esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Effective Narrowband Active Noise Control System for Accommodating Frequency Mismatch
## 4 Simulation Results and Discussion
### Simulation 2: The Tracking Performance of NANC System  
The amplitude and frequency mutation of primary noise are also FM phenomena that are often encountered in practical applications. The change in the FM usually influences the system stability. As the system with MLMS performs better than the regular system, we chose the former as the comparison experiment. Therefore, this case is used to simulate the tracking performance of the two compared NANC systems when the input signal is unstable. The FM jump is artificially introduced at the midpoint of the iteration. The same basic conditions of simulation 1 are applied to this simulation. The MSE and frequency tracking plots of the two NANC systems are demonstrated in Figure 10.  
Figure 8: Under 20% FM (**a**) the system with MLMS (**b**) the proposed system.  
Figure 9: MSE of AR-related coefficients \(c_{i}\) performance for various methods under 15% FM.  
We assume that there is a 5% FM up to 25,000 samples and a FM of \(-5\%\) is introduced at 25,000 samples; Figure (a)a shows that the error microphone signal is reduced adaptively, even after the transition of FM. As shown in (a), the proposed system can provide a faster tracking ability without damaging the system performance on the whole. Secondly, it is shown in Figure (b)b that, even if we introduce a jump from 10% FM to \(-10\%\) FM at the middle of the iteration, the proposed algorithm is able to track the FM efficiently, and the proposed NANC system converges faster than the NANC system with MLMS. Obviously, the new proposed NANC system provides excellent robustness against non-acoustic sensor errors.
## 2 Theory Background and Methods
### Mk-Mmd  
MMD [25] is defined as the square of the kernel embedding distance of the data distribution in reproducing the kernel Hilbert space, which is a commonly used non-parametric metric function to evaluate the difference between two distributions. For datasets \(s\) and \(t\) with sufficient data and different distributions, the MMD distance formula is as follows:  
\[MMD^{2}(s,t)=\
orm{\frac{1}{n^{s}}\sum_{i=1}^{n^{s}}\varphi(x_{i}^{s})-\frac{1} {n^{t}}\sum_{j=1}^{n^{t}}\varphi\Big{(}x_{j}^{t}\Big{)}}\
orm{_{\mathbb{R}}^{2}} \tag{7}\]  
where \(\varphi(\,\cdot\,)\) is the mapping function from the feature space to the reproducing kernel Hilbert space, and \(n^{s}\) and \(n^{t}\) represent the number of samples in the dataset \(s\) and \(t\), respectively.  
When calculating the distances of different distributions in Hilbert space, the multiple kernel functions are used to calculate the MMD [26] as  
\[MMD^{2}(s,t)=\frac{1}{n^{s}n^{s}}\sum_{i=1}^{n^{s}}\sum_{j=1}^{n^{t}}k\Big{(}x_ {i}^{s},x_{j}^{s}\Big{)}+\frac{1}{n^{t}n^{t}}\sum_{i=1}^{n^{t}}\sum_{j=1}^{n^ {t}}k\Big{(}x_{i}^{t},x_{j}^{t}\Big{)}-\frac{1}{n^{s}n^{t}}\sum_{i=1}^{n^{t}} \sum_{j=1}^{n^{t}}k\Big{(}x_{i}^{s},x_{j}^{t}\Big{)} \tag{8}\]  
Traditionally, the differentiable Gaussian kernel is used to optimize the gradient descent for the loss function. Considering the probability distributions in different reproducing kernel Hilbert spaces [27], the multiple kernel functions in the MMD method, which can improve the adaptively to the different data and measure the distance between two different distributions to extract good feature maps, are introduced to guarantee the optimal performance of the system. The MK-MMD with multiple Gaussian kernels with different parameters is adopted for superposition calculation and developing a method for optimal kernel selection and improving its effectiveness.
