esa-sceva/satcom-chunk-collection huggingface dataset:
# PaperNet: A Dataset and Benchmark for Fine-Grained Paper Classification
###### Abstract  
Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area.  
artificial intelligence application; dataset; multi-modal information processing; machine learning; paper classification +
Footnote †: journal: Medical Image Processing  
**Clustering:** [PERSON], [PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON]; PaperNet: A Dataset and Benchmark for Fine-Grained Paper Classification. _Appl. Sci._**2022**, 12, 4554. [[https://doi.org/10.3390/app12094554](https://doi.org/10.3390/app12094554)]([https://doi.org/10.3390/app12094554](https://doi.org/10.3390/app12094554))  
Academic Editor: [PERSON]  
Received: 10 April 2022  
Accepted: 25 April 2022  
Published: 30 April 2022  
**Published:** **Notic:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
In this paper, we introduce the PaperNet-Dataset, which includes 12 datasets and multi-modal data, and ran experiments on it using current mainstream classification models. PaperNet contains 2 coarse-grained (CV and NLP) and 20 fine-grained (7 in CV and 13 in NLP) classes.  
The main contributions of this paper are summarized as follows:  
* We introduce PaperNet-Dataset, which contains multi-modal data (text and figure) for fine-grained paper classification. To the best of our knowledge, this is the first multi-modal fine-grained paper dataset. In addition, it was pre-processed for convenience of use.  
* Extensive experiments using current mainstream models were conducted to evaluate PaperNet. None of them reached an accuracy of 80%. This shows that fine-grained paper classification is a challenging task and PaperNet could be used as a worthy benchmark.
* Additionally, we propose a multi-modal paper classification method as a potential direction for better performance. The proposed method combines the strengths of MobileNetV3 and Albert for multi-modal representation fusion and shows promising results.
# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 4 Experiments and Results
### Experimental Results  
We conducted several experiments to examine the effect of the ensemble classifier by measuring the accuracy (Acc) on the test dataset. We considered widely used high-performance CNN architectures (ResNet152, DenseNet201, EfficientNetB0) as base learners to show the effectiveness of the proposed CNN architectures (ResNet50_CA, ResNet50_SE, ResNet50_NL) as base learners. Further, to show the effectiveness of our proposed fusion method, we performed experiments with max voting and entropy voting.
## 6 Results
### Max PV with Respect to PR  
The results of the PR sensitivity analysis are presented in Figure 11. In order to reveal the effect of PR, some values within Monte Carlo simulation were fixed. The number of PVs was fixed to 3, the location of PVs was fixed to buses 2, 5 and 8. Finally, one measured data array was used, that was modified as previously described in Section 2.  
Figure 10: Voltage unbalance attenuation with optimal phase (**a**) and corresponding PV injection (**b**) at the lowest and highest background VU.  
Figure 9: Maximum PV injection at optimal phase selection.  
In case of phase connection strategy 1, the PR of background negative sequence voltage has almost no effect of HC. Maximum PV injection is governed by other parameters, such as phase selection. Chaotic phase connection causes high VU and the background unbalance has smaller relative contribution. However, if the phases are fixed, as in the case of strategy 2, a trend starts to be revealed. The maximum injection falls as the PR value deviates from 1. Strategy 3 underlines the above-mentioned conclusions. Three clusters of results can be distinguished in case of strategy 3, that corresponds to the starting phase of sequential phase selection. Low PR has negative impact on maximum PV injection at favourable starting phase. On the other hand, PR has no impact on the least favourable starting phase, as the PV-caused VU is dominating the total unbalance.
