esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Evaluation and Validation
The F1-score and G-index were calculated similar to our recently published study [56]. The main drawback of the train-test split method is that the performance estimates can vary widely because they depend on a single random split, indicating that different splits can result in widely varying performance. Another drawback is that if the random split is not representative of the distribution, it can lead to model overfitting or underfitting. Thus, we implemented cross-validation technique for better verification of our results. This involved partitioning the data into multiple subsets, training the model on subsets, and validating the remaining subset. For instance, in the k-fold cross-validation method, the model is trained on (k - 1) subsets and validated on the remaining subset. This process is repeated k times, each time with a different fold as the validation set. The results are averaged to produce a single performance estimate. Performance metrics are then averaged across multiple folds, which provides a more reliable and stable estimate of a model's performance compared to a single train-test split. This study implemented 5-fold cross-validation. Each model was tested using three methods, first on the testing dataset, then applying SMOTE (Synthetic Minority Over-sampling Technique) on the training dataset to balance the number of observations across fatigue levels. Finally, feature importances were computed for high-performing scenarios to identify the contributions of specific features to the overall model performance.
# Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN
## 4 Fault Diagnosis of Planetary Gear Train Crack Based on DC-DRSN
### Crack Failure Test of Planetary Gear Train  
In this study, the fault tests of planetary gear train cracks under different working conditions are carried out. During the test, the control system controls the input motor speed, when the input motor speed is 750 r/min, the voltage of the electronic control system is 380 V, and the frequency is 50 Hz. Under different loads, the operating state and vibration signal characteristics of the planetary gear train are different, and the vibration amplitude and frequency components of the gear train meshing under high loads are more complex. In order to avoid aliasing the characteristic signals under different loads and improve the applicability and robustness of the diagnostic system, two load conditions of 50% medium load and 100% heavy load are set by the load motor. A series of tests are carried out for normal, planetary gear cracks and sun gear cracks under each working condition, and the test environment is shown in Figure 10. The crack is located at the root of the gear, and the crack depth is about 1 mm, as shown in Figure 11. In the process of data acquisition, the sampling frequency is 25.6 kHz, and the continuous sampling time is 1 min each time. The vibration data samples in five states of the planetary gear train are finally obtained, which are labeled as 0, 1, 2, 3, and 4, respectively.  
Figure 10: Test environment of planetary gear train crack.  
Figure 9: Scaled-down nuclear planetary gearbox test bench. 1. Input motor; 2. Planetary gearboxes; 3. Power distribution pump; 4. Lubrication system oil pumps; 5. Lubrication system motors.  
Figure 12 shows the original vibration signal of a planetary gear system randomly selected for five states and their image (112 \(\times\) 112 pixels) after STFT. Table 6 shows the composition of the planetary gear train crack fault dataset obtained in this paper. A sliding window containing 1024 data points is used to sample the vibration signals of the planetary gear train in five states, with a window step size of 300, and each state contains 900 samples. Similarly, the training, validation, and test sets are randomly divided in the ratio of 3:1:1, and the final dataset contains 2700 training set samples, 900 validation sets, and 900 test set samples each. The structure of the training set, validation set, and test set of the upper and lower channels, as well as the data preprocessing steps and hyperparameter settings, are consistent with the preceding ones, and will not be repeated here.  
Figure 11: Faulty gear.  
Figure 12: The original vibration signals and STFT transformed images for different labels.
