esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions  
A total of 21 morning peak hour traffic was considered for analysis. These involved different weather conditions consisting of different speeds in congestion ranging from 32.5 km/h to 58 km/h. Column 1 and 2 in Table 2 shows the dates of the analysis and the time of the capacity drop phenomenon, respectively. As shown in previous studies, such as by [PERSON], [PERSON], and [PERSON] [10], the capacity drop seems to correlate with the first passage of a critical density. This current paper is consistent with those findings, except that the critical density becomes significantly lower during any weather condition. On clear days, the average density triggering the capacity drop is 78 veh/km. This value significantly lowers to 65 veh/km during any rainfall condition, which is representative of the light, average, and heavy rainfall. This can be explained by drivers increasing the headway between them and the vehicles in front of them during rainy conditions making the total number of vehicles in the same span of roadway lower. This condition is fairly reproducible across different days. This shows that density directly measured from the field compared to previous studies' measurements explains the capacity drop mechanism. It can be concluded that a density of about 78 vehicles/km can serve as a kind of threshold for clear weather conditions. When densities at this bottleneck rise to this value, it is expected that the queue discharge rate will be significantly lower than the pre-queue capacity. The threshold for a rainy day is about 65 vehicles/km with uncanny reproducibility. These critical densities are shown in the fifth column of Table 2.  
As discussed earlier, this is the first time this finding is being evaluated, as previous studies have always considered clear conditions in their analysis. This has a very important significance considering that rainfall is a common event in most countries. This has also not been considered in traffic management schemes such as ramp metering, variable speed limits, or a combination of both. Currently, there are already traffic control management strategies that prevent the capacity drop phenomenon by controlling the density to not exceed the specified threshold. However, they are using the same value of density for clear conditions and during bad weather conditions. With this, there will be errors in the real-time computations since the critical density decreases during rainy days. This follows that the capacity drop will not be averted if weather factors are not considered in the
## 6 Experimental Verification
### Harmonic Suppression Performance Tests  
To verify the effectiveness and feasibility of the proposed scheme, the load current waveforms under different strategies are observed. In each group of experiments, the speed is set to 8000 rpm; the fundamental frequency is 666.7 Hz; and a load of 0.5 N/m is applied to HPMSM.  
Figure 18 shows the three-phase current waveforms and its FFT analysis. The experiments are divided into three groups: without the LC filter, with the LC filter, and with the LC filter and ANF at the same time. It should be noted that to make it easier for readers to distinguish the content of each harmonic, the FFT waveforms have different scales. When harmonic filtering is not considered, as shown in Figure 18a, the current waveforms are three-phase symmetry but contain severe saw tooth spikes. FFT analysis revealed a large number of high-frequency harmonics, and the harmonics are mainly distributed around the multiples of the switching frequency, which is consistent with the theoretical analysis and simulation results. Figure 18b illustrates the case of introducing the LC filter. The high-frequency harmonics content drops significantly, but the system has a resonant peak near 4000 Hz which is highlighted by the red column, causing the FFT to rise by 5.82% instead. The waveforms show that the saw tooth spikes of the currents are suppressed, and the jitter of the waveforms becomes less significant. However, the sinusoidality of waveforms decreases, and the three-phase current becomes unbalanced. In Figure 18c, the ANF is introduced. The sinusoidality of the currents is improved, and the waveform becomes relatively smooth and regains three-phase symmetry. The FFT result shows that the high-frequency harmonics are almost completely filtered out, and the ANF precisely suppresses the resonant peak of the system, reducing the harmonic content by 9.9% and 15.72%, respectively, compared to the above strategy.  
The three-phase current waveforms and FFT analysis results at 8000 rpm with a 0.1 N load of HPMSM, and are added further to demonstrate the ability of ANF to eliminate resonant peaks, as shown in Figure 19. When the load is reduced, the current value also decreases, and the waveform contains more ripple. However, the symmetry of the current is good, and there is no resonant peak in the FFT, indicating that the ANF can adjust the parameters automatically to achieve active damping under different load conditions.  
Figure 17: The schematic diagram of the experimental setup.
