esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Experimental Results
### Simulations
#### 4.2.2 Multi-Mobile Robot Experiments  
Just like the simulations, we also carried out our approach in three cases. Referring to the simulations for the initial position and speed of the target, other parameters of three groups of experiments are shown in Table 2. The experimental results are shown in Figures 7-9. The motion trajectories in Figures 7a,b-9a,b indicate that the three nonholonomic mobile robots can track a stationary or moving target, form a uniformly distributed circular formation with the same or different radii around the target, and effectively avoid the obstacles encountered. Figures 7c-e and 8c-e indicate that the errors of the control objectives fluctuate around zero, but Figure 9c,e show that the phase error of robot 1 and the angular spacing error between the each pair of two adjacent robots fluctuate greatly because of the angular speed of the target is always fluctuating. It takes a certain time to adjust the robots' orientation to achieve the desired values, as can be seen from the output angular speed of the controller in Figure 9g. However, it can be seen from Figure 9f that all robots can track the linear speed of the target.  
The above three groups of experiments can obtain similar results to the simulations, which shows that the designed controller can work effectively and perform well.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Parameter** & **Case 1** & **Case 2** & **Case 3** & **Unit** \\ \hline \(\rho^{d}\) & \([0.8,0.8,1.2]^{T}\) & \([0.5,0.5,0.5]^{T}\) & \([0.5,0.5,0.5]^{T}\) & m \\ \(\Delta\rho^{d}\) & \([2\pi/3,2\pi/3,2\pi/3]^{T}  
Figure 7: Experimental results of case \(1\). **(a)** The motion views of three nonholonomic mobile robots forming a evenly distributed circular formation with two different circular radii to enclose a static target. **(b)** Trajectories of the three robots and the target in the plane. **(c)** Error between current phase and the desired phase of robot \(1\). **(d)** Distance errors between the three robots and their desired circles centered on the target. **(e)** Errors between current angular spacing and the desired angular spacing of all adjacent inter-robots. **(f)** The linear speeds of the three robots. **(g)** The angular speeds of the three robots.  
Figure 8: Experimental results of case 2. (**a**) The motion views of three nonholonomic mobile robots forming a evenly distributed circular formation with same circular radius to enclose and track a moving target with a constant speed. (**b**) Trajectories of the three robots and the target in the plane. (**c**) Error between current phase and the desired phase of robot 1. (**d**) Distance errors between the three robots and their desired circles centered on the target. (**e**) Errors between current angular spacing and the desired angular spacing of all adjacent inter-robots. (**f**) The linear speeds of the three robots. (**g**) The angular speeds of the three robots.
# Analysis of the Water Quality Improvement in Urban Stream Using MIKE 21 FM
## 3 Selection and Current Status of the Target Area
### Comparative Analysis of the Observed Water Quality Concentration
Regarding DO, BOD, TN, and TP, the water quality concentration in the middle and downstream has a similar tendency to each other. In particular, in the case of the midstream, high concentrations of water quality were found for BOD, TN, and TP due to the influence of non-point sources during a certain period. Table 1 is the statistics of all measured water quality from April 2016 to December 2019. The concentrations of BOD, TN, and TP were higher in the middle and downstream than the upstream. In particular, in the middle stream, the BOD concentration increased to 46 mg/L. Overall, the upstream showed stable water quality compared to the middle and downstream areas. Figure 5: Water quality comparison of the downstream of Gulpocheon [23].
## 3 Experiment and Analyses
### Static Experiment with Zero-Baseline  
#### 3.1.1 Fault Identification  
Since the antenna is shared between the rover and the base stations, the error source such as multipath has been eliminated by DD and the factor affecting positioning only remains noise. Therefore, the DD pseudorange residuals should be close to zero in theory. In Experiment 1, the receiver antenna is placed near (0.64, 0.08) and the positioning results based on carrier phase observations are shown in Figure 5. The color bar represents epochs.  
According to Figure 5, the ambiguity fixed rate is 25%. However, even if the ambiguities can be fixed in some epochs, it is fixed incorrectly. There is a large deviation in positioning results, and they are not reliable. In Experiments 1, the reference pseudolite is PL5 and DD pseudorange observations are shown in Figure 6.  
