esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
## 4 Results and Discussion
#### Research Prospect  
We look forward to proceeding with our research in three areas. First, the latest version of YOLO, such as YOLOv4 or YOLOv5, will be used. They not only have higher accuracy, but take less time to train the model, and require little time to detect objects. Next, we will use machine learning frameworks to construct the deep neural networks. We may use convolutional layers to address the images of computer vision. Lastly, the x and y coordinates of the car are predicted separately in this research. Therefore, we will make an effort to train the model to predict multiple features at the same time. In addition, the velocity and acceleration of the car will be added as our training features. We hope that we are capable of producing a more efficient and precise model. What is more, we will build a system that can warn the drivers. When other vehicles enter our lane area, or their predicted trajectory will cross the driver's path, the system can warn the drivers to take the necessary measures. It can not only predict, but also issue warnings. It is a combination of all our work and also enhances driving safety.  
Figure 25: The combination of all techniques of the second validation. We demonstrated the results in three rows: (**a**) the first second the vehicle enters the sight from our left side, (**b**–**e**) the vehicle moves forward from our left side, (**f**) the last second the vehicle moves forward from our left side.
## 3 Visual Saliency Estimation
### Evaluation Measures
#### 3.3.1 Measures for Fixations, Scanpaths, and Saliency Maps  
The **Pearson Correlation Coefficient** (CC) [51] is a measure of the linear correlation between prediction \(P\) and ground truth \(G\) considered as two statistical variables:  
\[\text{CC}=\frac{\text{cov}(P,G)}{\sigma_{P}\sigma_{G}} \tag{1}\]  
where \(\text{cov}(\cdot,\cdot)\) is the co-variance, \(\sigma_{P}\) and \(\sigma_{G}\) are the standard deviation values for, respectively, the predicted saliency data and ground truth saliency data.  
The **Normalized Scanpath Saliency** (NSS) [52] is used to compare a densely-estimated saliency map with a fixation-based ground truth. Specifically, it is the average of the estimated saliency values \(P\) in the locations indicated by eye fixations \(f\):  
\[\text{NSS}=\frac{1}{F}\sum_{f=1}^{F}\frac{P(f)-\mu_{P}}{\sigma_{P}} \tag{2}\]  
Note that the saliency estimation map is normalized to have zero mean and unitary standard deviation through corresponding statistics \(\mu_{P}\) and \(\sigma_{P}\). In this scenario, a null NSS indicates a correspondence between estimation and ground truth equivalent to random chance. Conversely, very high or very low NSS suggests a high correspondence or anti-correspondence.  
The **Kullback-Leibler** (KL) [53] divergence between two saliency maps considered as probability density functions, is computed as:  
\[\text{KL}=\sum_{x=1}^{X}G^{\prime}(x)\cdot\log\left(\frac{G^{\prime}(x)}{P^{ \prime}(x)+\gamma}+\gamma\right) \tag{3}\]  
where \(\gamma\) is a protection constant.  
The **SIMilarity measure** (SIM) [54], also called histogram intersection, compares two different saliency maps when viewed as normalized distributions:  
\[\text{SIM}=\sum_{x=1}^{X}\min\left(P^{\prime}(x),G^{\prime}(x)\right) \tag{4}\]  
The **Earth Mover's Distance** (EMD) [55] quantifies the minimal cost to transform probability distribution \(P\) into \(G\):  
\[\text{EMD}=(\min_{f_{ij}}\sum_{i,j}f_{ij}d_{ij})+|\sum_{i}G_{i}-\sum_{j}P_{j}| \max_{i,j}d_{ij} \tag{5}\]  
with:  
\[\sum_{i,j}f_{ij}=\min(\sum_{i}G_{i}-\sum_{j}P_{j}) \tag{6}\]  
where \(d_{ij}\) represents the difference between bin \(i\) in \(G\) and bin \(j\) in \(P\).