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 4 Experiment and Results
### Analysis of Experimental Results
#### 4.2.4 Counting Method Comparison Experiment  
To verify the effectiveness of the counting method proposed in this paper, we compared the experimental results before and after the addition of the specific region counting method. The experiments kept the detection model consistent and utilized the same dataset for training. The dataset contains 32 instances of tuna. All training parameters were set identically. The experimental results are presented in Table 4.  
Firstly, let us analyze the impact of different detection models on the counting results. The data in rows 1335 and 246 indicate that the performance of the detection models also has a significant impact on the counting results. YOLOv7-Tuna demonstrates superior detection performance compared to YOLOv5 and YOLOv7. Moreover, when utilizing the same ByteTrack algorithm, it also achieves better counting results.  
Next, to demonstrate the effectiveness of the specific area counting method added in this study, the comparison between rows 12, 234, as well as 56 in the table reveals that maintaining the detection algorithm constant while incorporating the specific area counting method yields superior counting results. The improvement addresses both the undercounting caused by occlusion issues and the overcounting resulting from the tuna's resistance movements. The counting error has been reduced to 3.1%, and the total erroneous count has been effectively decreased.  
The experimental results indicate that the detection model proposed in this study performs optimally, resulting in more accurate counting. It is suitable for automating the task of quantifying catch in tuna longline fishing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Counting Method** & **Repeated Count** & **Missed Count** & **Total** & **Counting Account** & **Counting Error** \\ \hline \hline \
## 4 Experiments
### Experiment Results
In order to compare the performance difference between RL + IG\({}_{\text{h}}\) and other algorithms, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multicolumn{2}{c}{**RL-Sample**} & \multicolumn{2}{c}{**RL-Greedy**} & \multicolumn{2}{c}{**NEH**} & \multicolumn{2}{c}{**SLW**} & \multicolumn{2}{c}{**EDD**} \\ \cline{2-10}  & **ARDP** & **ACT(s)** & **ARDP** & **ACT(s)** & **ARDP** & **ACT(s)** & **ARDP** & **ACT(s)** & **ARDP** \\ \hline \(20\times 5\) & **0** & 0.17 & 11.60 & 0.11 & 17.62 & 0.003 & 95.57 & 0.002 & 90.39 \\ \(20\times 10\) & **0** & 0.18 & 19.65 & 0.12 & 33.23 & 0.003 & 138.2 & 0.002 & 84.29 \\ \(20\times 20\) & **0** & 0.18 & 17.75 & 0.11 & 20.25 & 0.003 & 108.6 & 0.002 & 125.72 \\ \(50\times 5\) & **0** & 0.24 & 7.00 & 0.14 & 20.20 & 0.022 & 65.78 & 0.010 & 184.57 \\ \(50\times 10\) & **0** & 0.29 & 10.26 & 0.14 & 23.17 & 0.023 & 91.85 & 0.012 & 130.32 \\ \(50\times 20\) & **0** & 0.30 & 20.27 & 0.13 & 47.62 & 0.023 & 151.7 & 0.015 & 114.15 \\ \(100\times 5\) & **0** & 0.55 & 3.56 & 0.21 & 4.42 & 0.083 & 38.28 & 0.040 & 101.34 \\ \(100\times 10\) & **0** & 0.61 & 6.50 & 0.22 & 7.04 & 0.084 & 52.42 & 0.042 & 92.54 \\ \(100\times 20\) & **0** & 0.63 & 5.25 & 0.21 & 7.26 & 0.088 & 58.51 & 0.042 & 84.33 \\ \(200\times 10\) & **0** & 1.41 & 1.62 & 0.32 & 0.45 & 0.360 & 34.43 & 0.210 & 44.31 \\ \(200\times 20\) & **0** & 1.49 & 5.49 & 0.33 & 3.45 & 0.400 & 41.75 & 0.220 & 54.05 \\ average & **0** & 0.55 & 9.90 & 0.19 & 16.79 & 0.099 & 79.74 & 0.054 & 100.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of RL and multiple heuristic algorithms. Figure 10: Box plot of RL and several heuristic algorithms. are both LSTM structures, the output of each step of the encoder is used as a reference vector \(r_{i}\). Table 8 shows the objective function values of the two methods on some problem instances, the decoding strategy of both methods is greedy strategy. The performance of PtrNet deteriorates rapidly as the number of jobs increases, and if the number of artifacts is greater than 50, the objective function value of the obtained solution is worse than NEH. The PtrNet model can converge during training, but the model ends up in a local optimum, which reflects that GIN can learn the context information of the job; it greatly improves the model's performance, and the proposed RL method has a 6.7% performance improvement over PtrNet. The result of the attention mechanism is visualized as a heat map, as shown in Figure 12. Each color block in the figure represents the probability of selecting each job at time step \(i\). The brighter the color, the greater the probability. Figure 12a shows the heat map of the probability distribution of attention output before the model training, and Figure 12b shows the heat map result after the training. The color blocks are messy before training, and the probability distribution is clear after training. According to the greedy decoding strategy, it can be easily derived from the figure; the final job scheduling sequence is 3-17-15-8-9-14-11-13-5-7-4-19-6-16-1-18-2-12-10-20.
