esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Traditional Layout Design for Robotic Mobile Fulfillment System with Multiple Workstations
## 5 Numerical Experiments
### Results in a Traditional Warehouse Layout
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(N\)} & \multirow{2}{*}{\(L\)} & \multicolumn{6}{c}{\(K=4\)} & \multicolumn{6}{c}{\(K=6\)} \\ \cline{3-11}  & & \(TD_{op}^{a}\) & \(TD_{2n}^{b}\) & **gap(\%)** & \(TD_{n+1}^{c}\) & **gap(\%)** & \(TD_{op}\) & \(TD_{2n}\) & **gap(\%)** & \(TD_{n+1}\) & **gap(\%)** \\ \hline \multirow{6}{*}{32} & 30 & 16,192 & 16,320 & 0.79 & 16,800 & 3.75 & 13,734 & 13,920 & 1.35 & 14,400 & 4.85 \\ \cline{2-11}  & 40 & 24,912 & 24,960 & 0.19 & 25,600 & 2.76 & 21,564 & 21,760 & 0.91 & 22,400 & 3.88 \\ \cline{2-11}  & 50 & 35,200 & 35,200 & 0.00 & 36,000 & 2.27 & 30,994 & 31,200 & 0.66 & 32,000 & 3.25 \\ \cline{2-11}  & 60 & 47,040 & 47,040 & 0.00 & 48,000 & 2.04 & 42,024 & 42,240 & 0.51 & 43,200 & 2.80 \\ \cline{2-11}  & 70 & 60,480 & 60,480 & 0.00 & 61,600 & 1.85 & 54,654 & 54,880 & 0.41 & 56,000 & 2.46 \\ \cline{2-11}  & 80 & 75,520 & 75,520 & 0.00 & 76,800 & 1.69 & 68,884 & 69,120 & 0.34 & 70,400 & 2.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of two rules with an even number of workstations. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(N\)} & \multirow{2}{*}{\(L\)} & \multicolumn{6}{c}{\(K=3\)} & \multicolumn{6}{c}{\(K=5\)} \\ \cline{3-11}  & & \(TD_{op}^{a}\) & \(TD_{2n}^{b}\) & **gap(\%)** & \(TD_{n+1}^{c}\) & **gap(\%)** & \(TD_{op}\) & \(TD_{2n}\) & **gap(\%)** & \(TD_{n+1}\) & **gap(\%)** \\ \hline \multirow{6}{*}{32} & 30 & 18,560 & 18,816 & 1.38 & 19,552 & 5.34 & 14,720 & 14,720 & 0.00 & 14,986 & 1.81 \\ \cline{2-11}  & 40 & 28,560 & 28,736 & 0.62 & 29,552 & 3.47 & 22,960 & 22,960 & 0.00 & 23,296 & 1.46 \\ \cline{2-11}  & 50 & 40,160 & 40,256 & 0.24 & 41,152 & 2.47 & 32,800 & 32,800 & 0.00 & 33,206 & 1.24 \\ \cline{2-11}  & 60 & 53,346 & 53,376 & 0.06 & 54,352 & 1.89 & 44,240 & 44,240 & 0.00 & 44,716 & 1.08 \\ \cline{2-11}  & 70 & 68,096 & 68,096 & 0.00 & 69,152 & 1.55 & 57,280 & 57,280 & 0.00 & 57,826 & 0.95 \\ \cline{2-11}  & 80 & 84,416 & 84,416 & 0.00 & 85,552 & 1.35 & 71,920 & 71,920 & 0.00 & 72,536 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of two rules for traditional layout with an odd number of workstations. Figure 9: Workstation layout by optimization model and two simple rules (\(N=32,L=40\)).
## 2 System Description
### Antenna Array  
In general, 2-D beamforming applications, the antenna array is usually arranged as a plane array or a circular array, which can facilitate good pointing performance. The array of NOSTRADAMUS can also be seen as a type of plane array. However, the plane array needs much more antennas to play its advantages, while the circular array needs a large area of site but with low utilization rate. Although the  
Figure 3: **(a)** The overall structure of the receiver; (b) the boards of the analog leading-ends; (c) the photo of the ADC board; (d) the photo of the DDC board.  
Figure 2: **(a)** The transmitting channel; (b) the power amplifier; (c) the transmitting antenna.  
pointing ability of L-array is not as good as the first two, it is very suitable for the miniaturized system proposed in this paper. In addition to the ability to realize 2-D directional sounding, the hardware cost and site requirements are much lower.  
