esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Quality Assessment for Gibbs Ringing Reduction
## 3 Metrics
### Metrics Comparison Results  
In this Section, we evaluate \(\ell_{e}^{0}\), SSIM, MS-SSIM, MAE, Huber loss and entropy for our problem. We compare the metrics using a series of filtered Fourier reconstruction results generated by Gaussian filters. Gaussian filters change the degree of filtering based on the single parameter \(\sigma\) that affects the cut-off frequency.  
Figure 3 shows the evaluation of different Gaussian filters using \(\ell_{e}^{0}\), SSIM, MS-SSIM, MAE, Huber loss and entropy, all normalized to the range 0 to 1. The value of \(\epsilon\) used for \(\ell_{e}^{0}\) and Huber loss was \(10^{-4}\), which is the order of magnitude of the median error of the Fourier reconstruction. It can be seen from the figure that all six metrics show peaks or nadirs for all three test images. We can see that for the phantoms the six metrics suggest comparable settings while, for the MRI, \(\ell_{e}^{0}\) and MAE are the two that most closely agree with SSIM. \(\ell_{e}^{0}\) and Huber loss could be further tuned to better fit human perception of ringing by adjusting \(\epsilon\). We note that MS-SSIM suggests more lenient filtering in comparison with the results of SSIM. We will discuss that further in Section 5.  
We conclude that all six of these metrics are candidates for assessing Gibbs ringing quantitatively.  
In Figure 4, we present the optimal reconstructions of the Shepp-Logan phantom from the different metrics. For reference, we also show the ground truth without ringing and the Fourier reconstruction with added ringing. It can be seen from the figure that \(\ell_{e}^{0}\), Huber loss and MAE agree with the result of SSIM. The suggestion of entropy is blurry, which suggests too much filtering, whereas the results of MS-SSIM does not reduce ringing as much as possible.  
Figure 2: The transfer function of Huber loss. \(\epsilon\) is the transition point.
## 2 Materials and Methods
### Performance Evaluation and Spatialization  
Because farmers report sowing and harvest day and not the emergence or start and end of seasons, to estimate the sowing date, 10 days were subtracted from the detected SOS (provided by all algorithms that represented plant emergence) dates because this is the average time when soybean plants emerge and become visible to satellites [36; 57; 58]. Thus, it can be assumed that the gaps found between the estimates are due to the error between the data estimated by remote sensing and the data measured in the field. Shapiro-Wilk normality test [59], Spearman's rank correlation coefficient (R), the Root Mean Square Error (RMSE), bias (%), and linear regression between the reference field data and the estimates were used to assess the performance of the algorithms. The non-parametric Wilcoxon paired-samples test [60] (also known as the Wilcoxon signed-rank test) was used to assess whether there was a statistically significant difference between the estimates and the field data. The processing times (seconds) were also recorded.  
Lastly, after the validation step, a map of the soybean season's sowing date, harvest date, and length was generated using one of the packages for the entire region (four states combined) for the 2020-2021 season. The mask with soybean areas for the 2020-2021 season is available in [39].
## 5 Experiments and Results
### Comparative Experiments with Machine Learning Models  
As power battery operational data are inherently structured multivariate time-series data, traditional machine learning methods still possess a certain degree of modeling capability for such data. To further evaluate the advantages of the proposed approach, we conducted comparative experiments using five classical machine learning models: K-Nearest Neighbors (KNNs), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM).  
Since traditional machine learning algorithms cannot directly process two-dimensional time-series inputs, we adopted a flattening approach to convert the data format. Specifically, each original sample with shape (256, 8) was flattened into a (1, 2048) vector. This transformation preserves as much temporal feature information as possible while satisfying the requirement of fixed-size inputs for machine learning models. Compared to statistical  
Figure 13: Precision-Recall (PR) curves of deep learning models.  
feature engineering, this method retains the structure of the original sequence without introducing significant manual bias.  
The flattened training and test datasets were then used to train and evaluate the five machine learning models mentioned above. The same evaluation metrics--Precision, Recall, and F1-Score--used in the deep learning experiments were applied here for consistent comparison. The results are summarized in Table 6.  
Experimental results show that KNN and Random Forest performed relatively well in detecting fault samples, achieving F1-Scores of 77.95% and 75.36%, respectively, indicating a moderate capability for fault identification. In contrast, Naive Bayes yielded the worst performance, with an F1-Score of only 35.60%, indicating its inability to detect faults effectively.  
Logistic Regression and SVM showed high classification performance on the negative class (normal samples), with Precision and Recall both exceeding 90%. However, their Recall scores on the positive class (fault samples) were only around 50%, suggesting that they struggle with fault detection--a critical task in this domain--and thus may fall short of practical application requirements.  
In summary, although some traditional machine learning methods (such as KNN and Random Forest) achieved modest performance on structured fault data, their overall classification capabilities remain significantly inferior to deep learning models like GAN-CNN-LSTM, particularly in terms of detecting fault samples. These results further confirm the advantage of deep learning models in capturing complex temporal and spatial patterns in power battery operational data.
