esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 4 Simulation Results and Analysis
### Simulation Results
#### 4.2.6 Complexity Analysis  
The depthwise separable convolution splits a standard convolution into two convolutions (depth-by-depth, point-by-point), thus reducing the number of parameters and the total computational effort. In this paper, the lightweight model is based on the depthwise separable convolution, so the space complexity and time complexity of the standard convolution and the depthwise separable convolution are analyzed here. The space complexity is measured by the number of parameters in the convolutional layer, and the time complexity is measured by floating point operations (FLOPs). The space complexity and time complexity of the standard convolution are shown as  
\[P_{std}\sim O(K_{lb}K_{w}C_{in}C_{out}), \tag{7}\]  
\[T_{std}\sim O(K_{lb}K_{w}C_{in}C_{out}G_{lb}G_{w}), \tag{8}\]  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Rotate** & **RandMix** & **Rotate+RandMix (Our Proposed)** \\ \hline Accuracy & 88.85\% & 67.40\% & 96.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation experiments.  
Figure 12: Feature visualization.  
where \(P_{std}\) and \(T_{std}\), respectively, represent the parameters and FLOPS; \(K_{h}\) and \(K_{w}\), respectively, represent the two dimensions of the convolution kernel; \(C_{in}\) and \(C_{out}\), respectively, represent the number of input channels and output channels; \(G_{h}\) and \(G_{w}\), respectively, represent the two dimensions of the output characteristic graph. Compared to standard convolution, the space complexity and time complexity of the depthwise separable convolution can be expressed as  
\[P_{sep}\sim O(K_{h}K_{w}C_{in}+C_{in}C_{out}), \tag{9}\]  
\[T_{sep}\sim O(K_{h}K_{w}C_{in}G_{h}G_{w}+C_{in}C_{out}G_{h}G_{w}). \tag{10}\]  
The ratios of the space complexity and time complexity of depthwise separable convolution and standard convolution are as follows.  
\[\frac{P_{sep}}{P_{std}}=\frac{K_{h}K_{w}C_{in}+C_{in}C_{out}}{K_{h}K_{w}C_{in }C_{out}}=\frac{1}{C_{out}}+\frac{1}{K_{h}K_{w}}, \tag{11}\]  
\[\frac{T_{sep}}{T_{std}}=\frac{K_{h}K_{w}C_{in}G_{h}G_{w}+C_{in}C_{out}G_{h}G_{ w}}{K_{h}K_{w}C_{in}C_{out}G_{h}G_{w}}=\frac{1}{C_{out}}+\frac{1}{K_{h}K_{w}}. \tag{12}\]  
From Equations (11) and (12), it can be found that the number of parameters and FLOPS of the depthwise separable convolution is much smaller compared to the standard convolution, so the model can be more lightweight. A detailed comparison of the complexity of the lightweight CVResNet in this paper and the standard CVResNet is shown in Table 5. From the table, we can see that the number of parameters in the lightweight CVResNet has decreased by about 83.34%, and the FLOPs have decreased by about 83.77%, compared to those of the standard CVResNet.
## 3 Monitoring Data Collecting for the Forth Bridge  
### Data Collecting  
At present, the number of long-span bridges across the world is increasing, and extreme weather, including significant temperature fluctuations, strong hurricanes, and earthquakes, continue to damage the structural health of these buildings. Coupled with the increased demand for use, these inevitably accelerate the aging process of structural components. Therefore, it is necessary to conduct a comprehensive assessment of the safety and sustainability for these large bridges during operation. The GeoSHM system, which funded by the European Space Agency, can be employed by the bridge operators to conduct such an assessment. The GeoSHM project is designed as an integrated sensor system that uses GNSS technology integrating low-cost sensors (inertial and inclinometers, etc.) to monitor the deformation of the bridge and ensure the safety and reliability of the bridge operation, whilst using the remote sensing technique to do large scale land stability search. It is divided into five subsystems corresponding to the sensors, data collecting, data processing, bridge structure assessment, and data management. The project was launched in 2013 and it has been providing long-term monitoring for several bridges in the United Kingdom (UK) and China that include the Forth Road Bridge.  