In our system, six 6-m whip antennas form the receiving array in L-shaped. In addition to beamforming, it can also be employed angle of arrival (AOA) estimation in some other applications. The layout and actual scene are shown in Figure 4. Unlike the usual L-array, there are three antennas in each baseline along the east-west and the south-north but with no antenna in the intersection. In this way, the antenna array has the same performance in two orthogonal directions. The antennas and receiving channels are also utilized to the maximum extent.  
To facilitate the analysis, we define the true north as the \(x\)-axis, which is also the reference direction. The azimuth \(\varphi\) is defined with the clockwise direction (rotating to the East, \(y\)-axis) as positive. With the ground as a reference, the elevation is defined as \(\theta\). Thus, for the receiving array, when the intersection point O is defined as the reference point, the array manifold \(a(\theta,\varphi)\) can be expressed as Equation (1).  
\[\left\{\begin{array}{l}a(\theta,\varphi)=\left[\begin{array}{l}a_{x}( \theta,\varphi)\\ a_{y}(\varphi,\varphi)\end{array}\right]\\ a_{x}(\theta,\varphi)=\left[e^{-2j\pi\bullet d_{x}\sin\varphi\cos\theta/\lambda },e^{-2j\pi\bullet 2d_{x}\sin\varphi\cos\theta/\lambda},e^{-2j\pi\bullet 3d_{x} \sin\varphi\cos\theta/\lambda}\right]^{T}\\ a_{y}(\theta,\varphi)=\left[e^{-2j\pi\bullet d_{y}\cos\varphi\cos\theta/ \lambda},e^{-2j\pi\bullet 2d_{y}\cos\varphi\cos\theta/\lambda},e^{-2j\pi \bullet 3d_{y}\cos\varphi\cos\theta/\lambda}\right]^{T}\end{array}\right. \tag{1}\]  
where \(a_{x}\) and \(a_{y}\) are the flow pattern of the subarray on the \(x\)-axis and \(y\)-axis. \(dx\) and \(dy\) are the spacing between two adjacent antennas of the two axes. \(\lambda\) is the wavelength of the currently operating frequency. In order to ensure that there are no grating lobes generated in the frequency band of 5-15 MHz in the experiment described below, we choose \(dx=dy=10\)m.
## 2 System Overview  
### Hysteresis Voltage Control  
To simplify the output voltage control circuit, a hysteresis voltage control method is utilized. The corresponding circuit is shown in Figure 1, where \(S\) is a decoupling switch, power transmission is blocked (from primary side to secondary side) when \(S\) is turned off. A push-pull inverter is used to generate the high frequency AC source. \(L_{p}\), \(C_{p}\) and \(L_{s}\), \(C_{s}\) constitute the primary parallel and secondary series resonant circuits, respectively. \(D_{1}\) -\(D_{4}\) constitute the rectifier. \(R_{eq}\) is the equivalent input resistance of the rectifier circuit and equals to \(8R_{L}/\pi^{2}\). \(C_{L}\) is the output filter capacitor and \(R_{L}\) is the load resistance. \(E\) and \(U_{L}\) are the input and load DC voltages, respectively.  
Figure 1: Power transfer circuit with hysteresis voltage control.  
The hysteresis voltage control can be illustrated with the aid of Figure 2. Assuming the required constant output voltage is \(U_{L\text{-}req}\) with a hysteresis tolerance band of \(\pm u_{t}\). \(S\) will be switched to the on state when \(U_{L}\) is smaller than \(U_{L\text{-}req}-u_{t}\) and switched to the off state when \(U_{L}\) is larger than \(U_{L\text{-}req}+u_{t}\).
# Position Tracking Control of 4-DOF Underwater Robot Leg Using Deep Learning
## 1 Introduction
Furthermore, it allows for the maintenance of higher position accuracy compared to a single  
Figure 1: Radial-type quadruped ULR trying to remove fishing net tangled in a propeller. task-space PD controller with lower torque output. Performance evaluation is carried out using an underwater 4-DOF robot leg modeled after the leg of the radial-type quadruped ULR. The evaluations are conducted in both air and water environments to confirm applicability in various environments. The contributions of this study are as follows. * Through a deep learning model based on actual robot data, we considered non-linear factors, such as joint friction and fluid force, etc., which are challenging to calculate. * We proposed a deep learning-based robot leg control method that is applicable across various environments. * We employed a controller that combines feedback control with data-driven deep learning to increase its utility. * We employed a simpler model to ensure high positional accuracy and compliance, even with similar torque output.