## 2 System Model
### Modelling of RL-Based Clustering
Thus, consumed energy is given as:\[E_{TC,j}(B_{dt},d)=\ E_{MN}^{cs}+\ E_{tM} \tag{43}\] \[=\sum_{m=1}^{n_{x}}(T_{cs}P_{ed}+E_{sp})+B\big{(}3E_{ec}+2E_{am}\ a_{i, j}^{\beta}\big{)}\]  
Therefore, a reward of one will be obtained for minimum energy consumption, a reward of half will be awarded for energy consumption less than the maximum consumed energy, while the reward for maximum energy consumed is zero. Thus, the reward for energy consumption is given as:  
\[rw_{E,k+1}^{t}=\left\{ \begin{array}{l}1,E_{TC,j}=\min_{E_{TC,j}\in E_{TC}^{ set}}(E_{TC}^{set})\\ \\ 1/_{2},\ E_{TC,j}<\max_{E_{TC,j}\in E_{TC}^{set}}(E_{TC}^{set})\\ \\ 0,E_{TC,j}=\max_{E_{TC,j}\in E_{TC}^{set}}(E_{TC}^{set})\end{array} \right. \tag{44}\]  
State-action that leads to selection of a clusterhead that leads to low energy consumption receives a higher reward which translates into an increase in the corresponding Q-value and also its chances for likely selection in subsequent states. This means that less favorable clusters that lead to high energy consumptions are more likely to be excluded during the learning process. Local decision on PU existence in a channel is usually prone to errors due to the channel's propagation impairment which degrades the channel sensing performance. Therefore, the local decision accuracy determines the divergence of an individual member node's sensing outcome compared to the cooperative sensing outcome. The reward for local decision accuracy can be obtained by comparing the local decision \(LD_{t}^{2}\in LD_{i}\) made by member node \(MN_{i}\) on PU existence on the channels with the cooperative decisions \(CD_{j}^{z}\in CD_{j}\) which involves other member nodes MNs in the cluster \(cl_{j}\). Therefore, when a set of local decisions \(LD_{i}\) matches with a corresponding set of cooperative decisions \(CD_{j}\), then a reward of one will be received, while local decisions \(LD_{i}\) that agree with the majority of the corresponding cooperative decisions \(CD_{j}\) receive half rewards, local decisions \(LD_{i}\) that match the corresponding cooperative decisions \(CD_{j}\)with less than half the number of the channels attract a reward of zero (_i.e._, do not earn a reward). Thus, the reward for local decision accuracy can be expressed as:  
\[rw_{D,k+1}^{t}=\left\{ \begin{array}{l}1,LD_{i}\cap CD_{j}=\ n_{z}\\ \\ 1/_{2},LD_{i}\cap CD_{j}\geq(n_{z}/2)\\ \\ 0,LD_{i}\cap CD_{j}<(n_{z}/2)\end{array} \right. \tag{45}\]
## 3 Results and Discussion
### Biological Characterization of the Scaffolds
#### 3.7.3 ALP Activity of Pre-Osteoblastic Cells on Scaffolds  
The ALP activity results indicate similar values for all scaffold compositions, without significant changes between days 3 and 7 (Figure 9). On day 7, higher values were observed for the bioglass-containing scaffolds than for Alg/MC/TMC. The statistical analysis of the normalized ALP activity values indicated non-significant differences among the different scaffold compositions at each time point, suggesting their capacity to support osteogenic responses.
## 3 Experiment
### Experimental Results
#### 3.2.1 ROC Curves  
The formulas for calculating the true positive rate and false positive rate are as follows:  
TPR = \[\frac{\text{TP}}{\text{TP}+\text{FN}}\] (11)  
FPR = \[\frac{\text{FP}}{\text{FP}+\text{TN}}\] (12)  
where TP, FN, FP, TN represent the counts of true positives, false positives, false negatives, and true negatives, respectively. The proximity of the ROC curve to the top-left corner indicates higher accuracy in camera source identification, reflecting the superior quality of the extracted PRNU fingerprint and an improved performance in source camera identification.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** &
\begin{tabular}{c} **Image** \\ **Resolution** \\ \end{tabular} & **xDnCNN** & **BM3D** & **Wavelet** & **MPRNet** & **DIDN** & **InvDN** \\ \hline \multirow{3}{*}{DAXING} & 128 \(\times\) 128 & 0.3365 & 0.3660 & 0.4448 & 0.4428 & 0.4448 & 0.4558 \\ \cline{2-7}  & 256 \(\times\) 256 & 0.5462 & 0.5736 & 0.6363 & 0.6352 & 0.6363 & 0.6688 \\ \cline{2-7}  & 512 \(\times\) 512 & 0.7373 & 0.7603 & 0.7909 & 0.8078 & 0.7909 & 0.8197 \\ \hline \multirow{3}{*}{DRESDEN} & 128 \(\times\) 128 & 0.2421 & 0.2847 & 0.3411 & 0.3948 & 0.4161 & 0.4182 \\ \cline{2-7}  & 256 \(\times\) 256 & 0.4826 & 0.5241 & 0.6037 & 0.6392 & 0.6455 & 0.6349 \\ \cline{1-1} \cline{2-7}  & 512 \(\times\) 512 & 0.6890 & 0.7558 & 0.7826 & 0.8032 & 0.8012 & 0.7949 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Kappa values of six algorithms on two datasets.  