# Aircraft Target Interpretation Based on SAR Images
## 4 Experimental Results and Analysis
### Experimental Evaluation  
To accurately evaluate the detection performance, this paper uses the average precision (_AP_) and the mean average precision (_mAP_) as the evaluation indicators. _AP_ can reflect the detection performance of a single target category, and _mAP_ can reflect the comprehensive detection performance of all categories. _AP_ is calculated by precision and recall. The precision and recall are expressed as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Configuration** \\ \hline CPU & Inter(R) Core(TM) i7-7820X CPU @ 3.60 GHz \\ \hline GPU & NVIDIA TITAN Xp \\ \hline Accelerator & CUDA 10.2 \\ \hline Architecture & Pytorch 1.9 \\ \hline Language & Python 3.8 \\ \hline \end{tabular}
\end{table}
Table 2: Computer software environment configuration.  
Figure 8: Examples of 7 types of aircraft.  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
where \(TP\) represents a positive sample that predicts to be a positive example; \(FP\) represents a negative sample that predicts to be a positive example; \(FN\) represents a positive sample that predicts to be a negative example. At a specific Intersection over the Union (\(IoU\)) threshold, the \(PR\) curve is drawn with recall (\(R\)) as the horizontal axis and precision (\(P\)) as the vertical axis. \(AP\) and \(mAP\) are expressed as follows:  
\[AP=\int_{0}^{1}P(R)dR \tag{10}\]  
\[mAP=\frac{1}{C}{\sum AP(C)} \tag{11}\]  
where \(C\) represents the total number of the target category. This paper adopts \(mAP_{50}\) (\(IoU\) is 50%), \(mAP_{75}\) (\(IoU\) is 75%), and \(mAP_{50-95}\) (\(IoU\) is from 50% to 95%, step is 5%) to evaluate the precision of detection and recognition.
# PpyNT/NR/NBR Composites with Excellent Microwave Absorbing Performance in X-Band
## 3 Results and Discussion
### MA Performance of PPyNT/NR/NBR Composites  
Normally, an excellent absorber should allow the incident electromagnetic wave to penetrate it to the largest extent and simultaneously attenuate the entering wave within the limited thickness [23]. In the case of rubber composites, absorbing fillers modify the electromagnetic parameters of composites to fulfill these requirements. Therefore, their content in the rubber matrix plays a vital role in deciding the final MA performance. Meanwhile, different phase morphologies such as co-continuous and matrix-droplet structures can be formed by varying the blend ratio of rubber matrices, which greatly impact the final properties of the composites [24]. In this context, two decisive factors (i) PPyNT content and (ii) the blend ratio of NR/NBR are investigated in detail to gain the optimal PPyNT/NR/NBR composite with excellent MA performance.
# Ripple Suppression in Broadband Microwave Photonic Phase Shifter Frequency Response
## 3 Experimental Results
It had a center wavelength of 1558.05 nm, and a \(-3\) dB and \(-20\) dB bandwidth of 0.62 nm and 1.03 nm, respectively. Its roll-off factor was around 83 dB/nm (0.67 dB/GHz). The optical filter passed both the optical carrier and the wanted right sideband but suppressed the unwanted left sideband, especially at the frequencies far away from the optical carrier. Figure 8 shows that the unwanted left sideband at the frequency of 32 GHz away from the  
Figure 5: Experimental setup of the microwave photonic phase shifter. Figure 6: Optical spectrum at the output of the dual-polarization DPMZM with (red dotted line) and without (blue solid line) the optical carrier from the lower DPMZM. The upper DPMZM was driven by a 32 GHz RF signal. The optical spectrum analyzer (OSA) resolution bandwidth was 0.02 nm. optical carrier was suppressed to the OSA noise level. Therefore, a single sideband RF modulated optical signal with large unwanted sideband suppression can be obtained not only for low RF signal frequencies but also for high RF signal frequencies. The measured amplitude and phase response of the MPPS with the inclusion of an optical filter are shown in Figure 9. It can be seen from the figure that the MPPS exhibits a flat amplitude and phase response performance over a wide bandwidth from 4 to 40 GHz. The amplitude