# Application of Tooth Gear Impact-Echo System for Repeated and Rapid Data Acquisition
## 4 Experiment and Results  
### Testing Slab and the IE Results  
The developed tooth gear IE system was evaluated using a reinforced concrete slab, where artificial delamination-like defects were embedded. As shown in Figure 6, the testing slab had a depth of 250 mm and the depth of the artificial delaminations were 60 (shallow) and 200 mm (deep), respectively. The artificial delamination was made from acrylic sheet (2 mm thickness), which was embedded before casting the concrete. The IE testing was performed on the testing slab using the 11 and 30-tooth gears. A 12 mm steel ball impactor was also used to perform conventional IE testing as a reference. Two locations, including the deep and shallow delaminations, were tested, where the thickness mode and the flexural mode, respectively, were obtained. Based on the Equations (1) and (2), thickness mode frequency is about 10 kHz with conventional longitudinal wave velocity of concrete (\(V_{L}\)) as 4250 m/s while flexural mode frequency is about 5.5 kHz with the dimensionless frequency (\(k_{DL}^{2}\)) as 0.2. It is noted that the width (300 mm) to depth (60 mm) ratio of shallow delamination is 0.5. In the case of the steel ball impactor, repeated impact sources were generated at the same position while the vibration was measured using an accelerometer (PCB, 352C15). Using the tooth gear IE system, a rapid scanning test was performed while the system simply moved backwards and forwards.  
Figure 6: The details of the testing slab; (**a**) plan view (**b**) sectional view (the unit as mm).  
The results of the frequencies obtained are summarized in Table 2, and examples of the signals obtained from the shallow and deep delaminations are shown in Figures 7 and 8, respectively. In the case of the shallow delamination, the measured frequency from the impactors was identified as 5.4 kHz (Figure 7). Although the waveforms from the tooth gears were noisy, the dominant frequency was clearly shown. This is because the excited frequency range of the impactors covers the low frequency range from the flexural vibration of plate, and the flexural mode is loud enough to be measured as the dominant frequency among the impactors. Based on the results from the steel ball impactor (Figure 8), the thickness mode frequency was detected as 10.2 kHz, which corresponded well with the depth of the deep delamination with an apparent velocity of 4080 m/s. In the case of the 11-tooth gear, low frequency noise was dominant. As expected from the long contact duration, the excited frequency range was clearly limited to the generation of the thickness mode. The results from the 30-tooth gear showed that reducing the pitch creates a shorter contact duration and the thickness mode becomes dominant.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 3 Methodology
### Phase 4: Criteria Filtering  
In the fourth phase takes place during the execution of the plot detector. The optimized detection model is run to obtain 1024 \(\times\) 2048 output images with the objects of interest. A series of filters are applied to this output using three criteria to obtain the persistent objects. For an object of interest to be considered a persistent object, it must meet the following requirements:  
Persistence criterion: This criterion corresponds to the visibility of the target in three consecutive revolutions. To determine the persistence of the objects, a comparison is made based on a period of two radar revolutions; this measure is sufficient for declaring an object persistent in an oceanic context.  
Area criterion: All objects with dimensions greater than 30 \(\times\) 30 pixels are discarded. This value corresponds to the maximum Radar Cross-Section area value of the objects of interest. Confidence criterion: Only clippings of objects with a probability more significant than the threshold of 0.85 are considered, this being the value established to determine that the object is contained in the clipping.  
Figure 14 shows the processes of the criteria filtering phase.
# DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index
## 4 Experiments
### Long-Term Prediction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Portion** & **Accuracy Measure** & **Model** & \(t_{1}\) & \(t_{2}\) & \(t_{3}\) \\ \hline \hline \multirow{8}{*}{70–30\%} & & ARIMA & 701.29 & 789.60 & 989.10 \\  & & MLP & 121.19 & 199.15 & 229.12 \\  & & Deep RNN & 94.09 & 141.05 & 208.98 \\  & & LSTM & 71.79 & 139.65 & 208.80 \\  & & GRU & 70.93 & 133.71 & **204.14** \\  & & DERN & **64.12** & **123.98** & 234.25 \\ \hline \multirow{8}{*}{70–30\%} & & ARIMA & 693.11 & 732.51 & 932.43 \\  & & MLP & 89.62 & 148.18 & 189.19 \\  & & Deep RNN & 57.72 & 117.78 & 168.06 \\  & & LSTM & **55.62** & 107.58 & 166.01 \\  & & GRU & 56.63 & 108.76 & 146.91 \\  & & DERN & 58.00 & **99.96** & **126.03** \\ \hline \multirow{8}{*}{80–20\%} & & ARIMA & 98.81 & 101.08 & 131.43 \\  & & MLP & 15.31 & 17.91 & 25.93 \\  & & Deep RNN & 9.08 & 13.61 & 20.93 \\  & & LSTM & 6.45 & 12.73 & 20.46 \\  & & GRU & 6.13 & 11.99 & 20.09 \\  & & DERN & **4.05** & **9.87** & **18.46** \\ \hline \multirow{8}{*}{80–20\%} & & ARIMA & 694.93 & 872.92 & 991.80 \\  & & MLP & 152.71 & 195.15 & 299.92 \\  & & Deep RNN & 101.01 & 155.05 & 274.97 \\  & & LSTM & 91.79 & 149.11 & 278.82 \\  & & GRU & 91.10 & 134.31 & 269.22 \\  & & DERN & **89.04** & **132.93** & **258.21** \\ \hline \multirow{8}{*}{80–20\%} & & ARIMA & 901.97 & 1002.22 & 1032.43 \\  & & MLP & 68.92 & 199.98 & 208.01 \\  & & Deep RNN & 65.02 & 147.38 & 196.91 \\  & & LSTM & 63.92 & 127.38 & 186.91 \\  & & GRU & 62.97 & 128.48 & 176.91 \\  & & DERN & **60.57** & **126.35** & **167.76** \\ \hline \multirow{8}{*}{MAPE} & & ARIMA & 103.91 & 140.08 & 181.13 \\  & & MLP & 17.78 & 25.71 & 36.44 \\  & & Deep RNN & 12.28 & 17.73 & 33.04 \\  & & LSTM & 9.78 & 15.73 & 23.99 \\  & & GRU & **9.62** & 14.95 & 22.09 \\  & & DERN & 10.01 & **11.85** & **20.17** \\ \hline \multirow{8}{*}{80–20\%} & & ARIMA & 894.03 & 1012.42 & 1300.10 \\  & & MLP & 119.89 & 197.04 & 309.92 \\  & & Deep RNN & 99.91 & 179.14 & 292.82 \\  & & LSTM & 99.11 & 169.14 & 288.81 \\  & & GRU & 101.07 & 159.25 & 278.91 \\  & & DERN & **98.22** & **147.50** & **268.62** \\ \hline \multirow{8}{*}{90–10\%} & & ARIMA & 981.07 & 1302.22 & 1687.03 \\  & & MLP & 108.12 & 187.01 & 206.11 \\  & & Deep RNN & 72.32 & 160.01 & 199.61 \\  & & LSTM & 64.32 & 145.33 & 189.51 \\  & & GRU & 63.92 & **144.99** & 189.01 \\  & & DERN & **60.61** & 154.19 & **175.11** \\ \hline \multirow{8}{*}{MAPE} & & ARIMA & 114.21 & 180.18 & 221.90 \\  & & MLP & 16.95 & 24.95 & 33.91 \\  & & Deep RNN & 11.65 & 18.55 & 29.20 \\  & & LSTM & **10.64** & 16.63 & 24.01 \\  & & GRU & 11.80 & 15.03 & 24.91 \\  & & DERN & 10.71 & **12.01** & **19.82** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results of three-steps-ahead prediction of BDI. Figure 6: Long-term prediction: three-steps-ahead prediction of BDI: (**a**) Deep RNN; (**b**) LSTM; (**c**) GRU; (**d**) DERN. Figure 7: Long-term prediction: five-steps-ahead prediction of BDI: (**a**) Deep RNN; (**b**) LSTM; (**c**) GRU; (**d**) DERN.
# A Multi-Frequency Omnidirectional Antenna Based on a Ring-Shaped Structure
## 4 Comparison and Discussion  
As can be seen from Table 2, the proposed antenna structure can achieve more operating frequency points while having higher gain compared to existing multi-frequency antennas, but the operating bandwidth at each resonant frequency point is slightly narrower, so there is still room for improvement.
# Implementation of Adaptive Real-Time Camouflage System in Visible-Light Band
## 4 Conclusions  
In this paper, an adaptive optical camouflage control system design is demonstrated. The control system consists of three parts: background sensing, central processing, and physical driving waveform generation. In the central processing unit, a dominant color feature matching algorithm for image matching in the HSL space is proposed. Experimental results showed that the matching index of the proposed matching algorithm was 2.47 times of that of the conventional HSV gray histogram analysis method. In addition, a PWM-based driving circuit for CLC display with 250 V RMS amplitude was successfully implemented. Measurements show that the peak wavelength of the reflective band could blue shift from 604 to 544 nm by the proposed camouflage controlling system.  
Conceptualization and methodology, L.D.; software, P.Z.; validation, C.L. and Y.Z.; investigation, X.G.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z.; visualization, C.L. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **Platform** & **Camouflage Match** & **Response Time** & **Response Wavelength** & **Anti-Interference Ability** \\ \hline This work & Raspberry Pi & High accuracy & 1.17s & 604–544 nm & Stable \\
[4] & FPGA & N.A. & N.A. & 560–580 nm & Susceptible to reflected light \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparisons of system performance.  
Figure 12: Testing setups of the proposed camouflage control system. (**a**) Environmental images (grass images), (**b**) camera (PI NOIR CAMERA V2), (**c**) a Raspberry Pi embedded board, (**d**) USB-TTL chip (CH341T), (**e**) two M0518 SD2 AE MCUs, (**f**) signal generator, (**g**) a transformer, (**h**) a 400 \(\Omega\) high-power resistor, (**i**) the photo-coupler ELM3064, (**j**) oscilloscope.  
**Funding:** This research was funded by the National Key Research and Development Program of China (Grant No. 2017 YFA0204600), and the Fundamental Research Funds for the Central Universities of Central South University (Grant No. 2020 zzts052).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are freely available to the academic and research community from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Best Practices of Convolutional Neural Networks for Question Classification
###### Abstract  
Question Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding text representation, architectural characteristics, and learning hyperparameters) and some of their interactions, and for finding the most appropriate strategies for QC. Results show the influence of CNN settings on performance. Optimal settings were found depending on language. Tests on different data validated the optimization performed, and confirmed the transferability of the best settings. Comparisons to configurations suggested by previous works highlight the best classification accuracy by those optimized here. These findings can suggest the best choices to configure a CNN for QC.  
question classification; multilingual; convolutional neural networks; Natural Language Processing (NLP); deep learning +
Footnote †: journal: Applied Sciences
###### Abstract  
A novel dynamic inverse control method based on a dynamical neural network (DNN) is proposed for the trajectory tracking control of a flexible air-breathing hypersonic vehicle (FAHV). Firstly, considering that the accurate model of FAHV is difficult to obtain, the FAHV is regarded as a completely unknown system, and a DNN is designed to identify its nonlinear model. On the basis of [PERSON]'s second law, the weight vectors of the DNN are adaptively updated. Then, a dynamic inverse controller is designed based on the identification model, which avoids the transformation of the nonlinear model of FAHV, thereby simplifying the controller design process. The simulation results verify that the DNN can identify FAHV accurately, and velocity and altitude can track the given reference signal accurately with the proposed dynamic inverse control method. Compared with the back-stepping control method, the proposed method has better tracking accuracy, and the amplitude of the initial control law is smaller.  
flexible air-breathing hypersonic vehicle; dynamical neural network; dynamic inverse control; adaptive identification model +
Footnote †: journal: Applied Physics Letters  
1 Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361024, China; [EMAIL_ADDRESS]
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 4 Evaluation
### Evaluating Networks of Several Types  
In this examination, we ran nine sensor nodes deployed with a link quality between two nodes set randomly between 70% and 90%. We compared network schemes using minimal scheduling, orchestra scheduling, and PCLLF scheduling, with Bdist as the metric value and PCLLF with no retransmission configuration. Each sensor node generated 1000 packets and transmitted using the time slot assigned by the scheduling algorithm. We collected the data packet reception rate at the sink node.  
Figure 8 shows the data PRR of each sensor node at the sink. From Figure 8, the network performance of the proposed Bdist metric had the highest reliability compared with the other network schemes. Almost all sensor nodes achieved the target PRR by using the number of transmissions calculated with our method. Other network schemes showed lower performance since they did not consider the retransmissions.  
Figure 6: Effect of the hop count on network performance.  
Figure 7: The data PRR of sensor nodes under several estimation schemes.
## 3 Results and Discussion
### Influence of Wetlands on Water Quality
#### 3.5.6 Turbidity Assessment  
Turbidity in the wetlands of the Pantanal of the MRW met the maximum limit of 100 NTU for Class 2 in all years studied. There is also a reduction in Turbidity values at the inlet and outlet of the Pantanal, where values during the dry season are lower than during the rainy season (Figure 15).  
In a study conducted by the local government in the wetlands of the Duck Creek basin, Alaska, United States, a reduction in Turbidity from upstream to downstream was also observed, not exceeding more than 30 NTU for that location [54]. Wetlands are known to be responsible for numerous ecosystem functions, among which the retention and deposition of sediments stand out. This process improves water quality by reducing turbidity while retaining nutrients and pollutants [58].  
