esa-sceva/satcom-chunk-collection huggingface dataset:
# A Message Passing-Assisted Iterative Noise Cancellation Method for Clipped OTFS-BFDM Systems
## 4 Computational Complexity  
In [26], the MP-A method only needs one \(N\)-IFFT/FFT and two \(M\)-IFFT/FFT transforms, as well as one MP operation. However, in the proposed MP-AIC scheme, each iteration requires the same complexity as the MP-A method. Beyond that, with the in crease in iterations, the computational complexity of the MP-AIC method will increase substantially. Fortunately, the simulation results in the next section will show that two iterations can ensure the BER close to convergence, which is beneficial to the application of the MP-AIC method.  
The complexity of MP-AIC mainly exists in the message passing between observation nodes and variable nodes [7]. Moreover, an \(N\)-point IFFT requires \(N\text{log}_{2}N\) multiplications and \(\frac{1}{2}N\text{log}_{2}N\) additions. Assume that \(\mathbf{H}_{eq}\) has only \(S\) non-zero elements in each row and column, the size of modulation alphabet is \(Q\) and the number of iterations of MP algorithm is \(\tau_{\text{max}}\). To update variable nodes from observation nodes, \(LSQ^{S}(2S+3)\) multiplications and \(LS^{2}(Q^{S}-Q)\) additions are involved, while to update variable nodes from observation nodes, only \(LSQ(S-2)\) multiplications are need. Since the maximum number of iterations of the MP-AIC method is \(N_{iter}\), the total computational complexity can be calculated as:  
\[N_{mul}=\tau_{\text{max}}N\text{log}_{2}N+2\tau_{\text{max}}M\text{log}_{2}M+ \tau_{\text{max}}(N_{iter}+1)(LSQ^{S}(2S+3)+LSQ(S-2)), \tag{38}\]  
\[N_{add}=\frac{\tau_{\text{max}}}{2}N\text{log}_{2}N+\tau_{\text{max}}M\text{ log}_{2}M+\tau_{\text{max}}(N_{iter}+1)(LS^{2}(Q^{S}-Q)), \tag{39}\]  
where \(N_{mul}\) and \(N_{add}\) are the total complexity of multiplication and addition, respectively.
# Compensation of Measurement Uncertainty in a Remote Fetal Monitor
The aim of this work is to obtain an integral maternal-fetal watch program, integrated by a technological platform that allows the timely detection of perturbations in baby heart rate, that enable us to remotely send information to an obstetric monitoring center for timely and reliable diagnosis of the patient. When modeling a process, the most complicated component of the model is represented in probabilistic terms, and the hypotheses taken as valid in many situations are not met, and the results are not valid. This work presents a proposal of how, using the existing theory, with a deterministic model and the Monte-Carlo method [12], it is possible to apply probabilistic models that are genuinely representative of the application, and that characterize the statistics' properties used. In a specific application, the statistical properties of interest are generally mean, variance, extreme values, dependence on sample size, bias, etc. [13; 14; 15; 16; 17]. In general, only the mean and variance parameters are considered to obtain a probabilistic model because they characterize the uniform distributions that represent the worst-case scenario (total ignorance of the model) and the Gaussian distributions considered as typical or normal. However, to estimate these parameters, it is necessary to carry out a sampling to obtain more information about the system model. This additional information is not used in practice. This work proposes a new approach to include experimental data and to generate a representative probabilistic model without considering difficult suppositions. The suggested procedure is applied to a fetal monitor to correct the standard deviation estimation considering the noise type that occurs during the regular operation of the device.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 3 Experimental Setup and HEM Algorithm
### Hardware  
Four home appliances were selected for the experimental demonstration of the proposed HEM system. Table 1 details the appliances and their specifications. Each of the appliances in Table 1 was attached to a smart socket to monitor their energy consumption and continuously transmit the information to the main controller in the scheduling terminal. The temperatures of the first three appliances were also monitored by the appropriate sensing modules that were integrated with the condition monitoring circuit, as described above.