## 3 Results
### Effects of Injection at the Valve and Reactivity Fuels on the Emission Attributes
#### 3.3.3 UHC Emission  
As depicted in Figure 17a, the UHC emission decreased significantly as the engine load increased during the combustion of B5 and biogas injected at the valve. The cause of the decrease in the UHC emission might likely be an influence of either rise in the in-cylinder temperature, which might increase the rate of the fuel burned or fewer amounts of the fuel entering the crevices [8; 12; 46]. When the biogas was premixed, the combustion emitted higher UHC at all the engine capacities except 4.5 bar IMEP, which could also be an influence of high in-cylinder temperature or the amount of fuel entering the crevices. To explain the cause of the rise in UHC emission under the premixed mode, Figure 6c indicated that the combustion of B5 and premixed biogas generated less in-cylinder temperature, compared to the outcome of the biogas injection at the valve. The attribute of having less in-cylinder temperature accompanied by high UHC emission indicated the effect of the amount of fuel entering the crevices due to air-fuel mixing. Therefore, an estimate of the emission reduction shows that the combustion of B5 and biogas injection at the valve was found more promising in terms of reduced UHC emission, by about 5%, due to a rise in the cylinder temperature while the reduction in the amount of fuel entering the crevices lowered the emission by about 8%. However, the trend of the UHC emission was found identical to that of the conventional diesel RCCI combustion for both premixed and biogas injection at the valve. In contrast, Figure 17b revealed that the use of traditional diesel and biogas injected at the valve superseded in reducing the UHC across the engine loads, unlike the usual premixed RCCI combustion that showed the highest UHC emission at merely all the load conditions. The advantage could be due to the less homogeneity of the mixture in the cylinder, as a consequence of in-cylinder mixing, which enabled the air to occupy the crevice regions, hence resulting in less UHC emission [8].  
Figure 17: Engine-out UHC emission for (**a**) B5/biogas RCCI for premixed and port injection at the valve, (**b**) diesel/biogas in a premixed and port injection at the valve, and (**c**) B5 and premixed biogas RCCI combustion at various engine speeds.  
Figure 17c shows that the UHC emission decreased gradually with the increase in the engine speed for the various engine capacity likely because of the rise in the cylinder temperature resulting in reduced UHC emission as the load increased while given more of the discharge at low engine speed due to more mixture entering the crevices. Figure 17c also indicated that the difference in the UHC emission was more significant as the engine speed increased to 1800 rpm compared to the difference when proceeded to 2000 rpm. This trend could be because the change in the thermodynamic state of the in-cylinder charge tended to decrease as the engine speed increased and would likely remain constant with the further increase in the speed.
# FEA-Based Ultrasonic Focusing Method in Anisotropic Media for Phased Array Systems
## 2 TFM in Anisotropic Material
### TFM Imaging in Anisotropic Material  
The focus of the ultrasonic waves is realized by means of time delays, meaning that the transmitted pulses arrive in phase at the target region, which produces high-intensity focal points. In isotropic materials, the time delays can be determined using the longitudinal wave velocity and the relative position between elements \(i\) and \(j\) within the aperture and the target point; however, in anisotropic materials, this information is insufficient to determine the time delays because the propagation path of the ultrasonic wave is distorted, and does not maintain straightness. Precisely calculating the ultrasonic wave propagation path in austenitic welds is difficult because the complex grain structure and anisotropic material property cause skewing of the ultrasonic waves. For wave propagation simulation, information on the distribution of grain orientation and the anisotropic elastic constants is essential.  
In this section, an FEA-based ultrasonic beam focusing methodology for austenitic welds to determine time delays for the TFM is provided. Figure 2 shows a schematic procedure of TFM imaging in the welds. First, the grain orientation distribution and the anisotropic elastic constants, which are the major parameters for determining the ultrasonic wave propagation behavior, should be obtained (Step 1). Then, FEA is performed based on the information on the grain orientation distribution and the elastic constants of the welds (Step 2). From the FEA result, the ultrasonic wave propagation time database is extracted (Step 3). The DB contains the wave propagation time from every element within the aperture and every scanning target point in the grid for TFM imaging. Using the database, the time delays can be determined, and a TFM image can be formed in anisotropic material.  