The Forth Road Bridge is located above the Forth River in the north of Edinburgh, Scotland. The bridge is about 2.5 km long and its main span length is 1006 m. In this paper, the Forth Road Bridge is monitored by the GeoSHM project to obtain bridge vibration information. The panorama of the bridge, the distribution of the GNSS sensors, and the bridge coordinate system are shown in Figure 1. Figure 2 shows the pictures of the GNSS stations and their surroundings. Generally, shm1 is set on the north shore of the bridge as the base station, the rover stations shm2 is set in the middle span of the bridge, and shm4 is set at the top of the south tower. Two baselines can then be formed to carry out GNSS relative positioning resolution, which are baseline 1: shm1-shm2 for 1529 m, and baseline 2: shm1-shm4 for 1033 m. Detailed information of the two baselines is provided in Table 1. The receiver types adopted are all Leica GR10 receivers with the antenna of Leica Geosystem GR10 (LEIAR10). The satellite signal frequencies that are adopted here are GPS L1 and Galileo E1. Therefore, in this case, the ISBs between GPS L1 and Galileo E1 can be ignored according to Section 2.2.3. The GPS/Galileo tightly combined mode without inter-system biases will be applied in the baseline resolution. The monitoring data was collected from 1 May to 3 May 2018, with the sampling interval of 10 Hz, and the cutoff angle was set to 10\({}^{\circ}\) to reduce the impact of other reflection or refraction signals.
## 4 Discussion
### Remotely Sensed Data--High Performance and Ease of Extraction  
The past study confirmed that the nighttime light retrieved from the satellite could quantify electricity consumption at a provincial scale [1]. Moreover, we found that the lit area, sum of light intensity, and built-up area could also estimate the electricity consumption at the district level. These geographic variables played a considerable role in prediction models by providing high weight (see Table 3). For example, the built-up area was the second-highest weight (0.2) in the DT prediction model, and the area of lit was the highest weight (0.86) in the MR prediction model. However, the built-up area was more complex in terms of extraction methodology, and it was challenging to control the accuracy of data extraction results. Figure 6 illustrated that the built-up area is highly correlated to the area of lit and sum of light intensity. For this reason, the nighttime light intensity was a better proxy indicator to estimate the electricity consumption at the district level.
## 2 Improved AMCL Algorithm
### Improved Resampling to Avoid Kidnapping
#### 2.4.2 Simulation to Verify the Analysis of the Adjusted Dynamic Results  
Verify the effectiveness of KLD dynamically adjusting resampling particles to solve the kidnapping problem through the state estimation of nonlinear systems. The simulation uses a Windows 64 bit system and the simulation software is MATLAB 2021B.  
Simulation research is conducted on the state estimation problem of nonlinear systems, and the nonlinear system state equation used is as follows:  
\[\left\{\begin{aligned} x_{k}&=1+\sin(0.04\pi k)+0.5x_ {k-1}+v_{k-1}\\ y&=\left\{\begin{aligned} & 0.2x_{k}^{2}+n_{k}& k\leq 30\\ & 0.5x_{k}-2+n_{k}& k>30\end{aligned}\right.\end{aligned}\right. \tag{9}\]  
where the system noise is: \(v_{k}\sim Gamma(3,2)\), and the observation noise is: \(n_{k}\sim N(0,0.00001)\).  
The system noise is taken as gamma noise and the observation noise is Gaussian white noise. The initial azimuthal misalignment angle is \(10^{\circ}\); the initial horizontal alignment angle is \(1^{\circ}\); the accelerometer constant drift is \(3\times 10^{-5}\)g; the accelerometer random drift is \(1\times 10^{-5}\)g; the gyroscope constant drift is \(0.05^{\circ}/\)h; and the gyroscope random drift is \(0.01^{\circ}/\)h.  
From the simulation results in Figure 5a, it can be seen that the KLD and the improved KLD resampling methods conclude that only the improved KLD resampling has a more stable convergence speed and consistency, and can effectively solve the fluctuation problem of particle filtering. From Figure 5b, it can be seen that the alignment accuracy and convergence speed of the improved KLD resampling are significantly better than that of the unimproved method, and the improved simulation curve eliminates the fluctuation phenomenon present in other algorithms and has better stability.  
