esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparative Study  
The results of the performance comparison between our proposed research and previously published research are presented in Table 2, where the size of the MMA, material, absorbance, polarization, incident angle stability, and operation range are compared. The findings indicate that the proposed MMA we designed is more compact than the study provided as a reference. In comparison to the previous research, the absorbance of the MMA that has been proposed is satisfactory. The proposed SiO\({}_{2}\) and W-based MMA offers 70\({}^{\circ}\) stability for absorbance levels greater than 70%, while operating over a more extensive  
Figure 16: The proposed MMA absorbance comparison with different simulation software.  
working bandwidth. Moreover, a peak absorbance rate of 99.99% is also attained. However, the proposed design is appropriate for absolute invisible layers, substantial absorbance, wide-angle stability, magnetic resonance imaging (MRI), color images, thermal imaging, solar cells, etc.
## 2 Methods
### Extraction of the Grid Stripe Features  
The grid pattern consists of a number of intersecting light stripes. The common algorithms for stripe center extraction are inaccurate [35; 36]. The intersections of the grid lines could be precisely extracted based on our previous work [37]. Thus, an effective extraction method of the grid stripe features is proposed. The procedures of the method are as follows:  
1. The corner points of the grid lines are detected by using the Shi-Tomasi algorithm [38]. The green points in Figure 2(b) indicate the corner of the intersecting lines.
2. Then the corner points are clustered into \(m\) groups by using the DBSCAN algorithm [39]. The points in the same group wrap one of the intersecting regions \(R_{i}\) (\(i=1\), \(2\), \(\dots\)\(m\)) as shown in Figure 2(c).
3. After that, the center points of the regions can be easily obtained and sorted to be the markers (Figure 2(d)).
4. The gray centroid method is used to extract the strip centers between adjacent markers (Figure 2(e)). Finally, the horizontal stripes, \(L_{Hi}\), and vertical stripes, \(L_{Vj}\), are obtained and topologically labeled (Figure 2(f)).  
During the calibration, the grid pattern should be projected on the calibration board (Figure 4). The white circles on the board can disturb the corner extraction (Figure 4(b)). To solve the problem, the gray value of the ellipse region on the input image is properly attenuated. The ellipses are extracted during the calibration. The maximum gray value in each ellipse is used as the threshold of gray filtering to remove the interference of the white circles. After the image gray attenuation, the corner detection of the output image (Figure 4(d)) can achieve the correct result (Figure 4(e)). Hence, the extraction of grid stripe features in a captured image is guaranteed by using the proposed algorithm. In Figure 6, the red lines tag the extracted horizontal stripes, while the yellow lines tag the vertical ones.  
Figure 4: Grid pattern on the calibration board.  
Figure 5: Image gray attenuation for the grid pattern on white circles. (**a**) Input image; (**b**) The error occurred of corner detection on the input image; (**c**) Gray attenuation process; (**d**) The output image after the attenuation; (**e**) The correct corner detection result.  
Figure 3: Schematic diagram of the grid stripe feature extraction. (**a**) Input image; (**b**) Corner detection result; (**c**) Intersecting regions; (**d**) Markers; (**e**) Strip centers; (**f**) Horizontal and vertical stripes.