## 2 Materials and Methods
### Individual Application Method according to User Attachment of the Fabricated Sensor
#### 2.2.4 Detection of the User's Intention according to Movements of the Lower Limb  
Section 2.2 describes the sensor selection process according to the individual pressure range inside the socket and the process of cutting the outer part of the sensor according to the flexion of the attachment area. After attaching the sensor fabricated through this process to four target locations inside the socket, the goal is to detect the intention to change the control method according to discontinuous movements of the lower limb. The lower limb movements to be detected in this study are six basic movements that occur frequently in daily life: standing up, locomotion, standing to sitting, sitting to standing, stair climbing, and stair descending.  
Since the six movements have no similarity in their locomotion sequences, it is possible to classify them according to their changing patterns based on the initial pressure presented in the standing position [26]. The six suggested movements are classified as pressure changes occurring at the start of the transition to each posture. Among them, in the case of stair locomotion, the user requires power before performing the movement. Thus, the knee joint bending movement, similar to stair climbing, and the weight application posture, similar to stair descending, are defined and detected in advance. Figure 5 shows the prediction of the pressure change pattern in the socket that will occur during the movement of each lower limb.  
Figure 4: Sensor attachment positions and applied pressing force inside the socket: (**a**) Sensor attachment positions inside the socket; (**b**) internal pressing force inside the socket in standing position; (**c**) internal pressing force in the position of lifting the lower limb.  
Figure 6 represents a flowchart illustrating the process of detecting pressure changes according to each movement and classifying the pressure changes into intention signals. The proposed pressure sensor system receives pressure changes from four locations inside the socket during the movement of the lower limb. The input changes are converted into a ratio form based on the initial pressure. The converted pressure change rates at the four locations of the sensor are classified into six movement intentions through a multi-critical scheme. The suggested movement intention determination method will enable the simple and intuitive detection of the movement intention.
# A Novel Alternating \(\mu\)-Law Companding Algorithm for PAPR Reduction in OFDM Systems
## 6 Conclusions  
The peak-to-average power ratio (PAPR) issue within multiple-carrier technology is still troublesome for advanced wireless systems. Although many solutions have been proposed, each method has different pros and cons. Of course, no single approach will handle such tricky needs comprehensively. Among them, nonlinear companding transform (NCT) is one of the simple and popular technologies, especially the \(\mu\)-law rule. In this paper, we utilize the balance between BER and PAPR, adopt the Monte Carlo method, and define the ratio of improved BER to improved PAPR, \(\eta\), to estimate the appropriate \(\mu\) value more accurately. Thus, the SULC scheme can enhance the PAPR and not damage the BER. Moreover, the AULC scheme is proposed to improve complexity more efficiently. It sorts the transmission signals according to their amplitudes. Then, it estimates the rough companding tone amount (around 54% of the subcarriers) to separate the tones of the OFDM signal into two groups. The hybrid combination of the original \(\mu\)-law's parallel companding behavior and the partial sequential \(\mu\)-law's component-wise companding can reduce complexity compared to the SULC method. Simulation results and numerical analysis demonstrate that our proposed AULC scheme outperforms previous companding techniques regarding both PAPR and BER.  
Conceptualization, Y.-P.T., Z.-T.Z. and Y.-F.H.; methodology, Y.-P.T.; software, Z.-T.Z.; validation, Y.-P.T.; formal analysis, Y.-P.T.; investigation, Y.-P.T. and Y.-F.H.; resources, Y.-P.T.; data curation, Y.-P.T. and Z.-T.Z.; writing--original draft preparation, Y.-P.T. and Z.-T.Z.; writing--review and editing, Y.-P.T. and Y.-F.H.; visualization, Y.-P.T. and Z.-T.Z.; supervision, Y.-P.T.; project administration, Y.-P.T.; funding acquisition, Y.-F.H. and Y.-P.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council (NSTC), Taiwan, with grant number NSTC 112-2221-E-324-010.  