Figure 5: (**a**) Horizontal Positioning Results of Experiments 1, (**b**) Positioning Results on X, Y.  
In theory, DD pseudorange residuals in Figure 6 are close to 0, but it is obvious that there are abnormalities in DD observations of all pseudolites. The same circumstance is also found in DD carrier phase observations. It returned to normal around the 10 th epoch. Therefore, it can be judged that the reference pseudolite PL5 is faulty. The reason for the bad pseudorange quality of PL5 may be an accidental fault from pseudolite hardware itself.
## 6 Conclusions
\begin{tabular}{l l} \(1/T_{L}\) & natural frequency of the control loop \\ \(WP_{i}\) & way points \\ \(1/T_{Lc}\) & natural frequency of course loop \\ \(K_{w}\) & wave strength factor \\ \(1/T_{Lr}\) & natural frequency of RRR loop \\ \(K\) & turning-ability index \\ \(m_{x},m_{y}\) & added mass of inertia about \(x,y\) \\ \(T\) & turning-lag index \\ \(J_{x},J_{z}\) & added moments of inertia about \(x,z\) \\ \(E_{r}\) & range of track-belt \\ \(l_{x},l_{z}\) & moment of inertia about \(x,z\) \\ \(\psi_{d}\) & set course \\ \(l_{x},l_{y}\) & \(z\)-coordinates of the centers of \(m_{x},m_{y}\) \\ \(a_{d}\) & drift angle \\ \(e_{c},e_{r}\) & deviation of course and roll \\ \(C_{b}\) & block coefficient \\ \(k_{\zeta 1},\,k_{\zeta 2}\) & positive design parameters \\ \(P_{r}\) & propeller revolutions \\ \(k_{\tau 1},\,k_{\tau 2}\) & positive design parameters \\ \(U_{0}\) & forward speed \\ \(v,v_{c},v_{r}\) & linear robust controllers \\ \(U_{\xi}\) & current speed \\ \(\psi_{d},\phi_{d}\) & set course and set roll angle \\ \(a_{c}\) & current direction \\ \(\psi,\phi\) & yaw and roll angle \\ \(H\) & significant wave height \\ \(a_{0},a_{1},a_{2}\) & constant coefficients \\ \(r_{w}\) & turning radius \\ \(b,b_{c},b_{r}\) & constant coefficients \\ \(\phi_{\max}\) & maximum of roll angle \\ \(d_{i},\phi_{i},\delta_{i}\) & sample values of \(d,\phi,\delta\) \\ \(N_{s}\) & sample number \\ \(u,v,r,p\) & surge, sway, yaw and roll velocities \\ \(\delta\) & rudder angle \\ \(X,Y,N,J\) & hydrodynamic forces and moments \\ \(K_{\phi}\) & speed-related gains in roll \\ \(\tau_{X},\tau_{Y},\tau_{N},\tau_{J}\) & control inputs \\ \(K_{\phi}\) & speed-related gains in yaw \\ \(f_{d},f_{\phi_{r}},f_{\delta}\) & RMS values of \(d_{i},\phi_{i},\delta_{i}\) \\ \(GM\) & metacentric height \\ \((x_{y},y_{S})\) & ship's position S coordinates \\ \(a_{y}\) & \(x\)-coordinate of \(m_{y}\) center \\ \((x_{\mathrm{P}},y_{\mathrm{P}})\) & cross position P coordinates of the planned route and the vision field arc \\ \end{tabular}
## 2 Materials and Methods
### Element Analysis  
In research, it is important to accurately detect the concentration of heavy metals. Multiple methods are used to determine the concentrations of heavy metals. Atomic absorption spectrometry (AAS) has been widely used for its simple operation and good accuracy [24]. However, it requires expensive instrumentation and provides inefficient measurements. Electrochemical methods have received interest for their quick detection speed, easy manipulation, low cost, and high sensitivity [25]. However, due to their serious toxicity and difficult handling, an iron oxide/graphene composite, with moderate toxicity and stability, has drawn rising attention to replace it [26]. Plasmonic optical fiber gratings functionalized with bacteria are a new, interesting technique to detect heavy metals with high sensitivity [27]. These methods cannot provide simultaneous detection when the species of elements are diverse. In the present study, inductively coupled plasma mass spectrometry (7700\(\times\), Agilent Technologies, Palo Alto, CA, USA) was applied to detect heavy metals because of its optimum sensitivity, highest accuracy, and effective speed [28; 29]. We used it to analyze solutions acidified with microwave-assisted nitric acid, hydrogen peroxide, and hydrofluoric acid. Pb and Cd were detected using ICPMS, following the USA EPA 6020a method (Revision 1, February 2007). The detection limits of Pb and Cd were 0.001 and 0.002 \(\mathrm{\SIUnitSymbolMicro g}\)/L, respectively. Quality control was ensured through the analysis of two reference materials from the National Quality and Technology Supervision Agency of China (GBW07603 and GBW07604). Accuracy was checked by measuring reference materials. The measurement errors were below 5%, and the recovery of trace metals ranged from 99% and 110% of the reference materials. Geostatistical analysis was performed by ArcGIS 10.6. Descriptive statistics of Pb and Cd between needles and twigs were carried out by SPSS version 12.0 software with the Mann-Whitney U test. The significance level was set as \(p<0.05\).
