esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 6 Discussion
### Communication Performance of IRC Waveform  
In this section, the communication performance is analyzed for noises (such as additive white Gaussian noise (AWGN) and Middleton Class B model [41]) whose time samples are independent and identically distributed.  
The communication BER performance of the IRC waveform at different ICPBOs under an AWGN channel is first discussed. Figure 10 shows some BER performance comparisons of the initial waveform and the IRC waveform generated by our proposed algorithm and the one in [31], respectively, over the same SSPA. Note that the \"Ideal PA\" results obtained over a linear PA are also plotted for comparison. To achieve BER = \(10^{-3}\), the value of \(E_{b}/N_{0}\) needs to be 7.62 dB (ICPBO = 6 dB) and 7.05 dB (ICPBO = 8 dB) for CGIA-JOPP, 7.11 dB (ICPBO = 6 dB) and 6.9 dB (ICPBO = 8 dB) for the initial waveforms, and 7 dB (ICPBO = 5 dB) for the algorithm in [31], respectively. It is found that the BER performance of our proposed algorithm is slightly worse than that of the initial waveforms. However, as can be seen from Figure 8, the proposed IRC waveform shows a great advantage in radar SNR performance. As shown in Figures 8 and 10, the BER performance of the proposed IRC waveform at ICPBO = 8 dB is almost identical to that of the initial waveform at ICPBO = 6 dB and that of the IRC waveform generated in [31], while the radar SNR loss is improved by 2.8 dB and 0.8 dB, respectively. Therefore, combined with the PSLR performance in Section 5, the proposed IRC waveform has very good radar detection and communication performance.  
In practice, channel noise may suffer from both impulsive noise and Gaussian noise, i.e., the noise is heavy-tailed. Then, the non-Gaussian noise such as the Middleton Class B model [41] that simultaneously describes the impulsive noise and Gaussian noise is discussed. The characteristic function of the Middleton Class B model is expressed as  
\[F(\theta)=\exp\biggl{\{}-\
u^{\alpha}|\theta|^{\alpha}-\frac{1}{2}\sigma^{2} \theta^{2}\biggr{\}} \tag{51}\]  
where \(\
u\) is the dispersion, \(\alpha\in(0,2]\) is the characteristic exponent, which controls the heaviness of the tails of the distribution, and \(\sigma^{2}\) is the variance of Gaussian noise.  
Figure 9: PSLR performance along the low Doppler domain.  
Here, the simulation parameters are set: the ratio of signal power to Gaussian noise power \(\sigma^{2}\) is 6.5 dB, \(\sigma^{2}/\
u=2.4\), and \(\alpha=1.3\). Figure 11 shows the signal constellation diagram with the noise modeled by Middleton Class B added, and the calculated BER is \(1.3\times 10^{-4}\). This shows that the IRC waveform generated by the proposed algorithm can also work well under non-Gaussian noise channels such as the Middleton Class B model. In addition, there is also a certain research value for noises (such as [42]) whose samples are correlated in time, which will be considered in our future work.
## 1 Introduction
In this study, the AVL-MCC zero-dimensional combustion model which is included in the AVL BOOST software, dedicated mainly to the internal combustion engine operation by simulation, was the key part of the research activity. This zero-dimensional approach offers accurate results in the computation of the combustion characteristics, performance, efficiency, and pollutant emissions of a tractor diesel engine fuelled with B20 (20% biodiesel from rapeseed oil and 80% diesel) and diesel using various rate profiles of the injection pulse. The model was calibrated for two engine operating conditions (maximum brake torque and maximum brake power) using the experimental data registered on the engine test bench. There is a lack of information in the literature about the effects of injection rate shaping when fuelling the diesel engine with biodiesel B20, a perspective fuel in the current context in which solutions to reduce CO\({}_{2}\) emissions are required. Thus, the novelty of this study consists in the presentation of original results, which fill the existing gap in the literature concerning the influence of the rate of injection shape on performance and emissions of a diesel engine fuelled with biodiesel B20. This study evaluates which pollutant emissions, like nitrogen oxides (NO\({}_{\rm x}\)) and Soot, can be reduced without significant power losses or considerable growths in brake-specific fuel consumption when the shape of the injection rate is changed. Highlighting the linear correlation existing between the amount of fuel injected during the initial combustion phase and the values of NO\({}_{\rm x}\) and Soot is another important element of originality. The study intentions were to determine an optimal ROI shape when the classical diesel fuel is replaced by B20 using the analysis of combustion characteristics. The results are presented comparatively for both fuels considering two meaningful operating conditions of an agricultural tractor diesel engine: the maximum brake torque (R1) and the maximum brake power (R2). The engine operation with the optimum injection rate shape (Boot 2) showed that it is possible to obtain a simultaneous reduction of NO\({}_{\rm x}\) and Soot emissions by 17%, respectively 9% for diesel fuel on R1 test condition and by 28%, respectively 12% on R2 test condition. The replacement of diesel fuel by biodiesel B20 generates similar reductions of NO\({}_{\rm x}\) and Soot by 11%, respectively 4% on R1 test condition and by 22%, respectively 7% on R2 test condition.