Turbidity in wetlands affects the distribution of aquatic plants, such as submerged macrophytes, as they depend on light penetrating the water column for photosynthesis. When turbidity is high, the presence of macrophytes is limited due to the low availability of light in the water column [59]. In the aquatic environment, turbidity increases with higher solids load, reducing the depth at which sunlight penetrates the water. The higher the turbidity, the greater this effect. The depth at which light penetrates is the photo depth of the water body. Below this depth, there is not enough light for plants to photosynthesize. The depth of the photo zone is small in highly turbid wetlands, with primary production  
Figure 14: Spatial variation of Total Solids (mg/L) at the monitoring stations of the Pantanal (IA07, IA14, IA15, IA16, IA17).  
limited to floating or emergent plants and benthic, planktonic, or submerged plants only within the shallow photic zone. Primary production is then limited to the upper layers of water, and in the lower layers, the composition of the microbiota is altered [59]. The results found for Turbidity in this study suggest that the composition of aquatic biota may be relatively different between the inlet and outlet in the wetland area of the Miranda River due to the availability of light along the water column.
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
### Next Hop Selection in GeoQoE-Vanet
When the \(p(i)\) values of all neighbours are under the threshold of the desired weight, the current vehicle sends the same packet to the two best neighbours at the same time to maximise the probability of correct message reception. Algorithm 3 illustrates the process of the next-hop vehicle selection in GeoQoE-Vanet. Figure 4: Next forwarding vehicles selection scenario. Figure 5: Last forwarding vehicle selection scenario. ```
//Actions performed when deciding to send or forward a video data packet id \(\leftarrow\)\(\textit{Pkt.getDestination}()\); //Getting the destination of the video from the received packet ifid in neighboursTablethen  sendVideoPacketTo(id); //Sending the packet to the destination return; //Ending the process else \(wMax\gets 0\); //Weight of the selected vehicle \(bMOS_{routing}\leftarrow\textit{true}\); //Checking if all vehicles' \(MOS_{routing}\) is under the \(MOS_{routing}\) threshold for all i in neighboursTable do \(getPredictedPosition(\&xp,\&yp)\); //Calculating the predicted position (xp, yp) of the vehicle \(i\) using equation (1) \(Dist\gets getDistance(xp,xy,xd,yd)\); //Calculating the distance between vehicle \(i\) and the destination using equation (2) \(LET\gets getLET()\); //Calculating the Link Expiration Time between the current vehicle and the vehicle \(i\) using the equation (3) \(MOS\gets i.getMOS_{routing}()\); //Getting the \(MOS_{routing}\) of the vehicle \(i\) from neighboursTable \(W\leftarrow\alpha*MOS_{routing}+\beta*Dist+\gamma*LET\); //Calculating the weight of the vehicle \(i\) using equation (10) if\(W>thMOS_{routing}\)then //The vehicle \(i\) has a weight greater than the \(MOS_{routing}\) threshold \(bMOS_{routing}\leftarrow false\); endif if\(W>wMax\)then //The weight of the vehicle \(i\) is greater than the weight of the previous vehicles \(FSV\gets i\); //Forwarding vehicle is \(i\) \(wMax\gets W\); endif endfor if\(bMOS_{routing}=false\)then //The vehicle can send the packet with acceptable weight \(sendVideoPacketTo(FSV)\); //Sending the packet to the selected next-hop vehicle else \(selectTwoBestVehicles(FSV1,FSV2)\); //Selecting the two vehicles having the best weights \(sendVideoPacketTo(FSV1)\); \(sendVideoPacketTo(FSV2)\); endif endif
```  
**Algorithm 3** The next-hop vehicle selection process in GeoQoE-Vanet protocol
# Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method
## 2 Split-Spectrum Processing (SSP)
### SSP Filter Bank Parameters
Overall, these parameters are dependent on each other, which means that their values have a direct effect on other parameter values. Therefore, it is necessary to search for the optimum parameters and to select them appropriately. As an example, increasing the number of filters (N) would be required to increase the total bandwidth (B), or to reduce the filter separation (F), or a combination of both. Thus, as is shown in Figure 3 the number of filters (N) could be calculated as below: \[\rm{N}=\frac{B}{F}+1\] (1)