## 4 Motion Planning Algorithm Based on Co-Optimization of Communication and Sensing
### Receding Horizon Optimizing Model for Multi-UAVs Cooperative Target Tracking
Thus, the distribution optimization is performed in a specific order [25]. For example, \(A_{n}\) plans its trajectory based on the trajectories of the previous (\(n-1\)) aircrafts (\(A_{1}\sim\)\(A_{n-1}\)) and then sends its trajectory to aircraft \(A_{n+1}\). UAV \(A_{i}\) computes its own trajectory by solving the following local optimization problem:  
\[\mathbf{u}_{i}^{*}[k+1:k+T_{\text{P}}]=\text{arg}\;\underset{u_{i}}{\max}\Bigg{\{} \sum\limits_{l=1}^{T_{\text{P}}}\lambda_{k+l}\cdot\text{tr}\Big{(}\beta_{i,k+l }\cdot\hat{\mathbf{H}}_{i,k+l}^{\text{T}}\mathbf{R}_{i,k+l}^{-1}\hat{\mathbf{H}}_{i,k+l} \Big{)}\Bigg{\}} \tag{41}\]  
with the constraints of Equations (34)-(36).
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Tungsten-Doped Zinc Oxide Films  
Tungsten-doped zinc oxide films were deposited on the glass substrate [161]. The obtained films showed a hexagonal wurtzite structure and uniform morphology based on XRD and SEM analyses. When the amount of tungsten (W) increased, the band gap increased and the band edge emission moved towards the blue region.
## 4 Results and Discussion  
### Release Levels on Leachates from RPF by UNE-EN 12457-4:2003  
The release levels of pollutant elements were measured in the recycled plastic fibres (RPF) by performing the compliance test according to the UNE-EN 12457-4:2003 standard and the obtained data are shown in Table 2. Anions (sulphate, chloride, and fluoride) and heavy metals (Hg, Pb, Cd, and Se) are not included because their levels were lower than the detection limits.  
Low concentrations were measured for heavy metals and anions regulated by Council Decision 2003/33/EC [31], according to the compliance test, except for antimony. This surpassed the limit established for being classified as inert, and so FRP was classified as being a non-hazardous material. The release of antimony from PET is because it is widely used as a polycondensation catalyst in production [38].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Leachate Concentrations (mg/kg m.s.) According to the Compliance Test**} \\ \hline
**Elements** & **Inert Legal Limit at L/S of 10 L/kg** & **RPF** \\ \hline pH value & - & 9.2 \\ Cr & 0.50 & 0.0035 \\ Cu & 2.00 & 0.0557 \\ Zn & 4.00 & 0.0528 \\ Sb & 0.06 & 0.0860 \\ Ni & 0.40 & 0.0074 \\ Ba & 20.00 & 0.1102 \\ As & 0.50 & 0.0088 \\ Mo & 0.50 & 0.0053 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Leachate concentrations (mg/kg m.s.) from RPF by UNE-EN 12457-4:2003.
## 3 Distributed Target Tracking Systems
### Phase III: Target Positioning  
This phase presents a measurement mechanism to estimate the target position. The localization operation is performed in three phases: \"initial geometrical positioning\", \"position estimation refinement\", and \"estimation fusion\". For each sub-cluster member, the Bayesian particle filter is used to obtain an initial position estimate. Next, the localization adjustment problem can be solved by applying a neural networking model to refine the sample area and then redoing the particle filter to improve the positioning accuracy. Finally, estimation fusion is performed by the leader node in order to obtain a global estimate.
