esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Examples and Simulation  
In this paper, we used the data from the solar power plant in Ashland, Oregon, with the capacity of 5 kW, downloaded from the website of [23], to simulate the proposed FOS-ELM-based prediction model and compare it with the OS-ELM and ELM models. The plant faces south, tilted 15 degrees. As the paper has shown, the training time and prediction accuracy (in terms of \(MAPE\) and \(RMSE\)) of the ELM method are better than the BP neural networks [24], so we therefore focus on the comparison of ELM, OS-ELM and FOS-ELM.  
The 5 kW solar electric arrays are located on the roof of the Ashland Court House, which is shown in Figure 3.  
Figure 3: The 5 kW solar electric arrays on the roof of the Ashland Court House.  
Figure 2: Flowchart of the online sequential extreme learning machine with forgetting mechanism (FOS-ELM) prediction model.  
The details of the three models are presented as follows:  
Model 1 (FOS-ELM Algorithm): The sigmoid function was chosen as the active function; the data obtained at 48 h ahead of the current time (from 6:00 to 18:00, every 15 min) was used as the training data to predict the power output; the training sample was updated every one hour and then the data obtained 48 h ago was dropped.  
Model 2 (OS-ELM Algorithm): The sigmoid function was chosen as the active function; the data obtained at 48 h ahead of the current time (from 6:00 to 18:00, every 15 min) was used as the training data to predict the power output; the training sample was updated every one hour with the earlier data remaining.  
Model 3 (ELM Algorithm): The sigmoid function was chosen as the active function; the data obtained within the last 48 h of the last month (from 6:00 to 18:00, every 15 min) was used as the training data to predict the power output. In other words, the model was retrained every month.  
We set the number of hidden layer neurons as 120.
## 3 Evaluation  
### Metrics  
Three metrics are proposed to detect the presence or absence of crackle events: accuracy (_Acc_), sensitivity (\(S_{e}\)) and precision (\(P_{r}\)). All of them are calculated analyzing each signal within the dataset \(\psi\) by matching the original data and the estimated data provided by the proposed method. The metric _Acc_ represents the ability to correctly detect the presence or absence of the crackles events when they are active or inactive in the signal, \(S_{e}\) represents the ability to detect the number of missed crackle events within the dataset, and \(P_{r}\) represents the ability to detect frames within a signal with crackles when no crackles events are active. These three mentioned metrics are calculated as shown in Equations (8)-(10). The parameter \(t_{p}\) indicates true positives, that is, number of active crackle events within the dataset \(\psi\) correctly detected as crackle. The parameter \(f_{p}\) represents false positives or false alarm events, that is, the number of crackles events inactive within the dataset \(\psi\) incorrectly detected as crackles. The parameter \(f_{n}\) indicates false negatives or missed events, that is, the number of crackles events active within the dataset \(\psi\) incorrectly detected as no crackle.  
\[Acc=\frac{t_{p}}{t_{p}+f_{p}+f_{n}} \tag{8}\]  
\[S_{e}=\frac{t_{p}}{t_{p}+f_{n}} \tag{9}\]  
\[P_{r}=\frac{t_{p}}{t_{p}+f_{p}} \tag{10}\]
# Research Frontiers in the Field of Agricultural Resources and the Environment
## 4 Discussion
### About the Practical Implications  
Using LDA and other artificial intelligence tools to assist governments and regulatory agencies in developing better strategies for agricultural resource utilization, while considering environmental protection, will help improve the efficiency and sustainability of agricultural production. By accurately analyzing and predicting the utilization of agricultural resources and environmental impacts, governments and regulatory agencies can formulate more scientific and reasonable policies to promote the sustainable development of agriculture.  
Managers in the agricultural sector can utilize the research directions proposed to better improve the methods and measures of agricultural production processes. By applying text mining and topic mining techniques, managers can systematically analyze and evaluate existing agricultural production technologies, identify problems and bottlenecks, and propose targeted improvement measures.  