Figure 5: Simulation results: (**a**) comparison of mean square error results; (**b**) simulation results of heading angle error.  
Under the same simulation conditions, the KLD and the improved KLD methods are run for 1000 steps each, and the results of calculating the mean square deviation of the state estimation of the different methods and the running time consumed by running the simulation for 1000 steps are shown in Table 1.  
From Table 1, it can be concluded that the estimation accuracy of the improved KLD resampling method is better than that of the KLD method, and the computation time consumed is less than 1/2 of that of the KLD method, which evidently has a better real-time processing capability and is more suitable for real-time applications.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 5 Evaluation Result
### Effect of Message Delay Jitter
#### 5.1.2 Enhanced FTSP (E-Ftsp)
Figure 6 indicates that E-FTSP reduced the synchronization error significantly compared to FTSP. The maximum network synchronization error of the E-FTSP is only approximately \(20\,\mathrm{\SIUnitSymbolMicro s}\) and the maximum neighbor synchronization error is approximately \(10\,\mathrm{\SIUnitSymbolMicro s}\). These synchronization errors are equivalent to the results for FTSP and GTSP in a no-delay environment. However, it is noteworthy that E-FTSP exhibits the same convergence time as FTSP. In addition, E-FTSP exhibits a smaller synchronization error than GTSP under the same conditions. Figure 5: Average _estimatedDelay_ value of nodes at same hop distance to the reference node. Figure 6: Network errors and maximum neighbor errors of FTSP, GTSP, and E-FTSP in the presence of message delay jitter.
## 4 Discussion  
Analysis of the 20 studies included in this review demonstrates several key findings. Firstly, 3D-printed phantoms can produce similar morphology and attenuations to human thoracic tissues, on the premise that dedicated material and printing parameters are selected. This offers a promising avenue for precise, cost-effective alternatives to commercially available anthropomorphic phantoms. However, this review reveals that the field of 3D-printed thoracic phantoms is in its infancy, with most studies still focused on testing the feasibility of this approach through material experimentation to correlate with tissue-radiodensities, aiming to create radiation-equivalent phantoms [30; 34; 35; 37; 38; 39; 40; 41; 43; 44; 46; 48]. A few studies have progressed to application stages, having validated radiation equivalence [18; 45; 47; 50], and possible applications include using phantoms for quality assurance of medical imaging equipment, optimising imaging protocols, radiomics, and software validation, as well as complimenting anatomy education and as practice tools for surgical guidance. Additionally, most studies are single phantom experiments, warranting a broader research base and larger sample size of thoracic phantoms with similar designs tested on a range of patients. Furthermore, phantom results need to be verified against real patients before clinical implementation can be confidently pursued.
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## 3 Key Design of SRSS/ILMO  
### System Anti-Crash Mechanism Design  
Controller system crash is an inevitable problem in system reliability design. The particularity of the marine environment and the non-maintainability of an unattended system put forward higher requirements for the anti-crash performance of the seabed observation system. The anti-crash design of SRSS/ILMO adopts a two-stage anti-crash mechanism to respectively monitor the cold reset and hot reset of the system.  
The system's hot reset monitoring is a traditional watchdog design. When the program is disturbed and the program pointer jumps out of the active program area, the watchdog can effectively suppress the crash through hot reset. However, once the program enters an infinite loop containing the command of the feeding watchdog dog, the microcontroller will not be reset effectively. Therefore, SRSS/ILMO adopts a CPLD based cold reset anti-crash design. If the CPLD logic does not accept the normal monitoring signal, the control logic will perform cold reset processing; powering off the system for a certain period of time, and then re-powering the system. The two-stage anti-crash mechanism is shown in Figure 8.
## 5 Simulation Results and Water Tank Experiments
### Simulation Results
#### 5.1.2 Simulation of Joint Control of Heading and Surge Velocity  
In the heading control mode, the initial surge velocity and yaw angle are both 0. The surge velocity is set to 2 m/s, and the yaw angle is set to 60\({}^{\circ}\) at 10 s. Figure 11(a) shows the variation curves of surge velocity and thrust, while Figure 11(b) shows the variation curves of yaw angle and azimuth angle. The red and blue lines correspond to the absence of wind and wave interference and the presence of wind and wave interference, respectively.  