## 5 Experiments and Results
### Experiment 2: Boston University Dataset  
ReSVM outperformed the state-of-the-art approaches on the Boston University dataset as can be seen from Table 2 and Figure 9. As can be observed, the accuracy of ResNet-50, ResNet-101, VGG-19, MobileNet, InceptionV3, and Xception are reduced drastically in this dataset as compared to SFDDD. For instance, the percentage decrease in accuracy of VGG-19, ResNet-101, and ResNet-50 is [47.09%, 44.44%, and 87.15%], respectively. This is due to dim light conditions and lack of clarity in the frames of this dataset. However, ReSVM-50 remained comparatively stable with a percentage decrease of 3.31%. Both ReSVM-50 and ResNet-50 use the same deep features, however, they differ in classifier. The combination of deep features of ResNet-50 along with the SVM classifier is responsible for this stable performance of ReSVM. One reason includes the ability of the SVM classifier  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ds** & **R-50** & **R-101** & **V-19** & **M** & **I-V3** & **Xp** & **ReSVM** \\ \hline D1 & 89.00 & 86.00 & 92.15 & 95.00 & 94.00 & 94.00 & 95.50 \\ D2 & 87.30 & 44.44 & 47.09 & 60.32 & 86.24 & 85.71 & 90.46 \\ D3 & 87.61 & 45.90 & 39.34 & 80.33 & 85.25 & 85.25 & 93.44 \\ D4 & 82.50 & 54.50 & 48.50 & 94.00 & 92.00 & 91.50 & 94.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of ReSVM in terms of accuracy with existing architectures on four datasets, namely: (i) State Farm Distracted Driver Detection, (ii) Boston University, (iii) DrivFace, and (iv) FT-UMT datasets. DS = Dataset, D1 = SFDDD, D2 = BU, D3 = DrivFace, D4 = FT-UMT, R-50 = ResNet-50, R-101 = ResNet-101, V-19 = VGG-19, I-V3 = InceptionV3, M = MobileNet, Xp = Xception.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Epochs/LR** & **R-50** & **R-101** & **V-19** & **M** & **I-V3** & **Xp** & **ReSVM** \\ \hline Epochs & 20 & 60 & 70 & 20 & 20 & 20 & 20 \\ LR & 0.10 & 0.10 & 0.10 & 0.10 & 0.10 & 0.10 & 0.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal parameters (number of epochs and learning rate) of ReSVM and state-of-the-art deep networks. R-50 = ResNet-50, R-101 = ResNet-101, V-19 = VGG-19, I-V3 = InceptionV3, M = MobileNet, Xp = Xception.  
Figure 7: Comparison of ReSVM with existing state-of-the-art approaches in terms of percentage accuracy on SFDDD dataset.  
to show good performance on small samples and large features [61]. The accuracy of VGG-19 drastically decreases in this dataset as compared to SFDDD. One reason for this degradation in performance is the lack of a large amount of training data [62].  
Figure 8: ROC-AUC of ReSVM using (**a**) SFDDD dataset, (**b**) BU dataset, (**c**) DrivFace dataset, and (**d**) FT-UMT dataset.  
Figure 9: Comparison of ReSVM with existing state-of-the-art approaches in terms of percentage accuracy on BU dataset.
## 4 M2M Communication  
M2M Communication technologies are reviewed in this chapter. Afterwards, a set of potential technologies is shortly overviewed: Constrained Application Protocol (CoAP), EXtensible Messaging and Presence Protocol (XMPP), 6 LowPAN and RPL, and Bluetooth Smart. Finally, autonomic features and communication technologies are discussed.
# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
## 5 Experimental Results and Discussion  
### Performance Analysis for Varying the Number of Histogram Bins  
In this experiment, the face regions were first detected and aligned using a face registration algorithm that automatically positions the eyes and crops on the face image to discard background pixels. Then, the aligned face images were resized to a resolution of 100 \(\times\) 100 pixels. By setting the size of the sub-regions to 13 \(\times\) 13 pixels (i.e., 49 sub-regions in total), feature vectors were extracted from each image. The number of histogram bins was varied to reduce the feature vector length while achieving a high recognition rate. Note that for WLD, the histogram bins were set as \(M=2\), \(S=2\), and \(T\) was varied from 2 to 24. Figure 4 gives the results (results are given as the average accuracy during 10-fold cross-validation) obtained when the number of histogram bins is varied for different descriptor and classification combinations. We observe different trends depending on the type of classifier used. When SVM and MLP classifiers are used, the classification performance either improves or stays the same as the number of bins increases. A similar result is seen in the KNN classifier, although the large size of the features hurts the recognition rates from some descriptors (e.g., with the LDP descriptor, the performance decreases as the number of bins increases). On the other hand, when using the NB classifier, the recognition rates decrease as the number of bins rises, regardless of the type of features used.
# Comparison of Dual-Combustion Ramjet and Scramjet Performances Considering Combustion Efficiency
## 2 Materials and Methods
### Algorithm Verification
#### 2.3.1 Algorithm Validation of Scramjet  
The data of Figure 5d in reference [28] were selected to verify the correctness of the algorithm. The verified geometric model is shown in Figure 2a, and the inlet Mach number of the combustor was three. Gaseous C\({}_{12}\)H\({}_{23}\) was used for kerosene, and other boundary conditions were the same as the fourth condition in Table 1 in the reference.  
Firstly, the grid convergence characteristics were verified. Grids with quantities of 0.1, 0.19, 0.34, and 1.34 million were drawn, and the boundary conditions were set according to the reference. After the numerical calculation was completed, the convergence analysis between the calculation results and the grid scale was conducted [29]. Taking the calculation results of the 1.34 million grids as the exact value, the relative errors of the average total temperature of the 0.10, 0.19, and 0.34 million grids were 2.24%, \(-\)0.09%, and 0.012%, respectively, as shown in Figure 3a. The relative errors rapidly decreased as the equivalent mesh size decreased, indicating the realization of mesh convergence.  
Then, the wall pressure obtained by the three mesh calculations was compared with the experiment (Figure 5d in the reference), as shown in Figure 3b. The maximum wall pressures corresponding to the meshes with 0.10, 0.19, and 0.34 million grids were 137.6, 123.9, and 123.1 kPa, respectively. When the number of grids increased from 0.10 million to 0.19 million, the maximum wall pressure decreased by 11%. When the number of grids increased from 0.19 million to 0.34 million, the maximum wall pressure decreased by only 0.6%. This shows that when the number of grids is greater than 0.19 million, the simulation results remain basically unchanged. Therefore, the number of grids used for subsequent analysis of scramjet was 0.19 million.
# Passive Vibration Control of a Semi-Submersible Floating Offshore Wind Turbine
###### Abstract  
Floating offshore wind turbines have the potential to commercially convert the vast wind resource in deep-water area. Compared with fixed-bottom wind turbines, motions of the floating foundation complicate vibrations and loads of the wind turbine in offshore environment. To alleviate the responses of the wind turbine, this study investigates the use of fore-aft tuned mass damper (TMD) in nacelle/tower for passive control of a semi-submersible offshore wind turbine. A simplified structural model, considering the degree-of-freedom of platform pitch and surge, tower tilt and TMD translation, is proposed in the light of motion features of semi-submersible platform. After identifying ten unknown parameters, the correctness of the deterministic model is validated by pitch free decay responses. The mass, stiffness and damping of TMD are optimized using both method of exhaustion and genetic algorithm to avoid local minimum. Six optimized TMD devices are evaluated under three kinds of realistic environment conditions. The control effectiveness is assessed by the extreme and fatigue response reduction ratios. It is found that the high stiffness TMDs that directly dissipate the energy of tower oscillation exhibit an overall stable performance. Similar to the spar-type foundation, the TMDs in the nacelle/tower are capable of extending the service life of floating wind turbines.  