The use of topic mining (as well as general text mining) can help to ensure that management personnel related to agricultural resource extraction and utilization can observe the impact of their activities on the environment and establish practical and feasible measures, such as establishing monitoring and feedback mechanisms. By regularly collecting and analyzing relevant textual data, such as agricultural activity reports, and environmental monitoring data, etc., the impact of agricultural resource extraction on the environment can be discovered and evaluated in a timely manner, and feedback and suggestions can be provided to management personnel to promote environmentally friendly agricultural practices.
## Measurement error between patient and phantom
### Radiation Equivalence  
Radiation equivalence was achieved for the lung tissue (\(-987\pm 124\) HU), lung nodules (\(36\pm 53\) HU), and diaphragm (\(-65.7\pm 93.6\) HU), with no statistically significant median differences of \(-116\) HU (\(95\%\) CI [\(-143\), \(35.9\)], \(p>0.05\)) for lung tissue and \(-4.59\) HU (\(95\%\) CI [\(-13.5\), \(19.8\)], \(p>0.05\)) for lung nodules. No significant mean differences were observed for the diaphragm (\(-111\) HU, \(95\%\) CI [\(-232\), \(9.96\)], \(p>0.05\)).  
However, statistically significant differences in HU between their patient counterparts were measured for bone (\(-196\) HU, \(p<0.001\), \(95\%\) CI [\(-283\), \(-110\)]), heart (\(-116\) HU, \(p<0.001\), \(95\%\) CI [\(-131\), \(-102\)]), muscle (\(-166\) HU, \(p<0.05\), \(95\%\) CI [\(114\), \(196\)]), airways (\(77.7\) HU, \(p<0.05\), \(95\%\) CI [\(6.44\), \(149\)]), large blood vessels (\(-69.7\) HU \(\pm 110\), \(p<0.05\), \(95\%\) CI [\(-168\), \(-12.7\)]), pulmonary vessels (\(93.9\) HU, \(p<0.001\), \(95\%\) CI [\(64.6\), \(123\)], skin (\(-58.5\) HU, \(p<0.05\), \(95\%\) CI [\(-95.2\), \(-17\)], and subcutaneous fat (\(-176\) HU, \(p<0.05\), \(95\%\) CI [\(24.4\), \(303\)]). Phantom tissues exhibited, on average, lower attenuations and larger variation in their HU compared to corresponding patient tissues (Figure 8).
# Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels
## 5 Results and Discussions
### The Outcomes of the UL Scenario
Figure 17: OP vs. SNR for four users with varying distances, with 64 \(\times\) 64 MIMO and BW 80 MHz for UL NOMA. The BW and OP have an inverse connection, with an increase in BW resulting in a drop in OP. The OP drops dramatically when the system is optimized utilizing the MIMO technique. With an improvement rate of \(10^{-1.9}\) in OP, the results are superior to those of the top U2 users in [38].
## 3 Results
### Sensory Analysis  
In Figure 4, the sensory analysis of the wines produced from the Sauvignon Blanc variety is presented. In general, no statistically significant differences were observed in the grades received in the visual descriptor 'color', the olfactory descriptors 'floral' and 'citrus' as well as the gustatory descriptor'sweetness' between the wines produced. The wines produced by spontaneous fermentation received lower grades in the olfactory descriptor 'aroma intensity', the gustatory descriptor 'balance' as well as the overall quality, compared to the wines produced by inoculation. The different inoculation strategies also affected the sensory analysis of the wines to some extent. The wines produced with the monocultures of the strains under study received different grades (\(p<0.05\)) in the overall quality and the olfactory descriptor 'aroma intensity'. More specifically, the grades received by the wine made with the monoculture of the _S. bayanus_ BCS103 were higher than the respective received by the wine made with the monoculture of the _S. pastorianus_ SP2. Co-inoculation with both strains had also a significant effect on the sensory analysis of the wines. More specifically, the olfactory descriptor 'aroma intensity' of the wine made with the inoculum ratio 90/10 and the overall acceptance of the wines made with the inoculum ratios 70/30 and 90/10 were improved compared to the wine made by the monoculture of the _S. pastorianus_ SP2. Conversely, the olfactory descriptors 'aroma intensity', amylic' and 'complexity' as well as the gustatory descriptors 'body/roundness' and 'aftertaste/persistence' and the overall quality of the wine made with the inoculum ratio 99/1 received lower grades than the respective made with the monoculture of the _S. bayanus_ BCS103. Similarly, the wine made with the inoculum ratio 99/1 received a higher grade in the olfactory descriptor'reduction' compared to the one made with the monoculture of the _S. bayanus_ BCS103.  