As shown in the figure, regardless of the presence or absence of wind and waves, the surge velocity and yaw angle can track the given values without steady-state errors or oscillations. The surge velocity performance indicators are the same as those described in Section 4.1. When there is no wind or wave interference, the yaw angle overshoot values are 0.8% and 1.1%, respectively, and the adjustment times are 11.8 s and 11.5 s. When starting to turn, the thrust steadily increases; when the turn is completed and direct navigation is resumed, the thrust returns to its original value. When turning, the azimuth angle quickly increases to 5\({}^{\circ}\) to change the yaw angle, with a smooth and small amplitude change.
## 4 Integration into IoT
### Data Handling Challenges
In this figure, vertical farming systems have a high level of automation and operate only under artificial illumination in a controlled environment. Data from the sensors of both systems goes to its control systems. As mentioned earlier, data exchange between biotechnology control systems and building control systems is much less frequent. Data from a city data cloud can be transferred both via a building control system and directly in a photobioreactor control system. It is necessary to mention that it should be data from the same source in the case of direct data transfer. Additionally, collecting all data concerning photobioreactor operation in a city could lead to the development of new deep learning based-systems to control those systems. Can synthetic biology influence data handling in a city's IoT with the biotechnology mentioned above? Living matter is able to provide effective data storage and operation inside the cell using DNA and transcription/translation mechanisms. There are already existing approaches to the storage of non-biological data in DNA [103; 104; 105; 106]. The ability to encapsulate DNA molecules in silica beads, which in turn can be put in polymeric systems for long-time storage, has already been shown [105]. Due to the long time and higher cost of DNA synthesis and the subsequent sequencing to extract the  
Figure 4: Data flows between photobioreactors, vertical farming systems, and building control systems. **(A)** City data transfer to a photobioreactor control system through a data exchange with a building control system; **(B)** data transfer directly to a photobioreactor control system; **(C)** data exchange between a photobioreactor control system and data clouds containing data from all photobioreactors in the city. data, this approach today can be applied for long-time storage goals, addressing this data no more than a couple of times per year. One application for such data storage can be steganography [105]. More interesting for future IoT applications is the automatization of digital data transfer to functional biologics starting from DNA [107]. Many synthetic biology applications widely use standardization and unification approaches in, e.g., engineering disciplines, i.e., those based on standard biological parts [108; 109], synthetic biology open language [63], and automated project development software such as CelloCad [69]. Such developments have led to engineering methods whereby biological systems are built based even on bacteria or yeast. This has led to the possibility of organizing the fast development and production of cell-based biosensors combining the biologics production and modern 3D-printing technologies mentioned above [110; 111]. Such development technologies require additional effective data analysis techniques, which will be discussed next.
## 5 Conclusions  
1. The study in this paper utilized the NMR technique to determine the changes in the microporous structure of mudstone during the freeze-thaw process. The results indicated that the average pore size of mudstone increases as the number of freeze-thaw cycles increases. The process mainly affects the expansion of tiny pores, followed by medium and large pores. Additionally, the study found that the percentage of pores within the range of 50-100 nm increased with the number of freeze-thaw cycles, with the maximum gain observed between 6-10 cycles.
2. The uniaxial compression test confirmed the variations in the mudstone's macroscopic mechanical properties. Both the uniaxial compressive strength and elastic modulus of the mudstone displayed a linear decrease as the number of freeze-thaw cycles increased.
3. The characteristics of mudstone damage changed with the increase in freeze-thaw cycles. The damaged sample gradually transformed from a shear mode with a single rupture surface to a double rupture surface with conjugate shear wear. Finally, it was transformed into tensile damage with multiple rupture surfaces.
4. The process of clay mineral hydration resulted in the expansion of interlayer and intergranular spaces, exerting pressure on the rock skeleton. This pressure caused the existing pores to widen and new pores to form. As a result, the average pore size of  
Figure 18: Schematic diagram of clay mineral structure.  
natural mudstone samples increased significantly more after freeze-thaw cycles than sealed samples.