# Deep-Learning Multiscale Digital Holographic Intensity and Phase Reconstruction
## 3 Experiment and Result Analysis
### Experimental Results and Analysis
#### 3.3.3 Comparison of Mimo-Net and Y-Net at 128 \(\times\) 128 Scale  
The reconstructed results of the Mimo-Net and Y-Net models were compared using images at 128 \(\times\) 128 scale, the enlarged comparison figures are shown in Figure 7.  
Figure 7 shows that the intensity image reconstructed by the proposed Mimo-Net network is better improved than that reconstructed by the Y-Net network, and the phase image is also improved Table 4 shows the PSNR and SSIM values of the images reconstructed by the Mimo-Net network and the Y-Net network compared to the original images.  
Table 4 shows that the strength and phase images reconstructed by the Mimo-Net network model have improved PSNR values by 2.7914 dB and 0.0177 dB, respectively, compared to the Y-Net network. The SSIM values have also increased by 0.2442 and 0.0164, respectively. Therefore, the Mimo-Net network exhibits better reconstruction performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Intensity of Y-Net** &
\begin{tabular}{c} **Intensity of** \\ **Mimo-Net** \\ \end{tabular} & **Phase of Y-Net** & **Phase of Mimo-Net** \\ \hline PSNR/dB & 31.5431 & 33.9632 & 31.0891 & 31.3922 \\ SSIM & 0.9251 & 0.9463 & 0.9208 & 0.9252 \\ \hline \hline \end{tabular}
\end{table}
Table 3: 256 \(\times\) 256 scale PSNR and SSIM.  
Figure 6: Comparison of Mimo-Net and Y-Net network reconstruction results at 256 \(\times\) 256 scale.
## 4 Discussion  
Many types of architecture for systems for data acquisition and control are available, which offers various possibilities of operation, changing the number of channels, the speed, the resolution, the accuracy, and the costs.  
Plug-in data acquisition boards are an important category. Like display adapters, modems, and other types of expansion cards, plug-in data acquisition cards are designed to fit into computer motherboard slots.  
Figure 14: Calculated circuit phase shift, 3 A load.  
Figure 13: Calculated circuit phase shift, 2 A load.  
Viewed as a category, plug-in boards offer a wealth of test functions, a high number of channels, high speed, and adequate sensitivity to measure low-level signals, at a relatively low cost.  
External data acquisition systems represent another category of data acquisition systems.  
For economic reasons, a common \"plug-in\" acquisition board was chosen in this work. The first step in the design of such a system was the choice of the acquisition board, depending on its performance and its use. Since the quantities to be measured were known, a plate at an optimal price was chosen. If high performance is required, the purchase and installation price of the employed system is high; however, for the measurement system presented and the performance required, both the acquisition board used and the operating system in which it was integrated were purchased at a modest price. The choice of the acquisition board should be based, first of all, on the sampling speed of the analog-to-digital converter, which is the main factor that dictates the price of the acquisition board. Although the board used did not have a high sampling rate, which was only of 100 kS/s, it was sufficient for the chosen system, where the network frequency was 50 Hz. The maximum order of harmonics that can be visualized is 30-40, respecting [PERSON]'s theorem. Another problem is the number of input channels available, and their reference mode. The chosen board had 16 input channels for analog signals with a reference, much more than the required 6 channels + a ground channel.