Figure 3: Principal component analysis of 20 volatile compounds of Sauvignon blanc wines fermented with monocultures of _S. bayanus_ (BCS103) and _S. pastorianus_ (SP2), mixed cultures of both species in different inoculation ratio as well as the spontaneous fermentation condition.
# VLSM-Net: A Fusion Architecture for CT Image Segmentation
## 2 Methods
### V-Net Model  
The V-Net model plays a role in generating accurate initial segmentation results by using its 3D CNN architecture. The structure of the V-Net model is shown in Figure 3.  
Figure 2: The structure of the VLSM-Net model: (**A**) training period; (**B**) test period.  
The V-Net model mainly comprises an encoder path and a decoder path [23]. The encoder path is on the left side; it aims to encode the input and extract the features. The encoder path includes four stages, and each stage includes 1 to 3 convolutional layers and down-sampling layers. The role of convolutional layers is to extract characteristics from the image. On the other hand, the role of the down-sampling layer is to down-sample the feature map in order to decrease the size of input and increase its receptive field. The model avoids information loss through the use of a \(2\times 2\times 2\) convolutional kernel instead of a max-pooling.  
The decoder, on the right side, aims to decode the extracted feature maps and recover the size of the input. The decoder path has many commonalities with the encoder path. The decoder section includes four stages which contain 1 to 3 convolutional layers and use short cut, too. In the up-sampling stage, our purpose is to recover the size of the feature map by using the deconvolution operation. The encoder path and the decoder path are connected by 3 convolutional layers.  
The V-Net module uses a short cut to connect convolutional layers in different stages, which can significantly improve the gradient flow. It helps to solve the problem of network degradation.  
In addition, in the last output stage, the classifier contains a convolutional layer and a SoftMax layer, where the convolutional layer is applied to reduce the number of feature maps, while the SoftMax layer is applied to obtain probabilistic segmentation of the foreground and background regions.  
In conclusion, the V-Net model is a deep learning network that combines a 3D convolutional neural network and U-Net model with short cut connections. This model can extract detail features from medical images and improve segmentation accuracy. V-Net has been shown to achieve great performance in many medical image segmentation tasks.  
In our paper, dice loss is adopted as the loss function, and Laplace smoothing is added to avoid the division by zero and overfitting. The loss function is represented as:  
\[\text{Dice loss}=1-\frac{2|X\cap Y|+1}{|X|+|Y|+1} \tag{1}\]  
Figure 3: The structure of the V-Net model. The left encoder includes four down-sampling modules, the right decoder includes four up-sampling modules.  
where \(X\) is the predicted value, \(Y\) is the true value, and \(|X\cap Y|\) is the intersection of predicted and true values. Total dice loss consists of nodules dice loss and non-nodules dice loss.
# Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning
## 4 Experimentation  
Figure 1 shows the experimental scheme conceived for this work. The idea is to comparatively assess the performance of the RHC strategy in both the string and feature-based spaces. For that the initial string data undergo two different processes: on the one hand, the data is directly processed in the string space with our extended RHC proposal; on the other hand, the string data is mapped onto a feature-based representation by means of the Dissimilarity Space methodology for then being processed by the original RHC. The comparison between the results obtained in both representation spaces provides the main conclusions of the work.  
As of dissimilarity metrics used in this experimentation, we have considered the well-known Edit Distance [23] for the string space. This measure defines the distance between two sequences of characters as the minimum number of modifications (insertions, deletions, or substitutions) required to transform one string into the other. Regarding the feature-based representation we have resorted to the Euclidan distance. In all the classification stages we have fixed the parameter \(k=1\) for the \(k\)NN classifier. Note that since each cluster obtained by the RHC algorithm is represented by a single element, there is no point in using other values for this parameter.  
The rest of the section introduces the Dissimilarity Space process considered for mapping the string data to a statistical representation as well as the corpora and performance metrics used for the evaluation.