variation was \(<\)2.2 dB and the phase deviation was \(<\)\(\pm\)3.8\({}^{\circ}\) for a \(-\)180\({}^{\circ}\) to 180\({}^{\circ}\) phase shift over the MPPS bandwidth. Similar amounts of amplitude variation and phase deviation in the phase shifter frequency response were obtained when the 90\({}^{\circ}\) hybrid coupler used in the experiment was replaced with the other two 90\({}^{\circ}\) hybrid couplers with the frequency responses shown in Figure 2. These small amplitude variations and phase deviations are due to the residual carrier at the output of the OFS and the noise present in the system. Nevertheless, the experimental results verify the concept of inserting a low cost optical filter in a MPPS implemented using a 90\({}^{\circ}\) hybrid coupler can overcome the amplitude variation and phase deviation problem caused by the 90\({}^{\circ}\) hybrid coupler amplitude and phase imbalance. Figure 7: (**a**) Amplitude and (**b**) phase response of the MPPS without an optical filter. Figure 8: Optical spectrum at the output of the dual-polarization DPMZM followed by an optical filter with (**red dotted line**) and without (**blue solid line**) the optical carrier from the lower DPMZM. The response of the optical filter used in the experiment (**black dashed line**). The OSA resolution bandwidth was 0.02 nm. Figure 9: (**a**) Amplitude and (**b**) phase response of the MPPS with an optical filter inserted after the polarizer to suppress the unwanted sideband.
# Underwater Degraded Image Restoration by Joint Evaluation and Polarization Partition Fusion
## 3 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c} \hline \hline Camera Sensor Resolution & 2248 \(\times\) 2048 px, 5.0 MP \\ \hline operating temperature & \(-\)20 to 50 \({}^{\circ}\)C \\ active light & Rawray 150 W LED Light \\ linearly polarized light & USP-50C0.4-38 \\ High-Transparency Acrylic Tank & 40 cm, 40 cm, 50 cm with over 96\% light transmission \\ software platform & matlab2020a \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Working environment. the image visual quality. Objectively, according to the results of the experimental evaluation indexes, EME, Entropy, and Contrast, our method performs significantly better than the other methods, which confirms the feasibility of this paper's algorithm for application in specific underwater scenarios. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Underwater** & \multicolumn{3}{c}{**Joint Evaluation Indicators**} \\ \cline{2-4}
**Experiment 1** & **EME \(\uparrow\)** & **Entropy \(\uparrow\)** & **Contrast \(\uparrow\)** \\ \hline Origin Image & 6.026 & 5.180 & 3.216 \\ [PERSON]’s method & 12.912 & 5.412 & 24.318 \\ [PERSON]’s method & 20.443 & 6.312 & 32.048 \\ [PERSON]’s method & 23.734 & 4.777 & 15.181 \\ Our method & 18.727 & 6.751 & 45.058 \\ \hline
**Underwater** & \multicolumn{3}{c}{**Joint Evaluation Indicators**} \\ \cline{2-4}
**Experiment 2** & **EME \(\uparrow\)** & **Entropy \(\uparrow\)** & **Contrast \(\uparrow\)** \\ \hline Origin Image & 4.504 & 5.305 & 2.253 \\ [PERSON]’s method & 7.941 & 4.748 & 2.812 \\ [PERSON]’s method & 8.349 & 5.799 & 9.790 \\ [PERSON]’s method & 11.214 & 5.692 & 11.673 \\ Our method & 8.867 & 6.255 & 25.987 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Final experimental data (red is the maximum value, blue is the minimum value, \(\uparrow\) indicates that the larger evaluation index, the better the image quality). Figure 8: Final experimental results.
# Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production
## 3 Methodology  
### Overview of Test Sites and UAVs  
From the regulations related to aerial photogrammetry with UAV, the workflow is the following: 1. Establishment of a plan/process; 2. Installation of target signboards for aerial surveying and surveying of GCPs; 3. Unmanned aerial photography; 4. Aerial triangulation; 5. Creation of a digital surface model (DSM), etc.; 6. Orthophoto production; 7. Description of topography (with topographical features); 8. Production of a digital topographic map; and 9. Quality control and evaluation/summary. In this study, the values of the geolocation RMSE of an orthophoto were analyzed through the workflow, up to No. 6.  