# High-Performance Garbage Collection Scheme with Low Data Transfer Overhead for NoC-Based SSDC
## 4 Evaluation
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Max. Read Latency (ms) & Cassandra & Dbench & MySQL & SQLite & MongoDB & RocksDB \\ \hline Conventional & 181.257 & - & - & - & 2.832 & 21.686 \\ Proposed & 90.625 & - & - & - & 2.474 & 20.625 \\ \hline Max. Write Latency (ms) & Cassandra & Dbench & MySQL & SQLite & MongoDB & RocksDB \\ \hline Conventional & 1055.123 & 977.507 & 29.450 & 3.776 & 3.602 & 51.816 \\ Proposed & 758.686 & 836.451 & 25.372 & 3.341 & 3.200 & 50.637 \\ \hline Proposed/Conventional & Cassandra & Dbench & MySQL & SQLite & MongoDB & RocksDB \\ Improvement Ratio (\%) & 50.002 & - & - & - & 12.641 & 4.893 \\ Max. Write latency & 28.095 & 14.430 & 13.847 & 11.520 & 11.160 & 2.275 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Peak latency of read/write requests. Figure 9: Read and write request patterns of Cassandra.
## 3 Results
### Logistic Regressions
#### 3.2.3 Results for Epoch 2 (4-6 Years)  
In epoch 2, the Logistic Regression Model returned a significant level (Chi-Square = 170.52; \(p\) < 0.001), showing that the factors considered significantly predicted episodic memory when taken together. The analyses performed on epoch 2 showed very similar results to those obtained from the analyses performed on the entire sample of memories. In particular, it was observed that the presence of PVF, GSM, EpF and REp significantly reduced the probability that SEPE were also present (see Table 2).  
Upon analysis of how frequently SEPE and other types of memory were present together, it emerged that SEPE was present in 8% of cases containing PVF, in 11.8% of cases with GSM, in 3.4% that had EpF and in 16.7% with REp.  
The relationship between the number of Non-Single Episodic Event (NSEpE) memories and the presence of SEPE memories was evaluated by means of a Logistic Regression analysis. The results showed that as the number of incomplete memories increases, the likelihood of observing SEPE significantly decreases (B = \(-\)3.58 (SE = 0.46), Wald's Chi\({}^{2}\) = 60.03 (df = 1); \(p\) < 0.001, Exp(B) = 0.028).  
These results suggest that the more the participants report perceptual-visual fragments, general semantic memories, episodic fragments, and repeated episodes, the less likely it is that the memory is structured as episodic (as the NSEpE reported an increase in memories, the probability of SEPE decreased).
## 3 Construction of the Model
### Level 2 Evaluation: Signal Spectrum  
At Level 2, the EMC evaluation focuses on the types of interference in terms of the signal spectrum. In general, there are three types of spectral EMI: fundamental wave interference, high-order harmonic wave interference, and high-order intermediation interference. In accordance with the fact that spectral EMI can arise from the signal components, the IM criterion equations are given as follows.  