All data underlying the results are available as part of the article, and no additional source data are required.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & fifth-generation \\ A/D & analog-to-digital \\ AULC & alternating \(\mu\)-law companding \\ AWGN & additive white Gaussian noise \\ B5G & beyond fifth-generation \\ BER & bit error rate \\ CCDF & complementary cumulative distribution function \\ CMAs & complex multiplications and additions \\ D/A & digital-to-analog \\ DCT & discrete cosine transform \\ FFT & fast Fourier transform \\ HPA & high-power amplifier \\ IFFT & inverse fast Fourier transform \\ i.i.d. & independent and identically distributed \\ IoT & Internet of Things \\ ISI & inter-symbol interference \\ ITM & improved two-\(\upmu\)s \\ NCT & nonlinear companding transform \\ OFDM & orthogonal frequency division multiplexing \\ PAPR & peak-to-average power ratio \\ PRT & peak reduction tones \\ \end{tabular}
## 2 Methodology
### Grid Independence Verification  
The analysis model is selected by two methods, showing the numerical accuracy by the element quantity and the analysis time for the element quantity. An appropriate model was selected in consideration of analysis time and accuracy. Table 3 shows five models with different numbers of grids. Case 6 is a model that represents the number of grids used in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Material** & **Density** & **Specific Heat** & **Conductivity** \\  & **(kg/m\({}^{3}\))** & **(J/kg)** & **(W/m)** \\ \hline \multirow{3}{*}{Mold} & Aluminum & 2688.7 & 898.7 & 236.72 \\  & GFRP & 1870 & 1246.08 & 0.7166 \({}^{*}\) \\ \cline{1-1}  & & & & 0.4473 \({}^{**}\) \\ \cline{1-1} \cline{2-5}  
this study. Case 6, which has the highest maximum temperature, requires more verification than other cases because the temperature deviation is large. Since the aluminum mold has more active heat transfer than the GFRP mold, case 6 using the aluminum mold is suitable as a grid independence verification model. All boundary conditions and physical properties were the same and only the number of grids was different to make cases A, B, C, and D. Case D is the most accurate grid model because it has the largest number of grids. Comparing the temperature difference between case D and the other cases, the accuracy of the model with a few number of grids is compared.  
In Table 3, the spar-cap top surface temperatures at the same coordinates in each case were compared. In Figure 5a, in all cases except case A with the smallest number of grids, the spar-cap top surface temperature deviation for case D with the largest number of grids is less than 1 \({}^{\circ}\)C. This shows that verifying grid independence is a pointlessly small temperature deviation. The reason why temperature deviations below 1 \({}^{\circ}\)C are meaningless is that even temperature control in 1 \({}^{\circ}\)C increments is extremely difficult in actual manufacturing conditions. Since the control unit of a general temperature heating device is 1 \({}^{\circ}\)C, it is determined that all cases except case A are within the error range. Case D takes about 4 days to simulate, and it takes too long to simulate all cases depending on the mold type and heat panel temperature. On the other hand, Case 6 requires an acceptable analysis time of about one day. Therefore, in consideration of the analysis time, Case 6, having fewer grids than Case D, was selected as the grid model of this study.
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
## 3 Results Analysis
### Polarization and Oblique Incident Angle Sensitivity of the MA  
The proposed absorber's adaptive capacity for various polarization angles was analyzed for different polarization incident angles of EM waves. Figure 11a compares absorption for different polarization angles where \(\varphi\) is the polarization angle. In TE Mode, the incident wave and the magnetic field vector propagates in the direction of the z-axis. The magnetic and the electric field vector are along the y and x-axis. The MA is polarization insensitive, as the MA exhibits no significant variation in the absorption properties for polarization angles (\(\varphi\)) up to \(90^{\circ}\). In Figure 11b, a comparison of absorption characteristics for different oblique incident angles has been presented. The oblique incident angle is given by \(\theta\), which is along the z-axis of the MA structure. From the figure, it can be seen that the absorption characteristics of the MA were found stable up to \(\theta=45^{\circ}\) oblique incident angle as the average absorption is above 80%. The MA attained polarization insensitivity primarily because of its symmetrical resonators. Another reason the MA achieves significant incidence angle insensitivity is the high critical angle of reflection of SiO\({}_{2}\). The high critical angle of reflection of SiO\({}_{2}\) is \(55^{\circ}\) in the infrared optical spectrum [31]. As a result, the MA shows high absorption, almost up to \(45^{\circ}\). As the incident angle increases more than \(55^{\circ}\), absorption decreases rapidly. Moreover, the compact MA structure achieved the oblique incident angle stability due to the lower phase shift at the oblique incident angle. In Figure 11c at MAs array structure, for the oblique incident angle \(\theta\), the face shift \(\varphi\) proportionally depends on the periodicity D of the unit cell. This phase difference caused the MAs impedance mismatch with free space, reducing the absorption at a higher \(\theta\) value larger than \(45^{\circ}\).  