## 4 Experiments and Discussion
### Experiment Results and Discussion in DIOR Dataset
#### 4.4.1 Ablation Experiment
Model 4 improved by the complete DenseRes Block achieves the highest mAP and a significant increase in mAP\({}^{\text{S}}\), mAP\({}^{\text{M}}\) and mAP\({}^{\text{L}}\). It is probable that, on the basis of the 'Short-cut', the feature of each size object can be better retained when transmitting in the DenseRes Block, and can thus benefit the building of high-level semantic relevance with a background feature through 'Combine'. The experimental results of DCA module ablation are shown in Tables 4 and 5. Table 4 shows the influence of scaling factor R on the performance of the DCA Block. The results show that, when R = 32, DCA can achieve the best performance. Table 5 exhibits the influence of three different fusion methods shown in Figure 14 on the performance of the DCA Block. The results show that the DCA Block with a different fusion method can effectively improve the detection accuracy. Specifically, compared with DCA in series, DCA in parallel has a more obvious advantage in small and middle-sized objects, while the FPS is slightly reduced by 0.7 img/s. This may be due to the fact that, when employing the same number of operation layers in one building block, although the structure designed in parallel has a higher fragment, it can keep the integrity of the feature better compared with that in series. For the proposed DCA Block, which has a small structure complexity, utilizing the structure in parallel makes it perform better in the enhancement of feature expression without an obvious sacrifice of inference time. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Detectors**} & \multicolumn{3}{c}{**DenseRes Block**} & \multirow{2}{*}{**FPS**} & \multirow{2}{*}{**mAP\({}^{\text{0.5}}\)**} & \multirow{2}{*}{**mAP\({}^{\text{0.50,95}}\)**} & \multirow{2}{*}{**mAP\({}^{\text{S}}\)**} & \multirow{2}{*}{**mAP\({}^{\text{M}}\)**} & \multirow{2}{*}{**mAP\({}^{\text{L}}\)**} \\ \cline{2-2} \cline{6-9}  & **+Short-Cut* & & & & & & & & \\ \hline Model 1 & & & 65.7 & 70.8 & 38.2 & 9.1 & 29.9 & 54.4 \\ Model 2 & \(\surd\) & & 65.7 & 71.3 & 38.7 & 9.5 & 30.3 & 54.7 \\ Model 3 & & \(\surd\) & 65.7 & 71.2 & 38.5 & 9.0 & 30.2 & 54.9 \\ Model 4 & \(\surd\) & \(\surd\) & 65.7 & 71.5 & 38.8 & 9.4 & 30.4 & 54.9 \\ \hline \hline \end{tabular} Note: ‘Short-cut” indicates a short-cut to connect \(y_{i}\) (\(1<i\leq n\)) and \(y_{i-1}\) (\(1<i\leq n\)) in DenseRes Block; ‘Combine” means [\(y_{i}\)] (\(1\leq i\leq n\)) \(\oplus\)_Input_ in DenseRes Block.  
\end{table} 
Table 3: The ablation results of DenseRes Block. Figure 13: The structure of DenseRes Block in each detector in Table 3.
## 2 Results
### Verification of Aroma Extraction Methodology through HS-SPE
#### 2.2.1 Extraction Time  
The extraction time significantly impacted the chromatographic results, influencing the quantity and area of the volatile compounds in the CB sample. For example, when the extraction time was brief (e.g., one hour), incomplete extraction of all compounds of interest in the sample was possible, affecting both the concentration and quantity of the extracted compounds and the quality of the chromatographic results.  
However, when the extraction time is excessively extended, as in the case of 6 h, degradation of compounds, desorption of compounds, or loss of volatile analytes may occur (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Extraction Time (h)** & **Total Area** & **Number of Peaks** \\ \hline
1 & 392.0 & 38 \\
2.5 & 435.7 & 42 \\
4 & 735.1 & 62 \\
6 & 907.0 & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of extraction time verification (total area and number of peaks obtained when evaluating the extraction times).  
The results suggest that 4 h extraction emerges as the most pertinent option, as it proves to be more stable concerning the provided data. This extraction duration balances the determination of a wide range of volatile compounds of interest while preserving their integrity and the quality of the chromatographic results.
###### Abstract  
The general utilization of processing equipment in industry has increased the risk of foreign material contamination. For example, peanut and walnut contaminants in whole wheat flour, which typically a healthy food, are a threat to people who are allergic to nuts. The feasibility of utilizing near-infrared hyperspectral imaging to inspect peanut and walnut powder in whole wheat flour was evaluated herein. Hyperspectral images at wavelengths 950-1700 nm were acquired. A standard normal variate combined with the Savitzky-Golay first derivative spectral transformation was adopted for the development of a partial least squares regression (PLSR) model to predict contamination concentrations. A successive projection algorithm (SPA) and uninformative variable elimination (UVE) for feature wavelength selection were compared. Two individual prediction models for peanut or walnut-contaminated flour, and a general multispectral model for both peanut-contaminated flour and walnut-contaminated flour, were developed. The optimal general multispectral model had promising results, with a determination coefficient of prediction (\(\text{R}_{\text{p}}^{2}\)) of 0.987, and a root mean square error of prediction (RMSEP) of 0.373%. Visualization maps based on multispectral PLSR models reflected the contamination concentration variations in a spatial manner. The results demonstrated that near-infrared hyperspectral imaging has the potential to inspect peanut and walnut powders in flour for rapid quality control.  