The IM criterion for fundamental wave interference is expressed as follows:  
\[|f_{T}-f_{R}|\leq\frac{B_{T}+B_{R}}{2} \tag{9}\]  
The IM criterion for harmonic wave interference is expressed as follows:  
\[|nf_{T}-f_{R}|\leq\frac{B_{Tn}-B_{R}}{2},\;\;n<5 \tag{10}\]  
The IM criterion for intermediation wave interference is expressed as follows:  
\[||mf_{\text{T1}}\pm nf_{T2}|-f_{R}|\leq\frac{B_{R}+B_{\text{T1}m}+B_{T2n}}{2}, \;\;3<m+n<5 \tag{11}\]  
Above, \(f_{T}\) and \(f_{R}\) denote the working frequencies of the transmitter and receiver, \(B_{T}\) and \(B_{R}\) denote the bandwidths of the transmitter and receiver, \(nf_{T}\) is the working frequency of the n-th order harmonic wave of the transmitter, \({{B}_{Tn}}\) is the bandwidth of the n-th order harmonic wave of the transmitter, \({{m}f_{T1}}\) is the working frequency of the m-th order harmonic wave of transmitter 1, \({{n}f_{T2}}\) is the working frequency of the n-th order harmonic wave of transmitter 2, \({{B}_{T2n}}\) is the bandwidth of the n-th order harmonic wave of transmitter 2, and \({{m}+n}\) is the synthetic order coefficient of the intermediation interference.  
The second-level EMC evaluation is performed using the above equations. If any equation is satisfied, then the third-level EMC evaluation result will be \(\text{IM}>0\), which indicates that a potential EMI is present, and a third-level evaluation is required.
###### Abstract  
A power electronics converter is generally designed for a specific load condition. However, depending on the applications and its mission profiles, the operating load conditions can be distinctly lower than the specified ones (PV cell under shading conditions, etc.). During this light load condition, the efficiency diminishes considerably, especially if Si-IGBT devices are considered within the power circuit. This study explains a light-load circuit extension based on wide-bandgap (WBG, silicon carbide and gallium nitride) material, which can improve the light-load efficiency and transient response of the conventional IGBT-based active rectifiers and inverter. Such an additional circuit extension is, in general, associated with additional cost. Numerous factors, such as the power electronics application itself, mission profiles, converter power rating and sizing of passive components, etc., can shift the break-even point of the upgraded power electronics system in terms of time. Therefore, a profound investigation of the relevant areas of interest is required in advance to ensure the most efficient amortization of the additional incurred costs of the applied circuitry. A 125 kW 3-phase six-switch inverter is discussed to highlight relevant effects in light-load operation that must be considered for final product design.  
efficiency; wide bandgap; power electronics; AC/DC; DC/AC Article
## 4 Experimental Design
### Network Architectures
#### 4.2.1 3D Convolutional Neural Networks (3D CNNs)  
The 3D-CNN model is a variant of the traditional CNN and is composed of a feature extractor and an ANN (artificial neural network) classifier. Figure 4 depicts the model architecture for this evaluation, which consists of five CNN layers and two fully connected dense layers. The input 3D lung volume is passed through the first layer of the CNN using a sliding window of 3 \(\times\) 3 \(\times\) 3. The output of the first layer has dimensions of (32,32,16,1) and is passed to the next convolution layer, resulting in an output of with dimensions of (32,32,16,8). Each convolution layer uses a 3 \(\times\) 3 \(\times\) 3 kernel and regularizers with a regularization factor of 0.001. The activation function after each convolution layer is ReLU. The max pooling layer downsamples the feature maps before passing them on to the next convolution layer, using a pool size of (3,3,3), resulting in an output of shape of (11,11,6,8). Two more convolution layers are applied to the image, producing an output of shape (11,11,6,16). The final max pooling layer downsamples the output to (4,4,2,16). A dropout layer and a flattening layer are then applied to the image, with the latter reshaping the features into a 1D array that can be fed to the dense layer. The ANN component consists of two fully connected dense layers with ReLU as the activation function. The flattened image passes through the dense layer with 64 neurons, followed by batch normalization and dropout layers with 64 features each. The final layer is an output dense layer with one neuron.
