rightarrow}(t)=\sum \limits_{i=1}^{\text{H}}\mathbf{W}_{ji}^{\text{OH}}h_{i}^{\rightarrow}(t)+ \mathbf{B}_{j}^{\text{O}}\\ y_{j}^{\leftarrow}(t)=g(b_{j}(t)),y_{j}(t)=g(b_{j}(t))\\ y_{j}(t)=\alpha_{1}y_{j}(t)+\alpha_{2}y_{j}(t)\end{array} \tag{17}\]  
We adjust weights \(\alpha_{1}\) and \(\alpha_{2}\) under the limitation of \(\alpha_{1}+\alpha_{2}=1\), to determine the importance of past and future information. Even without extra complex computations to train the networks, it is the same as SGD training algorithms of standard RNNs in Section 2. BI-RNNs can compensate for gradients vanishing and exploding problems by virtue of their own symmetry, as illustrated in Figure 6. Actually, BI-RNNs have outperformed standard RNNs in various previous applications, such as protein secondary structure prediction [39], speech processing [40], dynamic ECG analysis [41], as well as our TSC task.
## 4 On-Orbit Thermal Analysis of S-STEP
### Results of Thermal Analysis
The \(\Delta\)T\({}_{\rm s}\) values of TRMs did not increase more than 7.6 \({}^{\circ}\)C in all cases. In this study, we also analyzed \(\Delta\)T\({}_{\rm s}\) over the SAR radiating board and satellite primary structure because they are important in assuring the image quality of SAR antenna against the dimensional instability due to thermal distortion. Figure 16a,b, respectively, show the representative temperature contours of SAR radiating board and satellite platform structure in the left-looking SAR imaging operation in the hot environment in which the \(\Delta\)T\({}_{\rm s}\) values are 56.6 and 13.5 \({}^{\circ}\)C. The antenna root-mean-square (RMS) surface error and antenna best-fit plane (ABFP) angle estimated in the previous study [22], based on those thermal analysis results, were 0.7 mm\({}_{\rm rms}\) and 0.005\({}^{\circ}\), respectively. These values were sufficiently low to comply with the S-STEP SAR antenna requirements (RMS error of \(<\)1.55 mm\({}_{\rm rms}\) and ABFP angle of \(<\)0.02\({}^{\circ}\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Orbital** \\ **Condition** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Analysis** \\ **Case** \\ \end{tabular} } & \multirow{2}{*}{**Operation**} & \multicolumn{4}{c}{**Spatial Temperature Diff. (\(\Delta\)T\({}_{\rm s}\))**} \\ \cline{3-8}  & & & **TRM \#1** & **TRM \#2** & **TRM \#3** & **TRM \#4** \\ \hline \multirow{4}{*}{Hot case} & Case 1 & \begin{tabular}{c} SAR imaging \\ (Left-looking) \\ \end{tabular} & 6.1 & 6.7 & 7.0 & 6.4 \\ \cline{2-8}  & Case 2 & \begin{tabular}{c} SAR imaging \\ (Right-looking) \\ \end{tabular} & 6.4 & 7.6 & 7.4 & 6.5 \\ \hline \multirow{4}{*}{Cold case} & Case 3 & \
\begin{tabular}{c} SAR imaging \\ (Left-looking) \\ \end{tabular} & 2.7 & 3.8 & 3.1 & 4.0 \\ \cline{2-8}  & Case 4 &
\begin{tabular}{c} SAR imaging \\ (Right-looking) \\ \end{tabular} & 2.7 & 3.9 & 3.0 & 3.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Spatial temperature difference (\(\Delta\)T\({}_{\rm s}\)) of TRM according to SAR imaging modes. Figure 15: Spatial temperature differences (\(\Delta\)T\({}_{\rm s}\)) of TRMs in SAR left-looking mode in hot environment (Case 1).
## 3 Results
### Temperature and Heat Results  
To assess the complete model, experimental tests were conducted in five operating points from the test matrix. Modelled outlet temperature \(T_{g,\,out}\) and heat loss through the exhaust pipe \(\dot{Q}\) are compared with experimental results in the five calibrating points (see Table 9).  
\begin{table}
\begin{tabular}{c c} \hline \hline \(A_{CO}\)(m\({}^{3}\)/(mol\(\cdot\)s) & \(3.42\times 10^{6}\) \\ \hline \(E_{CO}\)(m\({}^{3}\)/(mol\(\cdot\)s) & \(4.73\times 10^{4}\) \\ \hline \(A_{C_{j}H_{6}}\)(m\({}^{3}\)/(mol\(\cdot\)s) & \(2.30\times 10^{3}\) \\ \hline \(E_{C_{3}H_{6}}\)(0/mol) & \(1.98\times 10^{4}\) \\ \hline \end{tabular}
\end{table}
Table 7: Obtained kinetic parameters for \(CO\) and \(C_{3}H_{6}\) oxidations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Engine** \\ **Speed** \\ **(min\({}^{-1}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Measured** \\ **(Nm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Modelled** \\ **CO** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Error in** \\ **CO** \\ **Conversion** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\(C_{3}H_{6}\)** \\ **Conversion** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Modelled** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Error in** \\ **C3**\(H_{6}\)** \\ **Conversion** \\ **(\%)** \\ \end{tabular} } \\ \hline
1000 & 10 & 27.3 & 25.8 & \(-1.4\) & 1.4 & 0.0 & \(-1.4\) \\
1000 & 60 & 90.9 & 99.2 & 8.3 & 83.3 & 88.4 & 5.1 \\
1000 & 110 & 93.7 & 100.0 & 6.2 & 79.3 & 85.5 & 6.2 \\
1700 & 10 & 51.2 & 39.6 & \(-11.6\) & 3.6 & 0.0 & \(-3.6\) \\
1700 & 60 & 95.0 & 99.9 & 4.9 & 69.0 & 80.8 & 11.8 \\
1700 & 110 & 99.5 & 100.0 & 0.5 & 58.0 & 69.8 & 11.8 \\
2400 & 10 & 66.10 & 64.7 & \(-1.4\) & 72.0 & 76.3 & 4.3 \\
2400 & 60 & 97.37 & 99.1 & 1.8 & 73.6 & 67.4 & \(-6.2\) \\
2400 & 110 & 100.0 & 98.9 & \(-1.1\) & 58.8 & 55.4 & \(-3.4\) \\ \hline \end{tabular}
\end{table}
Table 8: Experimental versus chemical model conversion results.  
Figure 10: Plot for (**a**) CO kinetic parameters derivation. Linear fit: \(\text{C}_{\text{CO}}\) = 21.261, \(\text{D}_{\text{CO}}\) = 5685.10 K (**b**) \(\text{C}_{3}\text{H}_{6}\) kinetic parameters derivation Linear fit: \(\text{C}_{\text{C}3\text{H}6}\) = 14.647, \(\text{D}_{\text{C}3\text{H}6}\) = 2388.55 K.  
The remaining four operating points of the test matrix are used to evaluate the model out of training points. Results are shown in Table 10.
## 3 Results and Discussion
### Data Analysis Using a Fine-Tuned Mask R-CNN
Table 2 shows that the Precision and Recall of the proposed model for OTC were 0.877 and 0.842, respectively. These values are higher than the model metrics of Precision and Recall for Kocide 3000, with values of 0.857 and 0.821, respectively. These results corroborate the reliability and selectivity of the developed models to properly detect the pesticide residue coverage in contrast to other leaf features or defects. Independently, these two metrics (Precision and Recall) provide a better understanding of the number of misclassifications declared by the predictive model. Hence, the use of F1, a metric that combines both Precision and Recall, should be the most relevant metric to assess the performance of any developed model. The F1 score or Dice Sorensen Coefficient uses the harmonic mean from Precision and Recall to provide the quality classifier of a model in a range from 0 to 1. The developed model exhibited a high quality classifier for both OTC and Kocide 3000, with values of 0.823 and 0.804, respectively. Although these metrics showcased good performance of the developed software, there is an opportunity for further improvements, for instance addressing factors that contribute to noise (e.g., leaf orientation, camera lens, and lesions, among others) hindering higher levels of performance. Altogether, these findings demonstrate the effective development of an image analysis software assisted by a machine learning system for pesticide residue coverage determination. The developed software model showcased great reliability and versatility for field conditions image analysis. This technological tool was designed to assist citrus growers in their decision-making process during the pesticide application cycle and in their current efforts towards more sustainable agricultural practices.