Figure 4: Comparison of experimental results on the DRESDEN dataset.  
Figure 3: Comparison of experimental results on the DAXING dataset.  
The performance of PRNU fingerprint extraction using the InvDN is significantly superior to that of DIDN, MPRNet, Wavelet, BM3D, and xDrCNN for images with sizes 128 \(\times\) 128, 256 \(\times\) 256, and 512 \(\times\) 512 in the DAXING dataset, as demonstrated in Figure 3. In Figure 4, it can be observed that the InvDN outperforms the compared algorithms in PRNU fingerprint extraction for images with size 128 \(\times\) 128 in the DRESDEN dataset. For images with sizes 256 \(\times\) 256 and 512 \(\times\) 512, although the InvDN's performance is slightly lower than that of the DIDN and MPRNet networks, it still surpasses traditional Wavelet, BM3D, and xDrCNN algorithms. Furthermore, when comparing results across three different image sizes, the InvDN's ROC curves exhibit greater separation from those of the compared algorithms, particularly for lower-resolution images, indicating a more pronounced advantage.
## 4 Results Analysis  
### Numerical Model Validation  
#### 4.1.1 Comparison of Performance Curves  
The tested and calculated performance curves of the centrifugal pump were shown in Figure 11, where the unsteady calculation results were the mean values gathered from the unsteady CFD calculation for the last impeller revolution.  
In general, the predicted performance curves agree well with test results, especially in the nearby range of \(Q_{des}\). For the flow of \(Q_{des}\), the maximum errors between calculated head coefficients \(C_{h}\) and its test value are less than 0.036, with a relative deviation of about 3.3%, and the absolute difference between calculated efficiency \(\eta\) and its test value is controlled within 0.5%. When the flow moves far away from \(Q_{des}\), the error between calculated value and test value increases with the increase of flow offset, especially in the part-load conditions. However, the maximum relative errors of \(C_{h}\) and \(\eta\) are only about 4.6% and 3.9% (around \(Q_{0.6}\)), respectively.  
The computational errors are mainly attributed to the following two aspects. Firstly, both leakage and mechanical loss were ignored in the CFD model. Then, errors may come from the discretion and solving processes due to the complicated flow behaviors in centrifugal pump. Specifically, the solving accuracy of the SST \(k\)-\(\omega\) turbulence model declines in part-load conditions, thus resulting in the higher  
Figure 11: Comparisons of the performance curves.  
Figure 10: Picture of the mounted vibration acceleration sensor.  
solving error. Additionally, it can be seen from Figure 11 that the calculation curves of steady-state CFD analysis and unsteady CFD analysis differ to some extent, and their differences are much more significant under part-load conditions. Generally, the unsteady CFD results provide higher prediction accuracy. A possible explanation is that the inner flow is more unstable under part-load condition, and the unsteady CFD model with sliding meshes technique can reflect the RSI better than the steady CFD model with MRF setting for the impeller grids.
## 4 Results and Discussion  
### Joule Heat  
#### 4.1.1 The Effect of Different Insulators on Joule Thermal Errors  
Figure 5 shows the temperature change curve of Pt thin film for sensors with different insulators when the initial temperatures are 228.15 K and 238.15 K, respectively. We prepared Sensors 1-6, using Insulators 1-6 (as depicted in Figure 3) as the insulator structures for each sensor, respectively. Accordingly, the temperature corresponding to the response time is 231.31 K and 234.99 K, respectively. It can be seen in the figure that all curves change sharply at first and then slowly, and the final temperature approaches a steady state, which is higher than the ambient temperature. The final temperature decreases as the sensor number increases and the response time increases.  
Figure 5: Temperature variation curves of Pt thin film for sensors with different insulators at initial temperatures of **(a)** 228.15 K and **(b)** 238.15 K.  
The reason for the above phenomenon is that the Joule heat causes the final temperature of the Pt thin film to be greater than the ambient temperature. As can be seen in the graph, temperature Sensor 5 has the fastest temperature change of the Pt thin film, followed by Sensor 4, Sensor 3, Sensor 2, and Sensor 1. It can be seen in Equations (1) and (6) that the heat dissipated by the Pt film is proportional to the contact area. Additionally, the heat exchange between the Pt thin film and the silica is faster than that with the air. The large surface area of the silica makes it more fully exchange heat with the air, so the larger the area of the silica, the faster its temperature response.  
However, Sensor 6 is the slowest. This is because there is no insulating notch on the silica layer, the heat capacity of the whole sensor is large, and the temperature of the Pt film changes very slowly. For balance, the fifth temperature sensor was chosen to shorten the temperature sensor's response time and improve the sensor's measurement accuracy. As shown, under the influence of Joule heat, Sensor 5 had a response time of 20 ms during warming and 99 ms during cooling.
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
## 4 Verification of the Identification Model and Compensation  
After the estimation of all the unknown parameters contained in the transfer function of the TIADC system is completed, the results of system identification are verified in this section. Through the electronic test of the four-channel TIADC data acquisition system, the accuracy of the parameter estimation algorithm was verified.  
The test standard for ADC is IEEE 1241-2010 [27]. According to this standard, the sine wave can be used for testing, mainly because the sine wave frequency is single, and spectrum analysis can intuitively observe the characteristics of the system. The digital signal  
Figure 7: General structure block diagram of a 16-bit, four-channel TIADC data acquisition system.  
converted by the ADC system can be used to calculate its static performance parameters by histogram statistical analysis. The spectrum can also be directly obtained through FFT analysis, and then its dynamic performance parameters can be calculated.  
To verify the proposed parameter estimation method, the TIADC data acquisition system mentioned in Section 3 is tested. The four ADC channels used in the TIADC system are realized by two AD9653 chips, and a single frequency sine wave is generated by the clock board. The TIADC test system is shown in Figure 8, and the ADC acquisition card is shown in Figure 9.  
By identifying and analyzing the four sub-channel models of the acquisition system, a comparison of the characteristic curves between the model of the acquisition system and the actual input and output can be obtained, as shown in Figure 10. It can be seen from the comparison of amplitude-frequency and phase-frequency curves of four sub-ADC channels that the trend of the results of the identified model is similar to the experimental data, which shows the feasibility and validity of this proposed method.  
Figure 8: TIADC test system.  
Figure 10: Identification Model Output and Actual Output of Four-Channel TIADC system: (**a**) amplitude and frequency; (**b**) Phase and frequency.  
Figure 9: ADC acquisition card.  
Due to the existence of gain mismatch and phase mismatch, spurious will appear in the spectrum of 30 MHz, 83.3 MHz and 136.4 MHz. The output spectrum of the 150 MHz sinusoidal signal before and after compensation is shown in Figure 11. For sinusoidal input signals from 20 MHz to 150 MHz, after calibration by the TIADC system, SNR above 59 dB and SFDR above 68 dB can be obtained. SNR increased by more than 12 dB, and SFDR increased by more than 13 dB. The dynamic performance of the TIADC system is close to that of its sub-ADC, indicating the effectiveness of this method.  
In Table 1, we compare the method in reference [28] with the method in this paper. After the correction of these two methods, the dynamic performance of the TIADC system used in this paper is very close, which further proves the feasibility of this method in this paper.