## 1 Introduction
Despite the fact that deep learning methods often aim for end-to-end solutions, directly predicting trajectories from observed coordinates, we argue that extracting pedestrians' comprehensive motion features and using them as inputs for social interaction modules can more effectively capture motion-related pedestrian behaviors, such as following, collision avoidance, and walking in parallel. Although this approach may not be purely end-to-end, this paper also demonstrates that incorporating feature engineering can enhance the performance of artificial neural networks in tasks where features are explicitly defined. Figure 1: Illustration of a pedestrian’s trajectory, which is influenced by the surrounding people and environment, depicting multiple future possibilities. In this paper, we propose a Social and Spatial Attentive Generative Adversarial Network (SSA-GAN) for predicting pedestrian trajectories. This model can be integrated into the controllers of mobile robots, enhancing route planning efficiency by taking into account the movement of surrounding individuals. Our proposed prediction neural network comprises a social interaction module with neighboring pedestrians and a spatial interaction module with the scene, similar to SoPhie [22]. However, we extract comprehensive pedestrian motion features and model social interactions among pedestrians using a social attention mechanism. To represent human-space interactions, we employ a Visual Geometry Group (VGG) network pretrained on a semantic segmentation task to extract semantic features of the space as the input for the space attention module. Finally, we use an LSTM-based generative adversarial network to produce a diversified range of future trajectories. We assess the model's performance using both public single ground-truth trajectory datasets and our own multi-trajectory datasets. This paper's main contributions are as follows:  
1. A comprehensive motion feature extraction method, which includes the relative distance, speed, and velocity angle, is used as the query for an attention mechanism-based social interaction module. 2. A generative network, which is aware of social and spatial interactions, generates more socially and physically feasible trajectories. 3. The novel idea of using the CARLA simulator and manual annotations to create multi-trajectory datasets can enable the evaluation of metrics across multiple predicted trajectories. The remainder of this paper is organized as follows. Section 2 provides an overview of related works, and Section 3 introduces the problem definition along with our proposed prediction model. In Section 4, we present both the qualitative and quantitative results, comparing them with baselines and providing corresponding discussions. Finally, Section 5 concludes the paper.
## 3 Simulated Study of Different Decoupling Techniques  
A microstrip-fed circular patch and a partial ground with an open-end slot were used as a single UWB AE for simulation [74]. The isolation between the two-port UMA (designed using the same AEs) is enhanced through the application of various DTs. The simulations were carried out using the Ansys HFSS software. The results, such as \(S_{11}\), \(S_{12}\) (mutual coupling), and 3D radiation plots, were considered for the investigation of DTs.  
To verify the effect of mutual coupling on antenna radiation patterns, the radiation pattern/s of a single UWB AE and two-port UMA with and without CG are plotted, which are placed at quarter-wavelength separation in Figure 11. A CG design is essential for the MIMO antenna as all signal levels inside it can be measured accurately on a similar reference level. If separate and non-associated ground planes are used, the MIMO antennas may not work in real-world applications because the assumption that all ground surfaces have similar voltage levels becomes invalid [59]. The radiation pattern of a single UWB AE has an omnidirectional shape, and two-port UMAs tend to radiate in opposite directions (making the patterns orthogonal to each other) due to the mutual coupling, despite being with/without CG. This proves that the mutual coupling changes the shape of the radiation pattern.  
In Figure 12a, the \(S_{11}\) values of a single AE and a two-port UMA with/without CG are shown. As per the plot, there is not much variation in the \(S_{11}\) values because of the same AE/s design. In Figure 12b,c, \(S_{12}\) (representing mutual coupling) and ECC of two-port UMA with/without CG are displayed, respectively. The isolation without CG is higher than with CG as there is no connection between the ground planes of the two AEs, and no current will flow to the other port. Similarly, ECC values without CG are lower than those with CG. The recommended value of ECC is 0.5 for a good diversity performance of  
Figure 11: (**a**) Isolated UWB AE, (**b**) two-port UMA without CG, (**c**) two-port UMA with CG, (**d**) radiation pattern of the isolated UWB AE, (**e**) radiation patterns of the two-port UMA without CG when port-1 and port-2 are excited separately, and (**f**) radiation patterns of the two-port UMA with CG when port-1 and port-2 are excited separately.  
a MIMO system. It is shown in the plot that by only placing AEs with a proper gap, the ECC value is less than 0.02.