## 4 Numerical Examples  
### Criteria for Model Comparison  
The parameters in the mean value function \(\mathrm{m(t)}\) of models can be estimated using various parameter estimation methods. Here, the parameter for the mean value function \(\mathrm{m(t)}\) is estimated by the LSE (least squares estimate) method. Eight common criteria for model comparison, i.e., MSE (mean squared error), RMSE (root mean squared error), AIC (Akaike's information criterion), \(\mathrm{R^{2}}\) (correlation index of the regression curve equation), \(\mathrm{Adj~{}R^{2}}\) (adjicted \(\mathrm{R^{2}}\)), SAE (sum of absolute error), PRR (predictive ratio risk), and PP (predictive power), will be used for the goodness-of-fit estimation of the model and to compare the proposed model with other models in Table 1. These criteria are described as follows in Table 2. For six of these criteria, i.e., MSE, RMSE, AIC, SAE, PRR, and PP, the smaller the value, the closer the model fits relative to other models. \(\mathrm{R^{2}}\) and \(\mathrm{Adj~{}R^{2}}\) values should be close to 1 for a good fit.  
In Table 2, \(\mathrm{\hat{m}(t_{i})}\) is the estimated cumulative number of failures at \(\mathrm{t_{i}}\) for \(\mathrm{i}=1,~{}2,\cdots,n\); \(\mathrm{y_{i}}\) is the total number of failures observed at time \(\mathrm{t_{i}}\); \(\mathrm{n}\) is the actual data, which include the total number of observations; and \(\mathrm{m}\) is the number of unknown parameters in the model. We use the following Equation (9) to obtain the confidence interval of the NHPP SRM in Table 1.  
\[\mathrm{\hat{m}(t)}\pm Z_{\alpha/2}\sqrt{\mathrm{\hat{m}(t)}}, \tag{9}\]  
where \(Z_{\alpha/2}\) is the percentile of the standard normal distribution and \(\alpha\) is a significant level [30].
# Effect of PODE on Emission Characteristics of a China VI Heavy-Duty Diesel Engine
## 2 Experimental Setup
### Test Fuels  
The diesel fuel used in the tests was purchased from gas stations, which is 0\({}^{\text{g}}\) China VI standard diesel produced by CNPC. The PODE was bought from Jinchang Co., Zibo, China. The main PODE components were measured from PODE3\(\sim\)6, and their mass fraction is 37.5:23.2:19.5:12.6, accounting for 92.8%. Some physical properties of diesel and PODE are shown in Table 3.  
In the experiments, diesel fuel was blended with 10%, 20% and 30% PODE in weight. Hereinafter, diesel and the above blends were marked as D100, DP10, DP20 and DP30, respectively.
# Learning Approach for Angle Estimation Based on Characteristics of Phase Drift
## 4 Performance Evaluation  
### Training Environment and Data Composition  
Figure 8 shows the university classroom where the experiments were conducted. The room measuring 201.36 m\({}^{2}\) contains 40 desks with PC. The room layout included rows of desks and chairs, which could act as minor obstructions, while the walls, whiteboard, and glass windows introduced reflective surfaces that could cause multipath propagation.  
Due to the RS-232C serial communication's speed limitation of 115,200 Baud rate, it was challenging to extract data in real time directly. To overcome this, we implemented a software function that first stores the data in internal memory. Once a specific data size is reached, the system pauses measurement and transfers the data to the host PC via RS-232C. This setup particularly emphasized pa1 and pa2, which were subsequently used to calculate mean and difference values for further analysis. The training environment, summarized in Table 1, included specific hardware and software configurations to experiment model  
Figure 8: Layout of the experimental environment.  
training and evaluation. Additionally, the training data and parameters of our model are described in Table 2.
## 4 Case Study: Application to Missouri Crash Dataset
### Expert Validation of Flagged Cases  
To assess the model's real-world effectiveness, 80 records flagged as potential misclassifications (i.e., predicted czone \(\
eq\) actual czone) were manually reviewed by a traffic safety expert with domain experience in crash analysis. Each flagged case was reviewed manually using a structured protocol:  
1. Narrative-Label Cross-Check: The expert read the full narrative description and evaluated whether it included strong indicators of construction zone involvement--such as references to signage, workers, barriers, cones, or lane closures.