* The primary focus of this paper was to investigate the freeze-thaw damage of mudstone using NMR technology. The damage characteristics and micromechanisms of mudstone under different recharge conditions in open-pit mines were investigated. Currently, only the freeze-thaw damage mechanism of mudstone has been studied, but other lithologies in open-pit mines will be examined in the future. Additionally, the study aimed to explore the impact of freeze-thaw damage on slope stability.  
Methodology, Y.T.; Formal analysis, X.Q., Y.T. and Z.Z.; Resources, R.L.; Data curation, X.L., R.L. and Z.Z.; Writing--original draft, X.L., X.Q. and L.T.; Writing--review & editing, X.L. and Y.T.; Visualization, X.Q., R.L. and Z.W.; Funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (52204159) and the Natural Science Foundation of Jiangsu Province, China (BK20221125).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
some constraints (horizontal and vertical constraints) are needed to impose onto the tomography modeling [11; 12; 13; 14]. [15] proposed a tomography method using the adaptive smoothing and ground meteorological observations, which improves the tomographic result in the lower troposphere.  
With the increasing improvement of multi-GNSS, especially for BDS (BeiDou navigation satellite system), GLONASS satellite system and Galileo, more satellite rays can be used for establishing the tomography observation modeling, which is favorable for the multi-GNSS troposphere tomography. [16] have analyzed the accuracy of tomography results using the simulated data from three satellite systems (GPS, GLONASS and Galileo) and verified its improvement over that of the single GPS system. [17] also obtained similar results using the simulated data from BDS and GPS system. [18] found that, in some epochs, the additional Galileo observations do not have a positive impact in the tomography solution based on the simulated GPS and Galileo data. [19] obtained the three-dimensional water vapor in Wuhan using GPS, BDS and GLONASS observations with the Multiplicative Algebraic Reconstruction Technique (MART) method. However, two points need to be noted that: (1) only the simulated data was used to perform the multi-GNSS tropospheric tomography; and (2) the weightings for different satellite systems as well as the various constraints were not considered before. Therefore, an optimal tropospheric tomography method based on the Multi-GNSS Observations is proposed, while considering the weightings of different input information for establishing the tomography modeling. In this paper, the observed data from multi-GNSS (GPS, GLONASS and BDS) was used to establish the observation equation, and the horizontal, vertical and prior constraints were also introduced. In addition, the improved Bartlett test was used to determine the optimal weightings of different input information of tomography modeling by adjusting their posterior unit weight variances.
# Compact Sub 6 GHz Slot Multiantenna System for 5G Laptops
## 6 MIMO Antenna System Characteristics and Advantages Analysis  
To further investigate the degree of isolation of the six-antenna system, the electronic field distribution of the six-antenna system was also analyzed. The simulated electronic field distributions of the six-antenna system at 3450 MHz and 4900 MHz are shown in Figures 18 and 19, respectively. It can be clearly seen in the diagrams that when the six-antenna system was operated at 3450 MHz and 4900 MHz, no matter which antenna was excited, the other antennas did not exhibit a significantly excited electronic field distribution. The same situation occurred at 4900 MHz. This echoes the good degree of isolation and ECC performance of this six-antenna system, which may be associated with the structural design of these 5G antennas.  
Figure 19: Simulated electronic field distribution of the six-antenna system at 4900 MHz.  
Figure 17: Comparison of simulated reflection coefficients with changes in parameter gap.  
Figure 18: Simulated electronic field distribution of the six-antenna system at 3450 MHz.  
To highlight the advantages of our proposed MIMO antennas, we provide a comparison of MIMO antennas reported in the literature in Table 1. The table presents a comparison of the dimensions, spacing between two-antenna units (in terms of the wavelength \(\lambda\) at the center frequency for the first resonant mode [35]), isolation, and various radiation performance for 5G sub 6 GHz dual-band frequencies. The table shows that our proposed MIMO antennas are more compact than those reported in the literature with dual-band frequencies.