near-infrared hyperspectral imaging; peanut and walnut powders; whole wheat flour; visualization 2018 8 1076 doi:10.3390/app8071076
# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 2 Related Work
The RangeDet [7] network projects the point cloud onto RV and introduces a meta-kernel convolution operation, which makes the convolution kernel weight depend on the geometric information of the convolution center point and neighborhood points and improves the detection accuracy of distant objects and pedestrians. The RangeRCNN [8] network uses the expansion convolution to extract features of RV images, uses the RV-PV (point view)-BEV module to convert features from RV to BEV, and then generates 3D proposals on BEV. **Hybrid 3D detection** The hybrid method attempts to fuse the point-based method, voxel-based method, and pseudo-image-based method to achieve the purpose of complementation and then to obtain feature vectors containing rich information to improve the performance of the 3D object detector. The PV-RCNN [11] network uses PointNet++, sparse convolution and 2D convolution to extract original point features, voxel features, and BEV features, respectively, and then aggregates these features to a small number of key points sampled for refinement of 3D object recommendations in the second stage. The PV-RCNN++ [20] network improves the strategy of the original network sampling key points and the feature aggregation method, reduces the network computing cost and improves the running speed while ensuring the accuracy.Considering the sparsity of point clouds, deformable PV-RCNN [21] adds the idea of deformability to PV-RCNN, adaptively extracting features based on the density and object size of point clouds and improving the detection accuracy of small and distant targets. RSN [22] predicts the foreground spots from RV images, voxelizes the predicted foreground spots, and applies sparse convolution, which increases the efficiency of sparse convolution and object detection accuracy. Among the above methods, most methods generate 3D object proposals using single-view feature maps, but there is a problem of information loss in single feature maps. Therefore, we propose the multi-view joint learning and BEV feature-fusion network to fuse the features of multiple feature maps for information compensation, thereby generating fused feature maps for predicting high-quality 3D object proposals.
## 1 Introduction
[PERSON] [21] studied the effect of urea-formaldehyde (UF) resin containing E-51 epoxy resin microcapsules on the self-healing properties of cement-based materials, and he found that the mortar containing microcapsules healed cracks as efficiently as 45.6%, and the recovery rates of compressive strength and permeability were 13.0% and 19.8%, respectively. Although the microencapsulated self-healing technology has good application prospects and good research progress, it still faces the following problems to be solved: (1) Currently, microcapsules are prepared by chemical methods, such as in situ polymerization or interfacial polymerization, which carry the risk of producing chemical contamination; (2) The microcapsules are difficult to rupture under crack tip stress due to the high mechanical strength of the thermosetting polymeric shell (St-DVB, UF, etc.) [22]. To address the above problems, microcapsules (microcrystalline wax containing E-51 epoxy resin) have been prepared and applied to cement-based materials for self-healing in previous research work [23]. However, although these microcapsules rupture more easily under external forces, the mechanical properties of the shell material are relatively low and the risk of rupture during mixing process is high. Moreover, in addition to mechanical properties, leakage of the core material (healing agent) is also a major factor affecting the self-healing ability of microcapsules. Recently, several researchers have synthesized shell materials by adding nanomaterials to improve the mechanical properties and compactness of microcapsules. [PERSON] [24] studied the synthesis of nano-Al\({}_{2}\)O\({}_{3}\) modified poly(methyl methacrylate-co-methyl acrylate) coated paraffin. [PERSON] [25] prepared microcapsules by using nano-Fe\({}_{3}\)O\({}_{4}\) mixed with paraffin to coat isocyanate. These studies showed that it is feasible to improve the mechanical properties and compactness of microcapsules by incorporating nanomaterials into the shell material. In this paper, microcapsules of nano-CaCO\({}_{3}\)/ceresine wax composite shell and E-44 epoxy resin healing agent were prepared via the melt condensation method in order to improve the micromechanical properties and compactness of microcapsules. The core content and compactness of microcapsules were measured. The particle size distribution, morphology, chemical structure and micromechanical properties of the microcapsules were characterized. The microcapsules were mixed into the mortar, and the pore size distribution, compressive strength recovery rate and chloride ion diffusion coefficient recovery rate of the pre-damaged mortar containing microcapsules after self-healing were measured. The self-healing ability of microcapsules on mortar surface cracks was evaluated.
## Experimental Results
### Overall Performance of SaSLM  
In this section, in order to answer **RQ2**, we evaluate SaSLM for SPL, where only set-level ratings are available. We report the predictions of both set-level ratings and item-level ratings. Since we learned from set-level ratings, the set-level performance shows the learning capabilities of the different models. In comparison, the item-level results show the performance in our proposed SPL problem. As we have discussed in Section 4.1, we tried both MF and neural collaborative filtering (NCF) for rating prediction. Therefore, we append the postfix -MF or -NCF to distinguish the SPL-specific methods. We also compare these with two baseline methods, MFSet and BCF.
# A Highly Glyphosate-Resistant EPSPS Mutant from Laboratory Evolution
## 3 Results  
### Isolated Evolved Mutants Exhibit a Significant Increase in Glyphosate Tolerance  
In ALE experiment, a total of four single-nucleotide substitutions were identified and only one amino acid substitution (Y40I) was observed. To evaluate the glyphosate tolerance of the GR79(Y40I)-EPSPS mutant, the plasmid was retransferred into ER2799, which is a defective mutant that cannot grow in M9 minimal medium. The strains containing GR79-EPSPS and GR79(Y40I)-EPSPS grew well, except ER2799 and pACYC184(ER2799 strain containing pACYC184 plasmid) (Figure 1A). When the glyphosate concentration was increased to 200 mM, the growth rate of the recombinant strains slowed down, and only the strain containing the GR79(Y40I)-EPSPS mutant grew when glyphosate reached 300 mM (Figure 1B,C). However, none of the strains grew under 400 mM glyphosate (Figure 1D). The results indicated that the GR79(Y40I)-EPSPS mutant exhibited significantly increased glyphosate tolerance compared with that of the wild-type enzyme.
###### Abstract  
Convolutional neural networks (CNNs) have attracted significant attention as a commonly used method for hyperspectral image (HSI) classification in recent years; however, CNNs can only be applied to Euclidean data and have difficulties in dealing with relationships due to their limitations of local feature extraction. Each pixel of a hyperspectral image contains a set of spectral bands that are correlated and interact with each other, and the methods used to process Euclidean data cannot effectively obtain these correlations. In contrast, the graph convolutional network (GCN) can be used in non-Euclidean data but usually leads to over-smoothing and ignores local detail features due to the need for superpixel segmentation processing to reduce computational effort. To overcome the above problems, we constructed a fusion network based on the GCN and CNN which contains two branches: a graph convolutional network based on superpixel segmentation and a convolutional network with an added attention mechanism. The graph convolutional branch can extract the structural features and capture the relationships between the nodes, and the convolutional branch can extract detailed features in the local fine region. Owing to the fact that the features extracted from the two branches are different, the classification performance can be improved by fusing the complementary features extracted from the two branches. To validate the proposed algorithm, experiments were conducted on three widely used datasets, namely Indian Pines, Pavia University, and Salinas. An overall accuracy of 98.78% was obtained in the Indian Pines dataset, and overall accuracies of 98.99% and 98.69% were obtained in the other two datasets. The results show that the proposed fusion network can obtain richer features and achieve a high classification accuracy.  
hyperspectral images; convolutional neural networks; graph convolutional networks; feature fusion +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network. _Appl. Sci._**2023**, 1743. [[https://doi.org/10.3390/appl3127143](https://doi.org/10.3390/appl3127143)]([https://doi.org/10.3390/appl3127143](https://doi.org/10.3390/appl3127143))
## 4 Performance Evaluation in a Capsule Prototype  
In order to validate the performance of the proposed lossless compression algorithm in a real-world scenario, it is employed inside a capsule endoscopy prototype. The prototype is developed in our lab that consists of three major units--the capsule, the data logger and the computer software running in desktop computer.