###### Abstract  
As a crucial part of ubiquitous networking and computing (UNC) technologies, low earth orbit (LEO) satellite communications aim at providing internet connectivity services everywhere. To improve the spectrum efficiency of satellite-to-ground communications, adaptive modulation and coding (AMC) are widely used, which can adjust the modulation and coding types according to the varying channel condition. However, satellite-to-ground communication channels have the characterizations such as fast dynamic change, fast switching, and significant fading. These characterizations make it challenging to predict the channel state information accurately and, thus, to perform accurate AMC. For example, rain loss is one of the crucial factors in satellite-to-ground channel fading. In general, it is difficult to build an integrated global model for rain loss because it varies in different regions around the world. Moreover, for the emerging applications of multiple antennas on satellites, the conventional look-up table method cannot cope with the high-dimensional inputs of the multiple antennas. To tackle the above challenges, we propose an AMC method based on deep learning (DL) and deep reinforcement learning (DRL) for ubiquitous satellite-to-ground networks. The proposed method directly processes real-time global weather and location information in the environment and intelligently selects encoding schemes to maximize system throughput. Simulation results show that the proposed method can increase the total throughput. The total number of correctly transmitted bits per unit time is improved, and the efficiency of the satellite-to-ground communication is enhanced.  
ubiquitous networking and computing; adaptive modulation and coding; deep learning; deep reinforcement learning; satellite communication; rain loss Article
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 3 \(\mu\)TSN-CP Architecture
### SDN Controller Microservice  
Finally, the SDN Controller is the microservice that acts as the interface between the CP and the network. It has a RESTCONF-type communication interface to communicate with the Southconf microservice and a NETCONF communication interface to communicate with the TSN switches in the network. The main reason why we resort to this microservice instead of a direct NETCONF communication between the Southconf microservice and the network equipment is the simplicity and benefits of using an SDN controller. Figure 8 shows the operation diagram of the SDN Controller microservice.  
In our case, we chose OpenDaylight [30] as the SDN controller for our CP. OpenDaylight includes a module for YANG models and RESTCONF/NETCONF that allows storing all exposed models in a device inventory. By incorporating the list of devices with their respective IP addresses, the controller automatically creates a data model that allows all devices to be accessed simultaneously using a single RESTCONF communication. Therefore, it is only necessary to send a message containing all the configurations that must be applied to the equipment to configure the TAS with the offsets provided by the ILP. The process that this microservice follows is as follows:  
* The Southconf microservice sends the necessary configuration, including device IP addresses, using the RESTCONF protocol via REST API-like communication.
* OpenDaylight creates an inventory of all the TSN devices in the network and generates a data model that includes all the YANG models exposed in the NETCONF interfaces of the switches.
* Using NETCONF, OpenDaylight accesses all the TSN devices and configures them by following the directions provided by the Southconf microservice.