# MPEG and DA-AD Resilient DCT-Based Video Watermarking Using Adaptive Frame Selection
## 3 Proposed Method
### Data Bit Encoding Step  
For the proposed watermarking scheme, two pseudo-random sequences \(X\) and \(Y\) were adopted for the watermark pattern. Here, 256(=2\({}^{8}\))unique codes were generated to represent 8 bits of information. Sequence \(X\) for encoding the front 8 bits of information, and sequence \(Y\) for encoding the back 8 bits, were used. From this, 16 bits of data were encoded using two sets, \(\mathbb{X}\) and \(\mathbb{Y}\), of 256 zero-mean/unit-variance pseudo-random sequences as a watermark pattern.  
\[\begin{array}{l}\mathbb{X}=\{X_{0},X_{1},X_{2},\,...,\,X_{255}\},\\ \mathbb{Y}=\{\,Y_{0},\,Y_{1},\,\,Y_{2},\,...,\,Y_{255}\}.\end{array} \tag{1}\]  
Figure 3: Sample of video sequence for watermarking. Embedding intervals are selected from every 7.5 s.  
Figure 2: H.264 compression effect on correlation values as an extractor response (test sequence #2 from ITE/ARIB video).  
Therefore, two pseudo-random sequences \(X_{i}\) and \(Y_{j}\) were selected in each sequence set for data encoding. Indexes \(i\) and \(j\) correspond to the input data bit information. For example, if the input bit code is 0 xFF00, two sequences, \(X_{i}\) and \(Y_{j}\), are selected, where \(i=0\)xFF and \(j=0\)x00.
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 5 Experimental Results, Analysis, and Discussion
### Residual Energy  
The energy utilization of nodes is categorized primarily into three sections: Data transfer, data receipt, and cluster negotiation communication. The more extended nodes that are alive, the more uniform energy usage there will be. The result is that the energy usage by the proposed protocol is highly consistent compared to LEACH and LEACH-C, as shown in Figure 9. The approach suggested in the paper decreases energy consumption across the clusters and energy consumption within the cluster.  
Figure 8: Lifetime vs. rounds.
## 3 Coverage Prediction Tool
### Throughput Estimation  
From the simulated PL, we calculate the received power at every grid point via the inverse of (1), i.e., we subtract PL from the sum of TX power and antenna gains, assuming there are no losses. When a directional antenna is used, the calculated received power for every ray is corrected based on the antenna gain and AoD of the ray. We then select the MCS for which the receiver sensitivity is closest to, but lower than, the calculated received power. Table 1 presents the receiver sensitivities and corresponding data rates for the different MCS indices. These data rates, based on received power, are theoretic values and differ from throughputs based on the MCS selection by an IEEE 802.11 ad transceiver.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 3 Methodology
### Vitis AI Deploys CNN
#### 3.4.1 YOLOv4-MobileNetv3 Model Quantization  
It is important to note that quantification methods also need to be used in conjunction with specific hardware platforms. For example, NVIDIA uses TensorRT to quantify network models and deploy them on Nano, TX1, and TX2 devices. Since our FPGA evaluation boards are part of the Zynq UltraScale+ MPSoC series, we can choose Vitis AI's quantizer to quantize the model. The Vitis AI quantizer can reduce the computational complexity of the network model with little loss of inference accuracy. The deep learning frameworks currently supported by the Vitis AI quantizer are PyTorch and TensorFlow, and the quantizer names are vai_q_pytorch and vai_q_tensorflow, respectively. We use Int8 fixed-point quantization to optimize the algorithm model, and convert the 32-bit floating-point number into an 8-bit fixed-point number format. The quantization algorithm is as Algorithm 1. In the Vitis AI quantizer, the Pytorch quantizer supports two different methods: post-training quantization (PTQ) and quantize aware training (QAT), to quantize deep learning models. This paper selects the PTQ scheme to quantize the YOLOv4-MobileNetv3 model. PTQ is a technology that converts the pretrained floating-point model into a quantized model, and its characteristic model accuracy loss is extremely low. Model quantification generally needs to determine the number of quantized pictures according to the scene. The more detection categories and the more complex the scene, the more pictures required for quantization. Combined with the characteristics of many detection scenes in the DIOR dataset, this experiment uses 4000 images as a representative calibration dataset for calibration to run several batches of inference on the floating-point model to obtain the distribution of activations.  
```
Input: input 32-bit floating-point weight Output: Deployment file
1 Floating point model;
2 pretreatment;
3 Quantify the weight and calibrate the activation;
4 whileCalibration data set (No label)do
5 ifand else;
6 ifQualified don't accuracythen
7 Quantitative fine-tuning;
8 End of quantization.
9 else
10 End of quantization.
11
12 end if
13
14 end for
15
16 Generate the DPU model ;
17 Compile;
18 Deployment.
```  
**Algorithm 1**Model Quantization
## 5 Simulation and Experimental Results
### Experimental Model
#### 5.3.2 Simulation Results for SLFM
It is observed that the proposed scheme improves the control performance by reducing efforts of dc motor to 12.47%, 0.78%, and 65.80% as compared with GESO based method [13], EGESO based method [14], and Linear extended state observer-based sliding mode control (LESOSMC) [30] method respectively. Figure 9: Flexible link experimental setup.
# Compact 3 \(\times\) 1 Matrix Converter Module Based on the SiC Devices with Easy Expandability
## 2 Structure of the Module
Finally, the board involves a high-speed optocoupler, which ensures galvanic isolation and transfers the direction of the output current flowing through the module. The designed driver board is shown in Figure 7.  
Figure 3:0 Configuration of the turn-on and turn-off resistors. Figure 3:1 3D model of the driver board. The power and driver board are stackable with one another. The signal connections are made through pin headers. The connection of power high-voltage lines is made through steel-tinnned standoffs. On the power board, the standoffs are soldered directly to the board, and in the driver board, the mounting screw is used. The assembled 3D model of the power and driver board is shown in Figure 3:2  
Due to the space limitations on the driver board, the input signal board shown in Figure 2 must be designed on the separated PCB. This board consists of a buck power supply with an operating range from 9 V to 15 V and output power of 10 W. For the interface, three different connectors are used. For the pulse width modulation (PWM) signals, the RJ50 shielded connector is used. This connector transfers six PWM signals, and the remaining wires are used as supply voltage lines. The level shifter shifts input PWM signals to the 5 V level appropriate for the gate drivers. For the current direction and the actual measured current value, the RJ11 connector is used. The value of the output module current is transferred through the differential twisted pair to improve immunity to electromagnetic interference. Finally, the optical TOS-LINK connector is added to the board. Through this interface, the current direction is transmitted too so that the board can transmit the current direction signal in two ways. The optical interface is fast, reliable, and immune to electromagnetic interference, so it is preferred in a disturbed environment. The signal board is mounted perpendicularly to the driver board, so all connectors face upwards. The 3D model of the assembled \(3\times 1\) matrix converter module is shown in Figure 3:3  
Figure 3:4 Cross-sectional look at the power and driver boards connection. Figure 3:5 Assembled model of \(3\times 1\) matrix module. For further protection against the accidental touch of the high voltage connectors, the cover was designed. The finalized module with the designed cover can be seen in Figure 3:6  
The final dimensions of one \(3\times 1\) matrix module are \(130\times 54\times 43\,\mathrm{mm}\) (length \(\times\) width \(\times\) height). All PCBs were manufactured, and the final module was assembled according to designed 3D models.