Considering the current regulatory requirements and applying the required standards to the target area of this study, because the study site was a flat, low-lying area, the appropriate percentages of overlap (65%) and sidelap (60%) were determined for performing unmanned photogrammetry with UAVs. In addition, the modeling and analysis results indicated that the values of the root mean square error (RMSE) needed to be within \(\pm\)0.5 based on the map scale of 1/500 to 1/600.  
With respect to the selection of a study site, the \"ultralight vehicle flight areas\" as prescribed by the relevant regulations were considered as the top priority. In addition, an area of at least 500 m \(\times\) 500 m needed to be be secured, and the site could not have restrictions on the installation of GCPs. Considering these considerations, an area in Gyeongsan-si, Gyeongsangbuk-do Province, in Korea, was selected, as depicted in Figure 1 for conducting the activities of UAV photogrammetry and research, and the area of the study site was approximately 1,000,000 m\({}^{2}\) (\(\sim\)1 km\({}^{2}\)). The specifications of the UAV used in the experiments of this study are presented in Table 1.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
###### Abstract  
Acoustic emission (AE) source localization technology, since the early application to one-dimensional structures, has been extended to a wide range of applications to two-dimensional (2D) structures, including isotropic and anisotropic materials, which are currently the most widely studied and the most mature. With the development of AE source localization technology, more and more significant challenges have arisen for three-dimensional (3D) structures, which are mostly anisotropic and have complex propagation paths. This paper summarizes and discusses the AE source localization methods in different dimensions as well as their applications, including the main methods for 2D AE source localization, such as the triangulation method, beam forming, strain rosette technique, modal AE, artificial neural network, optimization and the time reversal technique, as well as state-of-the-art AE source localization methods in isotropic and anisotropic structures utilizing these methods. Recent advances in AE source localization in complex 3D structures are also reviewed.  
acoustic emission; beam forming; source localization +
Footnote †: journal: _sciences_
## 3 Field Measurements and Model Verification
### Model Verification  
Figure 8 shows a comparison between the ground vertical acceleration at the detection point calculated using the prediction model and field-measured results. The results obtained via model calculations in the time domain and the one-third octave (1/3 oct) band spectra are consistent with the on-site measurement results, demonstrating the accuracy and reliability of the prediction model. Meanwhile, due to differences between the US spectrum and rail irregularity in the actual testing conditions in our paper, the results calculated by the model and obtained by testing had differences. The difference between the irregularities considered in the model and existing in the test is one of the main causes. But the calculated results could be used as the excitation source for the FE model when the environmental vibration issues were discussed, and the established model can be applied to further analysis and calculations in the following sections.  
Figure 7: Typical fastener reaction force.
# English Speech Emotion Classification Based on Multi-Objective Differential Evolution
## 4 Experimental Results and Analysis
### Experimental Analysis
#### 4.2.1 Simulation Results on the KNN Classifier
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**MOGA**} & \multicolumn{2}{c}{**MODE**} & \multicolumn{2}{c}{**NSGA-II**} & \multicolumn{2}{c}{**MODE-NSF**} \\ \cline{2-9}  & **AVG** & **STD** & **AVG** & **STD** & **AVG** & **STD** & **AVG** & **STD** \\ \hline eNTERFACE05 & 0.2820 & 0.1393 & 0.3245 & 0.0923 & 0.1441 & 0.1308 & **0.0564** & 0.0616 \\ RAVDESS & 0.2437 & 0.0677 & 0.2940 & 0.0446 & 0.0995 & 0.0554 & **0.0623** & 0.0336 \\ SAVEE & 0.2167 & 0.0642 & 0.2546 & 0.0433 & 0.1220 & 0.0543 & **0.0783** & 0.0507 \\ TESS & 0.2314 & 0.0754 & 0.2933 & 0.0579 & 0.1541 & 0.0733 & **0.1070** & 0.0635 \\ \(>\)/\(=\)/\(<\) & 0/0/4 & & 0/0/4 & & 0/0/4 & & 4/-/- \\ Rank & 3 & & 4 & & 2 & 1 & \\ \(p\)-Value & \(2.56\times\) & & & & & & \\ \(10^{-2}\) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The IGD values of the algorithms. Figure 3: Pareto optimal solutions acquired by the algorithms. For eNTERFACE05, MODE-NSF obtained more solutions than MOGA, MODE, and NSGA-II. Furthermore, it achieved the highest recognition accuracy of 49%, which was superior to the other algorithms. The solutions of NSGA-II were located in a low dimension, and the solutions of MOGA and MODE were mainly in a medium dimension. For RAVDESS, the solutions of MODE-NSF were distributed over the entire feature space. The solutions of NSGA-II remained in a low dimension, and the solutions of MOGA and MODE were located in a middle dimension. Although MODE did not achieve as many optimal solutions as MODE-NSF, it outperformed MODE-NSF in obtaining an optimal recognition accuracy. For SAVEE, MODE-NSF presented with diverse characteristics. The solutions of NSGA-II were in a low dimension, and the solutions of MOGA and MODE were in a medium dimension. The recognition accuracy of the Pareto optimal solutions obtained by NSGA-II, MOGA, and MODE was better than that obtained by MODE-NSF. For TESS, MODE-NSF excelled in terms of diversity and accuracy. NSGA-II achieved an accuracy of 90% using a small number of features, and MOGA acquired a better accuracy in the middle dimension than MODE. From the Pareto optimal solutions, it was found that MODE-NSU utilized the NSF-guided mutation to enhance the solution's diversity and balances exploration and exploitation. Table 5 presents the running time of the algorithms. MODE had the shortest computation time using eNTERFACE05, and MODE-NSF exhibited the quickest execution time using RAVDESS, SAVEE, and TESS. The proposed algorithm exhibited a fast execution and low time complexity. It is worth noting that the algorithms required less time to execute using eNTERFACE05 and SAVEE compared to TESS. This is because TESS contains a larger number of samples, which impacted the execution of the algorithms.
## 3 MAV Communication
### Flight  
Different levels of autonomy require different amounts of communication. If the on-board controller is solely responsible for stability, then a human operator must remote-control the flight. In that case, the base station needs to obtain sensor data of sufficient fidelity to understand the environment in order to direct the MAV. This is generally at least video at a few frames per second, perhaps augmented with additional inertial sensor data. This typically requires a throughput on the order of 100 kbps. The operator must also send control signals back to the MAV. This is generally a much smaller amount of data, on the order of tens to hundreds of bits per second.  
This bidirectional data needs to be transmitted in real time and minimizing latency is key. Without intelligence on board the MAV, a network delay on the order of tenths of a second could drive the MAV into a wall or obstacle. Slower, more stable MAVs may be able to tolerate the delay, but may waste energy in low speed movement. As MAV functionality is optimized, round trip latency becomes even more critical.  
The required data rates and latencies are typically not difficult to achieve in a point-to-point link, and can even be attained over a short multihop path. However, the performance rapidly degrades as the distance between the MAV and its base station increases. Each additional hop along a network path adds both latency and network traffic, and as network traffic increases, the constant total bandwidth results in lowered end-to-end data throughput.  
This saturation can be alleviated by requiring autonomy of the MAV. This can range from merely obstacle avoidance along a user-defined waypointed path to full autonomous path planning and guidance decisions. Autonomy thus minimizes or eliminates intervention from a human operator, and similarly, reduces the data that then needs to be sent to the base station for flight control. Nonetheless, data throughput and latency are still important parameters that impact the ability of human operators to influence the MAV mission.