To implement the proposed FEA-based ultrasonic beam focusing methodology for typical nondestructive testing in sites, the information or database of the grain orientation distribution and the anisotropic elastic constants should be given in advance. There are two practical ways to obtain this information. The first is to predict the material properties of a nondestructive method in advance, which the authors proposed in previous studies [5].  
Figure 2: Schematic procedure of TFM imaging in austenitic welds.  
The second is to database the material properties in a destructive way for specimens made according to welding procedure specifications in advance. However, additional research should be conducted to minimize the difference of the material properties between a nondestructive test target and information obtained in advance, and to quantify the effect of material property uncertainties on the wave propagation behavior.
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 3 Experimental Results and Discussions
### Gain and Radiation Efficiency  
Figure 9a shows the simulated and measured gain and efficiency of the proposed design operating at a variety of frequency bands, including 28 GHz, 38 GHz, and 60 GHz. The measured gain is 1.8 dB and 1.6 dB at 28 GHz and 38 GHz, respectively. The simulated gain of the antenna in free space is 2.1 dB, 1.9 dB, and 5.1 dB at 28, 38, and 60 GHz, correspondingly. Most of the input power is lost as radiation by an antenna with a high radiation efficiency. Most of the energy is lost as internal losses in a low-efficiency antenna or is reflected away owing to an impedance mismatch.  
For antenna efficiency, also called radiation efficiency, the efficiency of an antenna is defined as the ratio of the input power to the output power (8):  
\[\varepsilon_{R}=\
icefrac{{P_{rad}}}{{P_{in}}} \tag{8}\]  
The simulated and measurement results show that the antenna's efficiency across all frequency bands is lower than 90%, as shown in Figure 9a. Figure 9b indicates the front-to-back ratio (FBR) of the proposed antenna in free space. The value of the FBR is found to be more than 15 dB at 28 GHz and 38 GHz, while more than 28 dB for 60 GHz.  
Figure 8: The 2D pattern of the proposed design; (**a**) at 28 GHz, (**b**) at 38 GHz, and (**c**) at 60 GHz.  
Figure 9: (**a**) Efficiency and gain curves of radiation in relation to different operational frequency bands in free space, (**b**) front-to-back ratio (FBR).
# Management of Plastic Wastes through Recent Advanced Pyrolysis Processes
## 4 Challenges Associated with the Pyrolysis of Plastics  
Although pyrolysis of plastic waste is a promising waste-management method, there are still various challenges associated with this process that need to be addressed. One of the major challenges that is slowing the scale-up of plastic waste pyrolysis is the complexity of plastic waste feedstock. Plastic waste usually contains dyes, pigments, papers, coatings, glasses, metal foils, etc., which can affect the purity and quality of fuel produced. Moreover, the percentage yields of liquid fuel from the pyrolysis process are relatively low. Another problem with plastic pyrolysis is the high fraction of olefins in liquid oil. The excess amounts of olefins limit the economic feasibility of pyrolysis oil, especially when it is intended to be used as a transportation fuel because high amounts of olefins can result in the formation of unwanted gums in the engine of the vehicles. Another important challenge that faces plastic pyrolysis is the complexity of its legislative framework involving policies on waste management, economy, product safety, and fuels. Currently, EU legislation does not recognize pyrolysis as a recycling technology if the end product is used to generate energy. This status needs to be changed for pyrolysis to become a feasible solution for plastic waste recycling. Establishing end-of-waste (EoW) criteria for pyrolysis oil would help classify it as a product, promoting it for commercial use and regulatory alignment with REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) registration [57; 58]. To address these challenges, recent research is focused on developing efficient plastic-waste pretreatment and impurity-removal technologies to produce valuable plastic feedstock. Also, research efforts are targeted toward developing more promising catalysts that can improve the efficiency and quality of the fuel produced from plastic waste.
# Image Deraining Algorithm Based on Multi-Scale Features
## 3 Multi-Scale Features to Remove Rain
### Multi-Scale Feature Processing  
In this section, the improved U-Net network is used for multi-scale feature extraction. The structure of the U-Net model is completely different from CNN and FCN because it downsamples on its left half and upsamples reciprocally on its right half. It mainly consists of the encoder; the decoder is connected with the U-Net-style jumps. The encoder presents a structure that shrinks gradually. It reduces the resolution of the feature map constantly and then uses two convolutional layers to extract features. The decoder presents an expanding structure symmetrical to the encoder. To achieve accurate localization, it repairs the details and spatial dimensions of the segmented objects. The U-Net-style jump connection is a splicing fusion in the channel dimensions, and the jump connection is designed to make the feature map recovered from the upsampling contain more low-level semantic information. Finally, the result is more refined.  
Figure 1: Model structure: The image features are first learned through a layer of convolution, then the features are enhanced through a spatial attention mechanism, then through an encoder–decoder network, and finally, the extracted spatial features are enhanced through a spatial attention module so that the desired fine features are preserved in the final output image.  
The decoder-encoder network is shown in Figure 2. It is based on the standard U-Net with the following parts. Firstly, the channel attention module is used to extract features at each scale. Secondly, the feature maps at the U-Net jump junctions are co-processed with the CAB (Channel Attention Block). Finally, bilinear interpolation upsampling and convolutional layers are used to improve the feature space resolution of the decoder instead of using transposed convolution. This approach reduces possible image artifacts caused by transposed convolution and improves the quality of the output images [34].  
The spatial attention module can preserve the fine details from the input image to the output image, as shown in Figure 3. Firstly, the input feature map is subjected to maximum pooling and average pooling. Then, the results of maximum pooling and average pooling are spliced according to the channels. Finally, the spliced results are subjected to the convolution operation and then processed by the activation function to obtain the feature map outputted by the spatial attention mechanism; this suppresses the less informative features and enhances the extracted spatial features.
# Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
## 4 Simulations and Results  
In simulations, we evaluate our proposed schemes by comparing them with an existing scheme Hydrocast [4]. The Hydrocast scheme is efficient in dense network however in sparse conditions lengthy path establishment increases the path loss probability resulting in high packet drop ratio. Therefore, the number of re-transmissions increases in the network which leads to more energy consumption and high propagation delay. Simulation results show that our proposed schemes outperform the compared existing scheme.  
This section consists of two subsections. In Section 4.1, simulation results are discussed for fast (7.71 m/s) moving AUVs in the network. Section 4.2 includes performance discussion of the proposed schemes when slow moving (0.3 m/s) sensor nodes are deployed at strategic locations in the network.
## 4 Numerical Simulation
### Effect of Mass Ratio on the Output Power  
Define \(M_{1}=M/\psi_{r}^{2}(L)\), \(K_{1}=K/\psi_{r}^{2}(L)\)[17], \(\mu=M_{2}/M_{1}\), and \(\alpha=K_{2}/K_{1}\), where \(M_{1}\) and \(K_{1}\) are the lumped mass and stiffness of the piezoelectric oscillator, respectively. \(\mu\) and \(\alpha\) are mass and stiffness ratios, respectively. According to the properties in Table 1, \(M_{1}\) = 0.0257 kg, \(K_{1}\) = 651.8 N/m, \(M_{2}\) = 0.0118 kg, \(K_{2}\) = 220.0 N/m, \(\mu\) = 0.46, and \(\alpha\) = 0.34. The output power of the HEH _versus_ excitation frequency for different mass ratio \(\mu\) is illustrated in Figure 5. For a given \(\alpha\), the first resonance shifts to a higher frequency with the decrease of \(\mu\). It is difficult to distinguish the tendency of the second resonant frequency, due to the mixture of two peak power. Because the HEH is used in the low-frequency environment, the dynamic characteristic of the first peak power is our research focus. The magnitude of first peak initially decreases and then increases as \(\mu\) decreases. Finally, it reduces to a smaller value when the first resonance is higher enough than the second one. In a word, the mass ratio affects the resonance distribution and output power of the HEH.  
Figure 4: Output power _versus_ excitation frequency for different \(d_{0}\).  
Figure 5: Output power _versus_ excitation frequency for different \(\mu\): (**a**) \(\alpha\) = 0.34; (**b**) \(\alpha\) = 0.23.  
For the given \(\mu\), the magnitude of the first peak power is enhanced with the smaller \(\alpha\) (\(\alpha\) = 0.23), which is contrary to the change of the second peak power. However, the first resonance shifts to a lower value with the decreasing of \(\alpha\). Under the same excitation condition, the smaller \(\alpha\) is, the more vibration energy can be harvested by magnetic oscillator. The second resonant frequency remains almost unchanged.