###### Abstract  
Massive traffic jam is the top concern of multiple disciplines (Civil Engineering, Intelligent Transportation Systems (ITS), and Government Policy) presently. Although literature constitutes several IoT-based congestion detection schemes, the existing schemes are costly (money and time) and, as well as challenging to deploy due to its complex structure. In the same context, this paper proposes a smart route Internet-of-Vehicles (IoV)-based congestion detection and avoidance (IoV-based CDA) scheme for a particular area of interest (AOI), i.e., road intersection point. The proposed scheme has two broad parts: (1) IoV-based congestion detection (IoV-based CD); and (2) IoV-based congestion avoidance (IoV-based CA). In the given area of interest, the congestion detection phase sets a parametric approach to calculate the capacity of each entry point for real-time traffic congestion detection. On each road segment, the installed roadside unit (RSU) assesses the traffic status concerning two factors: (a) occupancy rate and (b) occupancy time. If the values of these factors (either a or b) exceed the threshold limits, then congestion will be detected in real time. Next, IoV-based congestion avoidance triggers rerouting using modified Evolving Graph (EG)-Dijkstra, if the number of arriving vehicles or the occupancy time of an individual vehicle exceeds the thresholds. Moreover, the rerouting scheme in IoV-based congestion avoidance also considers the capacity of the alternate routes to avoid the possibility of moving congestion from one place to another. From the experimental results, we determine that proposed IoV-based congestion detection and avoidance significantly improves (i.e., 80%) the performance metrics (i.e., path cost, travel time, travelling speed) in low segment size scenarios than the previous microscopic congestion detection protocol (MCDP). Although in the case of simulation time, the performance increase depends on traffic congestion status (low, medium, high, massive), the performance increase varies from 0 to 100%.  
internet of vehicles; traffic congestion detection and avoidance; intelligent transportation system; route planning +
Footnote †: journal: Applied Soft
## 2 Minimally Invasive Therapies for HCC and Their Tissular Effects
### Non-Thermal Ablative Therapies
#### 2.2.2 Irreversible Electroporation  
Irreversible electroporation (IRE) is a relatively new, non-thermal therapeutic method for HCC in patients with nodules that usually measure less than 3 cm. IRE is usually reserved for patients that cannot be treated with other minimally invasive methods due to the nodule depth or proximity to major vessels [76]. IRE was successful in obtaining complete ablation in over 90% of nodules and prolonging survival [77; 78; 79]. From a technical standpoint, the NanoKnife(r) system, created by AngioDynamics, which is the widely available commercial solution, uses a direct current of low energy but with high voltage (1000-3000 V) that is generated between the inserted electrodes [80]. The 19-gauge electrodes are parallel-inserted with local anesthesia and under US or CT guidance [81].  
The application of IRE causes the appearance of minuscule pores in the cellular membranes in random locations within the ablation area [82]. These pores will expand in  
Figure 6: Left hepatectomy and partial right liver lobe resection. MRI examination of a patient who underwent PEI for an HCC nodule (arrow) in the liver segment VIII 5 months prior. The treated nodule has no contrast enhancement in the arterial (**b**) or hepatobiliary (**c**) phase, has low signal intensity on both T1 (**a**) and T2 (**d**) fat saturated images, has peripheral restricted diffusion (**e**,**f**), and an adjacent wedge-shaped area of hypoperfusion (dotted arrow).  
size, increasing membrane permeability and ultimately dissolving the membrane while also triggering apoptosis. The procedure seems to be independent of blood flow, hence its application in nodules near blood vessels [82]. The area of ablation is characterized by hemorrhagic necrosis and edema, and apoptosis contributes to the extent of the cell damage [76]. Large blood vessels, bile ducts, and nerves adjacent to the ablation area appear unaffected [83]. However, minor blood vessels may demonstrate vascular congestion [84].  
IRE of HCC nodules also triggers an immunological reaction; a recent study has shown that shortly after the application of IRE, an increase in immune cells such as white blood cells, monocytes, neutrophils, and natural killer cells was identified in peripheral blood; a decrease of regulatory T-cells was also noted, signifying that cytotoxic cells are favored and this immune profile might lead to tumor growth restriction [85]. Neutrophil infiltration of the treated region was detected shortly after the procedure [84].
## 3 Analysis of Losses in Electrical Steels  
To precisely model and predict losses before assessing the impact of controlling material and physical properties, it is crucial to acknowledge potential errors in loss measurements, which are often overlooked in the profitable classification of electrical steels. These steels are primarily used in AC magnetic environments, and their magnetic properties are evaluated through testing under time-varying magnetic flux densities and sinusoidal waveforms, with maximum values of 1.5 or 1.7 tesla at 50 or 60 Hz, using methods such as the IEC Epstein square or the (SST) [23]. While these methods are well established for commercial classification, their use as research tools for studying loss mechanisms is limited and can be misleading because they do not provide accurate or absolute values of loss under ideal magnetic conditions.  
A critical limitation arises from the necessity to assume a constant value for the length of the magnetic path in the Epstein square despite knowing that it varies with the material's composition and magnetic conditions [24]. Recent reports have demonstrated that assuming this constant length leads to significant errors when comparing losses of similar steel grades [25]. In the worst-case scenario, although highly reproducible, the loss indicated by the Epstein square may deviate by 5-10% from the absolute value. In electrical machine cores, factors such as mechanical pressures, flux harmonics, and rotational magnetization introduce varying degrees of additional losses in different steel grades. Consequently, it is vital to ensure that these additional losses do not undermine the improvements achieved in the fundamental physical characteristics assessed using the Epstein test. The detrimental effects of these factors can be mitigated through the careful design and construction of the magnetic core, although they may result in an overall increase in basic losses by 10-30% [26]. