esa-sceva/satcom-chunk-collection huggingface dataset:
# Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion
## 3 Methodology
### Performance Evaluation Index of Algorithm Model  
Five commonly used indicators, precision P, recall rate R, mAP (Formula (14)), detection speed and F1 (Formula (12)), were used to verify the performance of the model. For a binary classification problem, the samples can be divided into four types according to the combination of the true category and the predicted category of the sample: TP (True Positive), FP (False Positive), TN (True Negative) and FN (False Negative). In this paper, when IOU \(\geq\) 0.5, it was a True Positive; when 0 < IOU \(<\) 0.5, it was a False Positive. When IOU = 0, the background was detected, and it was regarded as a True Negative. When IOU \(\geq\) 0.5, and the prediction result considers that the IOU was less than 0.5, it was a False Negative. The confusion matrix for the classification results is shown in Table 4.  
The precision P and recall rate R were defined as Formulas (10) and (11). P was used to describe the proportion of the samples where the prediction is positive that the prediction is positive. R was used to describe the proportion where the labelling was positive that the prediction was positive. The higher the values of these two, the better the performance  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Labelled** & **Predicted** & **Confusion Matrix** \\ \hline Positive & Positive & TP \\ Positive & Negative & FN \\ Negative & Positive & FP \\ Negative & Negative & TN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix for the classification results.  
Figure 6: Detection results of 4 kinds of data-expanded images based on YOLO v4.  
of the algorithm. Using the precision P as the vertical axis and the recall rate R as the horizontal axis, the precision recall (PR) curve was achieved.  
\[P=\frac{TP}{TP+FP} \tag{10}\]  
\[R=\frac{TP}{TP+FN} \tag{11}\]  
The F1 score is a reference value derived from Recall and Precision, and its value is usually close to the smaller of the two. If the F1 score is high, it indicates that both the Recall and Precision are high, so it was hoped to obtain a high F1 score. The F1 score is defined as:  
\[F1=\frac{2\times P\times R}{P+R} \tag{12}\]  
The average precision (AP) can show the overall performance of a model under different score thresholds. In this paper, \(AP\) was obtained by averaging the precision value on the PR curve, and was defined as Formula (13). \(mAP\) was the sum of AP values for all categories/the number of categories, and C was the number of categories. Since only sugarcane stem nodes were detected in this paper, \(C=1\) was used in this study.  
\[AP=\int_{0}^{1}P(r)d_{r} \tag{13}\]  
\[mAP=\frac{\sum_{i=1}^{C}AP(C)}{C} \tag{14}\]  
The detection speed (f/s) is the derivative of the computational time required by each method to recognise one sample. All the aforementioned methods were coded and developed in Python 3.6, and the deep learning framework was Keras. A workstation with a 2.3 GHz Intel 5218 \(\times\) 2 processor, 64 GB RAM and 11 GB NVIDIA RTX 2080 Ti GPU was used for calculation and images processing.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
Well-known MTT algorithms are the joint probabilistic data association filter (JPDAF) [38; 43], multiple hypothesis tracker (MHT) [38; 41; 44], and random finite set (RFS)-based algorithms [41; 42; 45]. The IMM algorithm has been successfully used in the multiscan JDDAF [43], MHT [46] and RFS-based algorithms [47]. If a single-model filtering algorithm is used in a multi-target tracker for maneuvering targets, then the measurements will not be associated with predicted tracks due to model mismatch, and tracks will be terminated after a few scans. This will create many tracks corresponding to a true target trajectory. This phenomenon is known as track fragmentation. The fraction of correct measurements in a track, known as the track purity, will also be affected. In general, an IMM-based multi-target tracker will have a significant advantage over a single-model filter-based tracker. Since computer hardware and software systems have improved significantly over the last few decades, IMM-based multi-target trackers can solve complex tracking problems involving nonmaneuvering and maneuvering targets. The organization of the study is as follows. Section 2 presents the single-model nonlinear filtering algorithms. The hybrid state estimation needed for a maneuvering target is explained in Section 3. Sections 4 and 5 describe the dynamic and measurement models used in the current problem. The EKF, UKF, and CKF are explained in Section 6. The IMM algorithm based on the CKF and PCRLB are discussed in Sections 7 and 8, respectively. The numerical simulation and results for the \(3g\), \(4g\), \(5g\), and \(6g\) cases for various operating conditions are presented in Section 10. Finally, Section 11 provides a summary of the work in the study and discusses the future directions of this research.
## 4 The Accomplishment of the Composited Velocity Loop  
We set the continuous function \(a(t)\) to represent the real acceleration of the platform. Generally, in low frequency, the noise is very big that we cannot ignore them which is descripted by \(\xi\) and during a sampling time we can treat \(\xi\) as a constant. The integral of the accelerometers' signal is presented as follows:  
\[\widetilde{V}(t)=\int{(a(t)+\xi)dt}=\int{a(t)dt}+\xi\cdot t=\mathrm{V}(t)+\xi\cdot t \tag{11}\]  
Obviously, \(\widetilde{V}(t)\) contains a drift except for the real velocity \(V(t)\). Thus, we decide to use the CCD's position signal to correct it. However, at first, we need to acquire the estimated position by the integral of \(\widetilde{V}(t)\). The calculated position is as follows:  
\[\theta(t)=\int{\widetilde{V}(t)dt}=\int{\left(\int{a(t)dt}+\xi\cdot t\right) dt}=\int{V(t)dt}+\frac{1}{2}\xi t^{2} \tag{12}\]  
If we set \(S(t)\) to be the real position signal of CCD, from Equations (11) and (12), we can deduce the real velocity \(V(t)\):  
\[V(t)=\widetilde{V}(t)+2\cdot\frac{S(t)-\theta(t)}{t} \tag{13}\]  
Since the computers cannot process the continuous signal, we transform these equations into discrete forms with trapezoidal discretization method. The sampling period of the inner acceleration loop and the estimation of position is \(T_{a}\), which is much smaller than the CCD's sampling period \(T_{p}\). We assume \(T_{p}=(N-1)T_{a}\), which means during a sampling period of the CCD, the accelerometers would produce \(N\) sampling data. Figure 6 exhibits the sketch map of data update.  
The discrete forms of Equations (11) and (12) are as follows. The superscript represents the order of the CCD sampling period, and the subscript is the acceleration's order:  
\[{}^{i}\widetilde{V}_{j}={}^{i}\widetilde{V}_{j-1}+\frac{{}^{i}a_{j-1}+{}^{i}a _{j}}{2}\cdot T_{a} \tag{14}\]  
\[{}^{i}\theta_{j}={}^{i}\theta_{j-1}+\frac{{}^{i}\widetilde{V}_{j-1}+{}^{i} \widetilde{V}_{j}}{2}\cdot T_{a} \tag{15}\]  
Since there are drift in \({}^{j}\widetilde{V}_{i}\) and \({}^{j}\theta_{i}\), when every CCD signal comes, we need to correct them. The recursive forms are as follows:  
\[\left\{\begin{array}{l}{}^{i}\widetilde{V}_{1}={}^{i-1}\widetilde{V}_{N}+2 \cdot\frac{S_{1}-{}^{i-1}\theta_{N}}{T_{p}}\\ {}^{i}\theta_{1}=S_{i}\end{array}\right. \tag{16}\]  
Figure 6: The sketch map of data update.  
In the first sampling period, we can set the \({}^{1}\widetilde{V}_{i}\) and \({}^{1}\theta_{1}\) to be 0, and with the above 4 recursive equations, we will get the composite velocity. As the inner acceleration loop has improved platform characteristics, the traditional PI controller with a low-pass filter presented in Equation (17) can meet the velocity closed-loop control. After the rebuilding by the velocity loop, the characteristics will be further better, and we design the position controller to be an inertial element shown as Equation (18).  
\[C_{\mathrm{p}}=\frac{K_{p}s+K_{i}}{s}\cdot\frac{1}{1+T_{f}s} \tag{17}\]  
\[C_{p}=\frac{K}{1+Ts} \tag{18}\]
## 3 Results and Discussion
### High-Resolution Mass Spectrometry Analysis
ESI FT-ICR MS investigations has also highlighted the presence of numerous flavonoids, flavans, and their derivates in the form of aglycones, such as apigenin (flavone) and myricatin (galloyl flavonol sulfate), or glycosides, e.g., myricetin 3-(3\({}^{\prime\prime}\)/\(A^{\prime\prime}\)-diacetylIhamoside), quercetin 3-(6\({}^{\prime\prime}\)-malonyl-glucoside), quercetin 3-O-xylosyl-glucuron, luteolin 7-O-(6\({}^{\prime\prime}\)-O-malonyl)-beta-D-diglucoside, kaempferol 3-O-arabinoside. The general class of flavonoids, phenolic secondary plants metabolites, holds a wide range of potential activities. They have been thoroughly studied for the selective antibacterial activity and synergism with antibiotics on several bacterial strains. As reported in the literature [57], flavonoids display direct antibacterial activity and suppression of bacterial virulence. To explain these effects, many mechanisms have been proposed, including cytoplasmic membrane damage, inhibition of nucleic acids synthesis and inhibition of energy metabolism. In addition, it has also been described that flavonoids exhibit synergism with antibiotics by both improving \(\beta\)-lactam resistance and enhancing their efficacy in \(\beta\)-lactamase inhibition. Here, both ESI(+) and ESI(-) ionization modes have unveiled the presence of several water-soluble vitamins, like folic acid (vitamin B9), and fat-soluble ones, including vitamin D derivates (hydroxcyclitroid), vitamin A, vitamin K and two isoforms of vitamin E, hydroxy-gamma-tocotrienol and alpha-tocotrienol, acknowledged for their antioxidant activity [58]. During the MS experiments, numerous organosulfur compounds have emerged. All these substances are bioactive metabolites typical of Brassicaaceae (sulforapheny) and Liilaceae (allyl sulfide, garlic, S-Propyl-L-cysteine and S-(Allylithio)-L-cysteine) families, although they are very common in many plant sources. S-compounds present different health properties, including antimicrobial, antioxidant, and anticancer activities [59]. However, the overall beneficial activity can be attributed to the whole phytocomplex present in LO since several phytochemicals, reported above, may contribute with their additive and synergistic effects to the bioactive properties of LO. Interestingly, HRMS analysis revealed the presence of resmethin identified at \(m/z\) 377.15113 as potassium adduct [C\({}_{22}\)H\({}_{26}\)O\({}_{3}\) + K]+, an insecticide used in the control of mosquito population.
# A Detailed One-Dimensional Hydrodynamic and Kinetic Model for Sorption Enhanced Gasification
###### Abstract  
Increased installation of renewable electricity generators requires different technologies to compensate for the associated fast and high load gradients. In this work, sorption enhanced gasification (SEG) in a dual fluidized bed gasification system is considered as a promising and flexible technology for the tailored syngas production for use in chemical manufacturing or electricity generation. To study different operational strategies, as defined by gasification temperature or fuel input, a simulation model is developed. This model considers the hydrodynamics in a bubbling fluidized bed gasifier and the kinetics of gasification reactions and CO\({}_{2}\) capture. The CO\({}_{2}\) capture rate is defined by the number of carbonation/calcination cycles and the make-up of fresh limestone. A parametric study of the make-up flow rate (0.2, 6.6, and 15 kg/h) reveals its strong influence on the syngas composition, especially at low gasification temperatures (600-650 \({}^{\circ}\)C). Our results show good agreement with the experimental data of a 200 kW pilot plant, as demonstrated by deviations of syngas composition (5-34%), lower heating value (_LHV_) (5-7%), and \(M\) module (23-32%). Studying the fuel feeding rate (22-40 kg/h), an operational range with a good mixing of solids in the fluidized bed is identified. The achieved results are summarized in a reactor performance diagram, which gives the syngas power depending on the gasification temperature and the fuel feeding rate.  
one-dimensional SEG model; dual fluidized bed; sorbent deactivation; hydrodynamics; kinetics; fuel feeding rate; biomass Article
# Stimulation Montage Achieves Balanced Focality and Intensity
## 4 Discussion
### Robustness  
The Robustness tests here identify the important roles of tissue conductivities in the optimal stimulation, which is consistent with the existing literature [37; 38]. Through our systematic studies, we investigate the tissue conductivity variation effects for multi-electrode simulations with different target types and different algorithms. Overall, scalp and CSF produce slight effects on stimulation intensity, target error, and focality. Unfortunately, conductivities of the skull and cortex itself greatly influence the electric field distribution over the cortex. These results indicate the need for individual modeling, especially for the parameter settings of the clinical applications. Another possible solution is to construct general models of specific populations. For example, the tissue conductivities of children, adults, and aging population vary a lot, while they may be stable within their own group [46; 47; 48]. Furthermore, conductivity discrepancies resulting from pathologies are also being investigated [49; 50; 51]. The electrical property changes in disease states may greatly influence the electric field distribution and alter the stimulation results. Thus, the specific simulation model for certain disease treatments should be carefully considered. As a result, more experiments should be conducted to investigate the joint influence of different tissue conductivity, various stimulation protocols, electrode characteristics, electrode position displacement, more precise human head models, etc.  
It is interesting to note the robustness differences between algorithms that the MI method is in general less robust than other optimization algorithms. The conventional montage is slightly better than the MI method with relatively lower bars in Figures 4, 5, 6 and 7, but the robustness is not as good as LCMV or SBFI. LCMV and SBFI have comparable robustness against tissue conductivity uncertainty. The reason for the more robust behavior could be that both algorithms try to balance both target (\(\mathbf{e_{0}}^{\top}\)CS) and non-target regions (\(\parallel\mathbf{Ds}\parallel^{2}\)) with more electrodes. Therefore, the current distribution in the brain could be relatively more controllable, which leads to more robust results in the face of conductivity uncertainty. The results of CLS are the least affected by conductivity changes--far less than the others because of its low intensity over the whole cortex.
# STFEformer: Spatial-Temporal Fusion Embedding Transformer for Traffic Flow Prediction
## 4 Experimental Results and Discussion
### Performance Comparison
It also designs a spatial-temporal graph convolution based on an ODE structure to simultaneously handle spatial and temporal information. ASTGCN, based on node-adaptive parameters, constructs an adaptive graph and further combines it with an attention mechanism to explore local spatial-temporal features. Moreover, STGODE employs two TCN blocks to extract long-range temporal correlations, while ASTGCNs adopt both TCNs and ODEs to further explore global spatial-temporal features. Our model, STFEformer, exhibited superior prediction performance on all three real-world datasets. Specifically, on the PeMS04 dataset, STFEformer's MAE was 18.98, surpassing the second-best model, DSTAGNN, which had an MAE of 19.30. Additionally, STFEformer presented a lower RMSE of 30.94 compared to DSTAGNN's RMSE of 31.46, and a lower MAPE of 12.65% compared to DSTAGNN's MAPE of 12.70%. Similarly, on the PeMS07 dataset, STFEformer achieved an MAE of 20.50, an RMSE of 33.87, and an MAPE of 8.75%, while DSTAGNN obtained an MAE of 21.42, an RMSE of 34.51, and an MAPE of 9.01%. On the PeMS08 dataset, STFEformer achieved an MAE of 14.80, an RMSE of 24.22, and an MAPE of 9.85%, while DSTAGNN exhibited an MAE of 15.67, an RMSE of 24.77, and an MAPE of 9.94%. These improvements are attributed to the combination of short-term features and periodic features in the temporal dimension, the use of data-driven adaptive spatial embeddings without predefined graphs, and native feature extraction from raw data. Then, our model utilizes a spatial self-attention module which integrates an adaptive similarity matrix, thereby highlighting interactions between similar nodes and capturing dynamic and long-range spatial correlations. To explore dynamic temporal dependencies, a temporal self-attention module is also employed. In conclusion, our model effectively captures complex features in traffic data.
# Multi-Module Combination for Underwater Image Enhancement
## 3 Proposed Method
### Framework Architecture  
Figure 2 illustrates the general framework of the UMMC method, which comprises five fundamental modules. The color deviation detection module is primarily designed for images exhibiting color deviations, although such deviations may not be readily apparent in shallow water images. The color and white balance correction module computes a nonlinear color mapping function to correct the image's color by employing a k-nearest neighbor strategy. The visibility restoration module utilizes an enhanced rank-one a priori matrix decomposition technique, combined with depth information estimation, to achieve the precise removal of scattering blur [18]. The contrast enhancement module enhances the image's brightness through grayscale correction and the probability distribution of brightness pixels while also applying gamma correction to automatically improve the image contrast. The image fusion module integrates images processed by the contrast enhancement module with those processed through multiple exposures.  
Figure 2: An overall flowchart of the UMMC algorithm. The input image \(I(x)\) undergoes color deviation detection (the K value of the example image used in the flowchart exceeds the predetermined threshold) and subsequently enters the correction process. The corrected image \(\hat{Z}(x)\) undergoes visibility restoration and comparison enhancement to obtain \(L_{1}(x)\) and \(L_{2}(x)\), respectively. Finally, the final resulting image \(F(x)\) is obtained after fusion processing.
## 6 Discussion and Future Considerations
### Optimization  
Obtaining an optimal AoI solution can sometimes be very complex, especially when dynamically changing parameters are involved [84]. Particularly, many works only arrive at near-optimal solutions without absolute optimal. Optimal solutions should be sought during trajectory optimization even if that is at the cost of reasonable complexity. Different parameters are involved in UAV trajectory optimization. For this reason, several optimization and ML techniques are being used to optimize UAV trajectories. One of the main concerns with respect to the performance of optimization algorithms is convergence, especially for neural networks, which are difficult to study analytically. In this case, the choice of algorithm for optimization highly determines the convergence speed. The proper selection of hyper-parameters is required for ML-based solutions, as emphasized in [8]. Another challenge is characterizing a globally optimal solution for non-convex objective functions [82]. This can be tackled by breaking down the problem or developing efficient algorithms for solving such problems. However, it is important to consider that the proposed algorithms should be able to solve the problem within the shortest possible time. Similarly, the issue of high dimensional state variables is commonly reported in the literature and should be solved by choosing efficient algorithms. Another important consideration is 3D trajectory optimization using 3D obstacle data as it would improve the applicability of the studied models for real-life use cases. Additionally, if UAVs can obtain data by observing the ground in real-time, a priori knowledge of obstacle information is not required [62].
# Structure and Sound Absorption Properties of Spiral Vane Electrospun PVA/PEO Nanofiber Membranes
## 3 Results and Discussion  
### Fourier Transform-Infrared Analysis  
The FT-IR spectrum was showed in Figure 3. Many characteristic peaks of the pure PVA can be found at 840, 954, 1093, 1427, 1736, 2940, and 3349 cm\({}^{-1}\). It was obvious that the peaks at 840, 954, and 2940 cm\({}^{-1}\) was caused by stretching vibration of the functional group of -CH\({}_{2}\). The peak at 1093 cm\({}^{-1}\) was contribute to the stretching vibration of the functional group of C-O. The peaks at 1427 and 3349 cm\({}^{-1}\) was caused by stretching vibration of the functional group of -OH. The peak at 1736 cm\({}^{-1}\) was benefited from the stretching vibration of the functional group of C=O. It can be seen from Figure 3, the blend membranes possessed the same bands at 840, 954, 1093, 1427, 1736, 2940, and 3349 cm\({}^{-1}\). However, the peaks at 1427 and 3349 cm\({}^{-1}\) became more gentle with the addition of PEO. Moreover, the sample d appeared the characteristic peak of PEO at 2887 cm\({}^{-1}\), which was caused by stretching vibration of the functional group of -CH\({}_{2}\).  
Figure 3: FT-IR of PVA/PEO membranes. (**a**) 8 wt % PVA; (**b**) 8 wt % PVA/3 wt % PEO (90/10); (**c**) 8 wt % PVA/3 wt % PEO (80/20); (**d**) 8 wt % PVA/3 wt % PEO (70/30); (**e**) 3 wt % PEO. FT-IR: Fourier Transform-Infrared; PVA: Polyvinyl alcohol; PEO: Polyethylene oxide.  
Figure 2: The structure of impedance tube.  
According to theory, PEO macromolecules with the ultra-high modulus passed through the PVA macromolecular chains, hydrogen bonds were formed between the ether bond of PEO macromolecule and hydroxyl group on the side-group of PVA molecule during the film-forming process [32; 33; 34]. Hydrogen bonds between the PVA molecules and the orderly arrangement of PVA molecules were hindered by PEO macromolecules [35]. PEO can increase the intermolecular entanglement rate between fibers, due to its long molecular chain and good viscoelasticity [36; 37].