# Fully Symmetric Convolutional Network for Effective Image Denoising
## 4 Experimental Results
### Comparative Analysis of Algorithm
#### 4.2.1 Quantitative Evaluation  
The average PSNR results of different methods on AWG noise with \(\sigma\) = 0.09 are shown in Figure 8. It can be seen that the proposed FSCN model yielded the highest PSNR on most of the images. The proposed model outperformed the competing methods by 0.4 dB to 0.8 dB on most of the images and failed to achieve best results only on two images, \"Barbara\" and \"fingerprint\", which were dominated by repetitive structures and contained many regular structures. This result is consistent with  
Figure 6: PSNR values on the validation set during training with different depths of layers.  
Figure 7: Average PSNR values on 10 images for different depths of layers.  
the findings in [28; 39]: nonlocal, means-based methods are usually better on images with repetitive and regular structures, whereas discriminative training-based methods generally produce better results on images with irregular textures. This is reasonable because images with regular and repetitive structures meet well with nonlocal similarity prior; conversely, images with irregular structures would weaken the advantages of such specific prior, thus leading to poor results.  
The proposed model was also evaluated for varied noise levels (0.05-0.19) and produced best results on 8 out of the 10 test images. Figure 9 presents the average PSNR results of different denoising methods with respect to various noise levels. It is worth noticing that the proposed architecture gave a higher average PSNR value compared to WNNM, which is the best method known so far. From the above results and discussion, it is obvious that the combination of 10-layer convolutional and deconvolutional network achieves better results than previously known state-of-the-art methods in terms of denoising. The main reason for this superior performance of the FSCN model could be attributed to the sequence of convolution and deconvolution layers built in our architecture. Even the minute loss of details of the image contents were recovered due to the unique combination of layers.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 3 Preliminary Knowledge and Analysis  
### Aodv  
AODV (Ad hoc On Demand Distance Vector) was published as the experimental RFC of Ad hoc network routing protocol by IETF's MANET working group in 2003. AODV is an on-demand algorithm. It draws on the broadcast routing discovery algorithm and routing maintenance scheme with DSR (Dynamic Source Routing). However, it's not exactly a source routing protocol. It's a large cost protocol because each data packet takes the source routing, so the AODV dynamically establishes a routing table entry by the intermediate nodes. It also employs DSDV (Destination Sequenced Distance Vector)'s hop-by-hop routing scheme and sequential numbering, and periodically keeps the routing information up-to-date in the route maintenance period. AODV efficiently avoids generating a loop by effectively using the sequence number of the destination node. Form the on-demand property, we can realise that: (1) nodes that are not on the route towards the destination node are neither involved in maintaining any routing information nor responsible for any periodical routing information exchange; (2) nodes do not need to discover and maintain routes to other nodes unless a communication needs to take place; (3) if the mobile nodes need a local connection, they obtain the routing information from the route discovery process. Generally, AODV protocol includes the following five aspects:
## 3 The Proposed Channel Estimation Methods  
In this section, three denoise methods are proposed to enhance the accuracy of adaptive channel estimation by supposing the channel in the DD domain is nearly constant.
# Unique Triboelectric Nanogenerator Using Carbon Nanotube Composite Papers
## 3 Results
### Investigation of Performance Changes Due to Changes in CNT Composite Paper-Making Conditions  
#### 3.2.1 Effect of Changing Amount of CNTs  
In this subsection, it is considered how the triboelectricity generation performance changes with the changing amount of CNTs in the samples prepared via the methods described in Section 2.3.1. Table 3 shows the typical properties of the fabricated CNT composite papers. In the table, a variation in the thickness of the resulting samples can be seen. The reason for this is the same as that described in Section 3.1.  
Figure 7 shows the results in terms of power generation for our TENGs with changes in the CNT amount. Figure 7a-c show the output from samples containing CNTs, confirming the presence of a waveform that is characteristic of triboelectricity, similar to the results in Figure 5. Figure 7d is the output from a pulp-only sample without CNTs, which did not show the characteristics of triboelectricity, in comparison with Figure 7a-c. In addition, this waveform showed many signals that appeared to be noise. This may have been due to the fact that this sample did not contain CNTs, so the generated charge could not be collected properly, resulting in more noise. In addition, in the measurements, some results showed asymmetric peaks instead of the clearly alternating output peaks that appear in general TENGs. According to other reports [34,35], depending on the surface shape of the device, there may not be clearly alternating output peaks. The surface of the CNT composite paper used in this study was not smooth and had non-uniform flatness. Therefore, no clearly alternating output peaks could be seen in the output waveform in this study. Figure 8 shows the RMS values calculated based on the oscilloscope measurements. While there was no significant difference in the output between the samples containing 5 mg and 30 mg of CNTs, the output of the 15 mg sample was approximately 3.7 times higher than that of the aforementioned samples. This suggests that there is an optimal amount of CNTs required to increase the output and that the inclusion of more CNTs will decrease the output. It is also thought that CNTs do not increase the output; rather, they efficiently transfer the electric charge generated during triboelectric power generation to the electrodes, as initially assumed.
# Tensile Properties and Tensile Failure Criteria of Layered Rocks
## 3 Numerical Study of Layered Rocks
### Direct Tension Test on Layered Rock Samples by RFPA\({}^{2D}\)
#### 3.3.2 Results and Analysis
Figure 13: Fracture patterns of specimens with a layer dip angle from 0\({}^{\circ}\) to 80\({}^{\circ}\) and 90\({}^{\circ}\). the joints. The cracks perpendicular to the loading direction only occur near the top or the bottom end of the specimens. For specimens with layers parallel to the tensile direction with angles of 85\({}^{\circ}\) and 90\({}^{\circ}\), the fracture plane also exhibits a clear saw-toothed shape, but only a few parts of the failure plane occur along the foliation. The phenomenon of the saw-toothed failure plane reflects the fact that progressive failure occurs during the direct tensile tests. The progressive failure may include primary tensile cracks perpendicular to the tensile loading direction and secondary cracks parallel to the joints. Figure 14 shows the progressive failure process of the specimen with a dip angle of 85\({}^{\circ}\). It is interesting to see that the cracks do not initiate perpendicular to the loading direction in the joint material but in the rock material. They often run across many layers before beginning to propagate along the joints. The saw-toothed fracture pattern is caused by alternate propagation perpendicular to the loading direction and along the joints. Figure 15 shows the failure patterns of sandstone of Lyons, tested in direct tension with different inclined angles by [PERSON] in 1969 [39]. In the direct uniaxial tension tests, we can see that, when the inclined angle is less than 45\({}^{\circ}\), the sandstone of Lyons mainly undergoes tensile failure along the bedding plane. The sandstone of Lyons also shows the saw-toothed fracture pattern when the inclined angle of bedding plane is greater than 45\({}^{\circ}\) and less than 90\({}^{\circ}\). When the inclined angle of the bedding plane equals 90\({}^{\circ}\), the sandstone of Lyons exhibits failure of the rock material, and the failure plane is perpendicular to the loading direction. The failure modes of the layered rock specimens obtained by numerical simulations under direct tension are similar to the experimental results [39; 40], and the uniaxial tensile failure modes of the layered rocks can be divided into three types: (1) tensile failure along the bedding plane; (2) progressive saw-toothed failure; (3) tensile failure of rock material. The diagram of failure modes is shown in Figure 12[40; 44].
# Band-Pass Sampling in High-Order BOC Signal Acquisition
## 3 BOC Acquisition Based on Band-Pass Sampling  
### Problems in BOC Acquisition  
As a result of the subcarrier, the ACF of BOC signals has multiple peaks. The squared magnitude ACF is directly relevant to the class of non-coherent energy detection acquisition receivers considered here. Figure 1a illustrates the normalized squared magnitude ACFs of BOC(15,2.5) and BPSK(2.5). As shown in this figure, the squared magnitude ACFs of BOC(15,2.5) has as many as 23 peaks, while BPSK(2.5) has a unique peak.  
This indicates that the step of the searching time bin should be sufficiently small to be able to detect the main peak of the ACF (i.e., we need a higher number of timing hypotheses in order to search a given time-uncertainty window compared to BPSK case) [6]. Another problem is that the bandwidth of BOC(\(m\),\(n\)) signals is much wider than BPSK(\(n\)) signals, so that the Nyquist frequency is very high, which results in a higher computational complexity.