Figure 11: (**a**) The polarization insensitivity. (**b**) The oblique incident angle insensitivity (**c**) phase shift at the oblique incident angle.
# Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model
## 6 Conclusions  
For the sake of achieving accurate and efficient testing and identifying the mission of plant diseases and pests, this paper proposes an improvement method based on the YOLOv8 model. The superiority of the proposed method is verified by the analysis of experimental data. The main research contents of this paper are as follows.  
1. In the Backbone network, after the SPPF module and before the output features, the GAM is adopted to weigh the important feature information, thereby improving the accuracy of the model.
2. In the mixed feature part of the Neck, the RFA Conv method is used instead of standard convolution operations to enhance the processing ability of feature information, reduce computational complexity and costs, and improve network performance.
3. In terms of data calculation, the improved model is concentrated in rice datasets and cotton data. The mAP values reach 71.27% and 82.91%. In the ablation and comparison experiments, compared to other models, it shows lower missed detection rates, higher target matching rates, and higher detection accuracy.  
Therefore, the application of the improved model proposed in this paper to plant pests and diseases is of great significance for promoting high-quality agricultural development.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Rice**} & \multicolumn{3}{c}{**Cotton**} \\ \cline{2-7}  & **mAP** & **F1** & **FPS** & **mAP** & **F1** & **FPS** \\ \hline Faster & 49.33\% & 0.45 & 22.78 & 76.88\% & 0.65 & 23.58 \\ R-CNN & 61.80\% & 0.49 & 57.20 & 78.36\% & 0.66 & 57.89 \\ \hline YOLOv8 & 66.47\% & 0.47 & 57.18 & 79.56\% & 0.71 & 57.82 \\ \hline Ours & 71.27\% & 0.53 & 55.57 & 82.91\% & 0.78 & 55.65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results of different models.  
**Author Contributions**: Methodology, Y.W.; Validation, C.Y.; Formal analysis, T.H.; Data curation, J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Natural Science Foundation of China (52205537), Hubei Province Key Research and Development Plan (2021 BAA194), and Guangxi Key Research and Development Plan (Guike AB21075009).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: These data were derived from the following resources available in the public domain: [[https://www.kaggle.com/](https://www.kaggle.com/)]([https://www.kaggle.com/](https://www.kaggle.com/)) (accessed on 1 May 2024).  
**Conflicts of Interest**: The authors declare no conflicts of interest.
# MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template
## 4 Experiments
### Experiments with Actual Workpieces
#### 4.3.2 Analysis of Registration Accuracy of Multiple Partial Point Clouds  
Taking the egg-shaped pressure hull 'a' as an example, use MPCR-Net to register all the partial point clouds to the global template point cloud and the ICP algorithm to optimize the registration results. Then generate the full registered point cloud. Figure 18 shows the full registration point cloud (Figure 18a) and its surface reconstruction model (Figure 18b) of hull 'a'.  
The above method is used to generate surface reconstruction models of all egg-shaped pressure hulls a-i, and the registration accuracies can be indicated by detecting the surface contour deviation between surface reconstruction models and actual digital models. The contour deviation cloud maps of hulls a-i are shown in Figure 19; the smaller the deviation, the higher the registration accuracy.  
Figure 17: Scanning scene photo of the hull ‘a’ and generate the actual digital model: (**a**) Scanning scene; (**b**) Actual digital model.  
Figure 18: The final 3D reconstruction results: (**a**) Full registered point cloud; (**b**) Surface reconscheme 19. Cloud maps of contour deviation of hulls a–i calculated by MPCR-Net.  