## 1 Introduction
Thus, it is necessary to develop an approach of obtaining body dimensions used for garment pattern making faster and more accurate than the current methods. With the rapid development of artificial intelligence (AI) nowadays, predicting human body dimensions by AI rather than measuring them physically has attracted more and more attention in the apparel industry. Recently, due to the advantages of artificial neural networks (ANNs), such as good non-linear approximation abilities and adaptive and self-organizing abilities, and as one of the most popular machine learning approaches, ANN technology has been widely in many fields, including viscosity prediction of nanofluid, human behavior prediction, pattern recognition, and adaptive control [10; 11; 12; 13; 14]. In the field of garment pattern making, [PERSON] et al. [15] presented an artificial neural network model to predict the pattern parameters of men's shirt in 2003. However, the inputs of the model proposed consisted of 58 body dimensions which were rather complicated and not easy to collect simultaneously. In 2014, one study by [PERSON] et al. [16] put forward a non-linear model to predict the detailed body sizes using feature parameters extracted by principle component analysis. But the feature parameters used as the inputs of the proposed model were difficult to calculate in the research. Another study by [PERSON] et al. [17] in 2017 developed a back propagation (BP) neural networksmodel to predict lower body dimension used for pants pattern design. However, the application of other neural networks models was not mentioned in that study. What is more, little attention has been focused on the application of radial basis function (RBF) ANN in garment pattern making. Therefore, the aim of this paper is to put forward a new ANN model based on radial basis function to improve the precision of estimating body dimensions used for garment pattern making. Additionally, the proposed model could be used for pattern makers with a lack of expertise and experience, through inputting the learning data based on the knowledge of experienced pattern makers. The rest of the sections of this study are organized as follows. The \"Methodology\" section expounds on the research scheme and procedures in this paper, including anthropometric data acquisition, and construction of the RBF ANN-based predictive model. In the \"Results and Discussion\" section, key factors affecting predictive precision of the proposed model are analyzed and an application of the model is put forward and in making the patterns of active leggings. Finally, the \"Conclusion\" is presented with the conclusions and possible future works.
## 7 Evolved Solution FSM
### Test Case Mapping onto the Evolved FSM  
After the FSM is evolved, its functionality is validated by mapping the six TCs (five of which were defined earlier in Section 5 and shown in Figure 4, the dataset generation process), onto the evolved state machine. These TCs represent various user interactions with the vending machine, covering all valid input-output behaviours. Each TC is shown in a unique colour on the FSM diagram in Figure 9 to clearly highlight the path it follows through the states.  
always @(posedge clk)//orposedgerst) begin if(!rst) begin... elseif(state==S0)begin if(x1==1)begin state=S4; out=1; trrn=1; end elseif(x0==1)begin state=S3; out=0; trrn=0; end elsebegin state=S1; out=0; trrn=0; end elseif(state==S0)begin if(x1==1)begin state=S0; out=0; trrn=1; end elseif(x0==0)begin state=S1; out=0; trrn=1; end elsebegin state=S5; out=0; trrn=1; end end elseend elseend state=S0; out=0; trrn=0; end end end elseend elseend best state=S0; out=0; trrn=0; end end elseend best state=S1; out=0; trrn=1; end end elseend best state=S0; out=0; trrn=0; end end end end ```  
The mapping uses the following colour scheme for the six TCs:  
* Azure for TC-1;
* Orange for TC-2;
* Maroon for TC-3;  
Figure 8: Code snippet taken from one of the perfectly evolved solution circuits using LS.  
Each coloured path corresponds to a valid sequence of transitions based on the inputs defined in the respective test case. For example, in TC-2, the system begins in state S0, detects a water selection x0 = 0, and proceeds to state S1. From there, it transitions through additional states depending on the payments made using x1, until the total payment matches the product cost.  
The same logic applies to the fizzy drink selection path, such as in TC-4, which begins with x0 = 1 in S0 and continues through states like S3, S1, and S2, depending on the combination and order of EUR 0.50 and EUR 1 coins. It finally ends at S3 with the delivery of the product and a return change of EUR 0.50. In each TC, the state transitions and output responses follow the correct timing and behaviour, as expected from the FSM.  
The additional TC, TC-6, is shown in parrot green in Figure 9 and follows the input and output pattern '1_01x_0010_0000'. This TC is specifically included to highlight the use of state S5, which is not involved in the first five TCs. By showing a path that passes through S5, the evolved FSM demonstrates its coverage of all relevant states and confirms its flexibility in handling less common but still valid scenarios. The transition labels in this figure also follow the same colour coding as used in the dataset generation figure (Figure 4).  
Even though the evolved FSM shown in Figure 7 includes some transitions that are not used during execution, these do not affect the system's operation. The testbench restricts the inputs per cycle, so only the correct transitions are activated. The evolved FSM, therefore, completes all TCs correctly and achieves the maximum fitness score of 64, meaning it delivers the right outputs for all 64 input vectors derived from the dataset.