2. Contextual Consistency: The structured fields (e.g., time, lighting, crash type) were reviewed in relation to the narrative to detect inconsistencies that might support or refute the predicted label.
3. True Misclassification Criteria: A case was marked as a genuine misclassification if the narrative clearly indicated construction zone conditions, yet the original structured label (czone = 0) contradicted this.
4. Ambiguous Cases: If the narrative lacked sufficient clarity or if the indicators were indirect, the record was not counted as a true misclassification, to maintain high confidence in label corrections. Based on this review, each flagged case was classified into one of the following categories:
* Confirmed Misclassification (True Positive): The model correctly flagged a mislabeled record.
* Correct Label (False Positive): The model flagged a case, but the expert found the original label to be correct. The expert validation results are summarized in Table 4 and visualized in Figure 5.  
The expert validation confirms that the model is capable of surfacing truly mislabeled records. Although only four of the flagged records were verified as incorrect labels, these would likely have remained undetected through manual review alone. This analysis supports the model's role in semi-automated auditing, especially when used to prioritize cases for further investigation.  
Out of 80 model-flagged mismatches, 4 were confirmed as mislabeled and 76 had their original labels verified as correct by the expert.
## 4 Results and Discussion
### Correlations of CMP Results with Pad Performance Index  
After 50 rounds of CMP tests with totally 150 CuB substrates, the MRR of CMP and surface roughness \(\rm S_{a}\) are presented in Figures 12 and 13. The average MRR is 602.97 nm/min and the average \(\rm S_{a}\) is 3.496 nm. The MRR is 710 nm/min and reaches a maximum 762.5 nm/min in the third round of CMP experiment. From experimental results, the PELI and PU remain the same during the first three rounds of tests. The MRR decreases under average value after 25 rounds of tests and \(\rm S_{a}\) of wafer increases over the average line after 31 rounds of test, but \(\rm S_{a}\) value has a trend to increase around 25 rounds of test.  
The PELI refers to the remaining groove depth of pad, which represents the ability to store and transfer slurry during CMP process. The effective groove depth can be used to refresh and spread the slurry into the surface between pad asperities and CMP area of Cu film. With the wearing of pad thickness or reducing pad groove depth, the MRR of CMP keeps decreasing. When PELI is smaller than 65%, i.e., the groove depth is less than 280 \(\rm\upmu\)m, the MRR of CMP becomes unstable. The MRR is 525 nm/min as PELI is between 35-50%. The MRR of CMP decreases 35% as the PELI of pad is over 70%.  
Figure 11: Cross-section SEM photo of pad.  
Figure 12: Average MRR of CuB wafer of 50 rounds of test.  
Figures 14 and 15 show the correlations between MRR of CMP with PELI and PU of polishing pad. The MRR of CMP has a high correlation as 0.94 with PELI and \(-\)0.94 with PU. The high correlation factor shows the MRR of CMP is highly influenced by the PELI of pad.  
Consequently, Figures 16 and 17 show the correlations between wafer S\({}_{\text{a}}\) with PU and PELI. The correlations are obtained as 0.74 and \(-\)0.74. The wafer S\({}_{\text{a}}\) keeps in the same level in the first 25 rounds of the test, and then raises with continuous tests. The correlations are obtained 0.93 and \(-\)0.91 by calculating only the last 25 rounds of the CMP tests, where the PELI is below 66.8%. The wafer S\({}_{\text{a}}\) is significantly affected when pad is conditioned after rounds of processes. The correlations of each performance index and wafer quality are shown in Table 6.  
Figure 14: The correlations between MRR and PU.  
Figure 13: Average Sa of CuB wafer of 50 rounds of test.  
Figure 16: The correlations between wafer Sa and PU.  
Figure 15: The correlations between MRR and PELI.
# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 1 Introduction
The aim of this study is to construct a new prediction model by integrating PCA and the Elman Neural Network to improve the accuracy and efficiency of debris flow risk assessment, effectively model complex nonlinear relationships, and provide technical support for debris flow disaster prevention and mitigation in Yunnan Province and similar areas.