t 20 March 2017, 7, 509; doi:10.3390/app7060509
## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.1 Edge Computing Constraints
This means that although these solutions optimize one objective, they sacrifice performance for another objective. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Times**} \\ \cline{2-3}  & **Number of Non-Dominated** & **Number of Non-Dominated** \\  & **Solutions Is Zero** & **Solutions Is Maximum** \\ \hline MOBA-CV & 0 & 30 \\ \hline NSGA-II-CV & 0 & 0 \\ \hline MOPSO-CV & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of non-dominated solutions. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**L Latency Time (s)**} & \multicolumn{2}{c}{**Energy Consumption (W)**} \\ \cline{2-3}  & **Value** & **Difference** & **Value** & **Difference** \\ \hline MOBA-CV & 107.1 & 2.81 & 594 & X \\ \hline NSGA-II-CV & 144.3 & X & 671.5 & 77.54 \\ \hline MOPSO-CV & 145.5 & 1.2 & 628.7 & 34.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Latency time and energy consumption. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Diversity Metric**} \\ \cline{2-3}  & **Average** & **Percentage** & **Standard Deviation** \\ \hline MOBA-CV & 0.2098 & X & 0.0029 \\ \hline NSGA-II-CV & 0.2155 & 2.72\% & 0.0025 \\ \hline MOPSO-CV & 0.212 & 1.05\% & 0.0029 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of diversity metric without reinforcement learning.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 1 Introduction
### Mobile Ad Hoc Networks (MANETs), Flying Ad Hoc Networks (FANETs)  
The principle underlying MANETs is related to wireless connectivity, and that any device that also has access to a cabled connection to the broader infrastructure can relay traffic from other wireless-only devices if they are within the coverage area of the device. This has the potential to make wireless networks flexible and more resilient to failures [1]. Due to this potential, MANETs are currently deployed in commercial [2; 3], as well as in military [2; 4; 5], and in civil [6; 7] security [8] applications and have spawned specialized sub-fields such as Vehicular Ad Hoc Networks (VANETs) [9; 10; 11; 2].  
The dynamic and continuously evolving nature of wireless ad hoc networks that make use of mobile routers or relays is a driving factor in their usability and success. It is, however, also posing challenges as the dynamic topology of the network requires continuous re-evaluation of the routing within the network [3; 5]. The less the nodes are acting predictably, the more robust a network will have to be if it is to ensure connectivity at all times [12; 13]. Research into adaptive routing algorithms for dynamic topologies has received a lot of attention in the literature [14; 15; 16; 17]. In a more recent development, drone technology comes into play: Unmanned Aerial Vehicles (UAVs) are increasingly considered to be platforms for the routers of a Flying Ad Hoc Network (FANET) [1; 2; 3; 6; 18]. The potential benefits of deploying drones actively ensuring MANET or FANET connectivity and cooperating as a swarm to optimize coverage are significant [6].
# Contactless Fingerprint Recognition Using Deep Learning--A Systematic Review
## 2 Review Methodology
### Research Questions  
1. RQ1. How do different sensor systems capture finger images to ensure the acceptable quality of fingerprints? Research findings will help to investigate whether the capturing systems have any impact on the model architecture or the recognition performance.
2. RQ2. How does the classical machine-learning method preprocess the contactless finger images and prepare for recognition algorithms? Research findings explore the classical methods used for feature extraction, image segmentation, minutiae point extraction, image deblur, background noise removal, particular portion segmentation, and suitable feature extraction from finger images.
3. RQ3. How do deep neural networks replace the classical recognition models? The answer will explore the architecture of related deep neural networks and their performance improvement over traditional methods.
# Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management
## 3 Preliminaries and Background  
### Encryption for Secure Deduplication  
Clients are required to encrypt and store their data for security purposes. However, if each client encrypts data using their individual secret key, even identical data will produce distinct ciphertexts based on the client. As a result, cloud storage recognizes each ciphertext as a unique object, making data deduplication impossible. To address this issue,a proposed encryption method aims to enable clients with identical plaintext to utilize the same secret key.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane  
### Introduction to Control Plane  
The control plane in KDN is logically centralized and self-learning with respect to knowledge, where the control is based on both application policies and knowledge learned or rules derived from network data. As knowledge and rules are generated based on real-time data, adaptive control decisions can be made, such as identifying congested links based on knowledge of network traffic flow to direct traffic through less-congested  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Protocol/Framework/Method** & **Purpose** & **Developer** \\ \hline \multirow{7}{*}{Network management} & CONMan [249] & Complexity oblivious network management with minimum protocol specific data & H. Ballani et al. \\ \cline{2-3}  & PACMAN [250] & Network configuration management using active documents & X. Chen et al. \\ \cline{2-3}  & OF-CONFIG [251] & A protocol for configuring and monitoring OpenFlow switches & R. Narisetty et al. \\ \cline{2-3}  & Ontology based configuration [252] & Automatic configuration of switches using an OWL-based ontology & A. Martinez et al. \\ \cline{2-3}  & HybNET [253] & Automated network management framework in hybrid KDN and legacy networks & H. Lu et al. \\ \cline{2-3}  & SNMP [254] & A protocol for monitoring and configuration of network devices & J.D. Case et al. \\ \hline \multirow{3}{*}{Network monitoring frameworks} & Payless [255] & Network monitoring with adaptive network statistics collection & S.R. Chowdhury et al. \\ \cline{2-3}  & OpenSample [256] & Sample based network load and flow statistics monitoring & J. Suh et al. \\ \cline{2-3}  & OpenNetMon [257] & Monitoring using adaptive rate polling to collect per flow metrics & N.L.V.Adrichem et al. \\ \cline{2-3}  & HONE [258] & Traffic monitoring using traffic measurement data & P. Sun et al. \\ \hline \multirow{7}{*}{Data collection} & IQP [260] & Data collection minimizing communication cost, delay, and overhead & S.N. Wijesekara et al. \\ \cline{2-3}  & Packet sampling [261] & Collect sampled wild card flow entries & P. Wette et al. \\ \cline{2-3}  & Adaptive data collection [262] & Dynamically selects data collection nodes and samples network traffic based on flow characteristics & D. Zhou et al. \\ \cline{2-3}  & Sensor measurements collection [263] & Collects sensor measurements in an energy efficient manner & W.H. Liao et al. \\ \cline{2-3}  & Predictive data collection [264] & Predicts and uses multihop routing using ad hoc and cellular interfaces for data collection & Z. Jiao et al. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of management protocols, network monitoring frameworks, and data collection methods in management plane.  
links. This controlling approach is more efficient than the traditional SDN approach, where network policies determine network control and knowledge-based decisions are not made.
# A Survey on Machine Reading Comprehension--Tasks, Evaluation Metrics and Benchmark Datasets
###### Abstract  
Machine Reading Comprehension (MRC) is a challenging Natural Language Processing (NLP) research field with wide real-world applications. The great progress of this field in recent years is mainly due to the emergence of large-scale datasets and deep learning. At present, a lot of MRC models have already surpassed human performance on various benchmark datasets despite the obvious giant gap between existing MRC models and genuine human-level reading comprehension. This shows the need for improving existing datasets, evaluation metrics, and models to move current MRC models toward \"real\" understanding. To address the current lack of comprehensive survey of existing MRC tasks, evaluation metrics, and datasets, herein, (1) we analyze 57 MRC tasks and datasets and propose a more precise classification method of MRC tasks with 4 different attributes; (2) we summarized 9 evaluation metrics of MRC tasks, 7 attributes and 10 characteristics of MRC datasets; (3) We also discuss key open issues in MRC research and highlighted future research directions. In addition, we have collected, organized, and published our data on the companion website where MRC researchers could directly access each MRC dataset, papers, baseline projects, and the leaderboard.  
machine reading comprehension; survey; dataset +
Footnote †: journal:  
In the NLP community, machine reading comprehension has received extensive attention in recent years [4; 5; 6; 7; 8]. The goal of a typical MRC task is to require a machine to read a (set of) text passage(s) and then answers questions about the passage(s), which is very challenging [9].  
Machine reading comprehension could be widely applied in many NLP systems such as search engines and dialogue systems. For example, as shown in Figure 1, nowadays, when we enter a question into the search engine Bing, sometimes the Bing can directly return the correct answer by highlight it in the context (if the question is simple enough). Moreover, if we open the \"Chat with Bing\" in the website of Bing, as shown in the right part of the browser in Figure 1, we can also ask it questions such as \"How large is the pacific?\", the Bing chatbot will directly give the answer \"63.78 million square miles\". And on Bing's App, we can also open this \"Chat with Bing\", as shown in the right part of Figure 1. It is clear that MRC can help improve the performances of search engines and dialogue systems, which can allow users to quickly get the right answer to their questions, or to reduce the workload of customer service staff.