The maximum positive deviation, maximum negative deviation, and the area ratio exceeding the distance tolerance \(\pm 0.5 mm\) of the surface contour between the surface reconstruction model and the actual digital model are denoted as indicators A, B, and C, respectively.  
MPCR-Net, PRNet, and RPM-Net algorithms were used to perform surface reconstruction experiments on all egg-shaped pressure hulls a-i, and indicators A, B, and C were used to evaluate the reconstruction accuracy of three algorithms. The results are presented in Figure 20.  
As shown in Figure 20, the smaller the values corresponding to indicators A, B, and C are, the higher the registration accuracy is. Each bar represents the average registration accuracy of each algorithm under different indicators for hulls a-i, and each error bar indicates the distribution range of the indicator on hulls a-i.  
For indicator A, the average maximum positive deviation of the MPCR-Net was 17.6% smaller than that of PRNet and 27.1% smaller than that of RPM-Net. For indicator B, the average maximum negative deviation of MPCR-Net is 18.2% smaller than that of PRNet and 28.0% smaller than that of RPM-Net. For indicator C, the area ratio exceeding the  
Figure 19: Cloud maps of contour deviation of hulls a–i calculated by MPCR-Net.  
Figure 20: Registration accuracy of MPCR-Net, PRNet, and RPM-Net on hulls a–i.  
distance tolerance of MPCR-Net is 32.1% lower than that of PRNet and 42.4% lower than that of RPM-Net. In summary, the performance of MPCR-Net for the accuracy indicators A, B, and C is better than that of PRNet and RPM-Net, indicating that MPCR-Net can provide higher registration accuracy for actual workpieces.
# Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
## 5 Optimization Effect Analysis  
The optimized reconstruction algorithm is used to analyze the temperature field, and the influence of the number and arrangement of sensors on the reconstruction accuracy is explored. The number of measuring points is determined to be 13, and the optimal arrangement of measuring points is calculated. The core tensor with dimensions of \(9\times 4\times 5\times 13\) is applied to build the temperature field of five test cycles, and the overall optimization effect is analyzed. The reconstructed results of the five test conditions after optimization are as follows.  
From Figure 10, we can see that the optimized Tucker decomposition algorithm can improve the accuracy of the temperature field reconstruction. The five test conditions are increased by 4.2%, 2.7%, 6.1%, 5.9%, and 6%, respectively.  
Test case 5 has the largest average reconstruction error; therefore, 10 measuring points are randomly selected to compare the reconstruction temperature value with the actual temperature value, as shown in Figure 11.  
Figure 11: Comparison between Reconstructed Temperature and Actual Temperature of 10 points.  
Figure 10: Optimized Effects of Reconstruction Algorithm.  
As can be seen from Figure 11, the temperature value reconstructed by the optimization algorithm is basically consistent with the actual temperature value. The error value of the measuring point position 10 is the largest, and the maximum error is 44 K.  
To make the application scenarios of the algorithm closer to the actual situation and test the effect of the algorithm in the presence of measurement errors, Gaussian random noise is added. The concept of Signal Noise Ratio (SNR) in the signal processing is introduced to characterize the noise intensity. The temperature measurement signal-to-noise ratio calculation formula used in this paper is as follows.  
\[\text{SNR}=10\ln\left(\frac{T}{T_{cr}}\right), \tag{12}\]  
where SNR is signal-to-noise(dB); \(T\) is actual temperature; \(T_{cr}\) is temperature measurement error.  
Add noise to the temperature measurement (SNR = 20 dB,30 dB), and for the purpose of for reducing the impact of noise randomness on the reconstruction results, a more general conclusion is obtained. The unoptimized algorithm and the layout of the measuring points calculated based on the optimal layout algorithm are used to reconstruct 1000 times, respectively. The average value of the reconstruction error is obtained as the performance evaluation index of the final algorithm. The comparison results of test conditions 1-5 are shown in Figure 12.  
As shown in Figures 10-12, after optimizing, the accuracy of the reconstruction is improved obviously. Additionally, in the presence of measurement errors, the optimization effect maintains good consistency.