## 2 Related Works
They presented the model's classification performance over the full and reduced dataset. Their results show that, in general, the performance of classifiers did not drop when a reduced feature set was used. These researchers would have achieved better results if an ensemble had been used. [PERSON] et al. [26] performed their experiments on the UNSW-NB15 dataset. They identified that multivariate correlation techniques are rarely used in dimensionality reduction. To counter this trend, they developed a feature selection technique named CorrCorr. This method, combined with an addition-based correlation detection, produced a feature set that was compared with the original feature set and PCA. The CorrCorr feature set showed better performance. [PERSON] et al. [9] performed their experiments on three datasets: ISCX 2012, NSL-KDD, and Kyoto 2006+. This research used a hybrid dimensionality reduction approach by combining information gain and Principal Component Analysis techniques. An ensemble classifier was created using the Support Vector Machine, an instance-based learning algorithm, and multi-layer perceptron for classification. The performance of the proposed system was evaluated on the accuracy, detection rate, and false positive rate. Results show that the proposed system outperformed individual approaches. Our research is related to this work, but instead of using information gain, we have used the recursive feature elimination technique since information gain cannot perform well when an attribute has a very high number of distinct values [27]. [PERSON] and [PERSON] [28] determined significant features of the UNSW-NB15 and KD-DCup99 datasets. They replicated these datasets and used the association rule mining technique to generate the strongest features. They evaluated the predictive power of selected features using multiple classifiers and used accuracy and the false positive rate as evaluation metrics. Their results show that the accuracy of the KDDCup99 dataset is better than the UNSW-NB15 dataset; however, the false positive rate is lower. This research did not investigate if the identified features can be transformed to reduce input data size further without hitting the performance of classifiers. In summary, contemporary research used a range of classification techniques on various network datasets. These techniques used PCA, autoencoders, genetic algorithms, and correlation-based methods for feature selection and reduction. These techniques utilized diverse classifiers, including SVC, MLP, DT, XGBoost, and Ensemble models for classification.
# Research on the Aerodynamic-Propulsion Coupling Characteristics of a Distributed Propulsion System
## 3 Numerical Method Validation and Grid Independence Validation
### Validation of Numerical Method  
The NACA 4415 airfoil is selected, and the numerical simulation method for the flow field of the wing is validated by comparing the numerical simulation results in the present study with the experimental results [10]. The geometric model and computational domain of the NACA 4415 wing are illustrated in Figure 8. The geometric parameters of the airfoil and the incoming flow conditions can be found in Reference [10]. The radius of the computational domain is 25 times the chord length of the airfoil and a pressure far-field boundary condition is applied. A density-based implicit coupling algorithm is selected to solve the three-dimensional steady RANS equations using the k-\(\omega\) SST turbulence model. The height of the first layer of the boundary layer grid is 0.01 mm to ensure that y +\(\approx\) 1. The grid growth rate is 1.2, and the total number of grids in the boundary layers is 30. The wall was set to a no-slip adiabatic wall.  
The variations in lift and drag with the inflow angle of attack (AOA) for the NACA 4415 wing are presented in Figure 9. It can be observed that the variations in the lift and drag with the angle of attack agree well with the experimental and numerical simulation results [10]. At low angles of attack, the numerical simulation results obtained in this study coincide with those reported in the literature. Near the critical angle of attack, there are  
Figure 8: Geometric model and computational domain of NACA 4415 wing: (**a**) geometric model; (**b**) computational domain.  
Figure 7: Five-ducted-fan over-wing integrated model.  
slight deviations in lift due to the simplification of the wing model and CFD methods. However, the lift and drag values obtained in this study are closer to the experimental results. Overall, the numerical simulation method still has high reliability in rapidly evaluating the characteristics of wing flow fields.
## 3 Proposed Methodology
### Data Preprocessing  
#### 3.3.1. Data Denoising  
The raw sensor data contains process measurement noise or other unanticipated noise resulting from the individual's lively motions during the investigations. A noisy signal distorts the signal's actionable data. As a result, it was critical to decrease the influence of noise to recover relevant information from the signal for the production process. The most frequently used filtering techniques are the mean filter, the low-pass filter, the Wavelet filter, and the Gaussian filter [46]. Our investigation used a mean smoothing filter for the accelerometer and gyroscope sensors to denoise the signals.  
Figure 2: The number of activity samples in the HAPT dataset.
## 2 Study Area and Data Sources
### Landslide Conditioning Factors
#### 2.3.1 Topographic Factors
Profile curvature (Figure 2f) measures the rate of slope variation, indicating whether the terrain is steep or gentle, affecting water flow velocity and erosion dynamics [35]. Figure 2: Landslide conditioning factors: (**a**) DEM, (**b**) slope degree, (**c**) slope aspect, (**d**) plan curvature, (**e**) TWI, (**f**) profile curvature, (**g**) NDVI, (**h**) soil types, (**i**) distance from rivers, (**j**) distance from roads, (**k**) lithology, and (**l**) fault density.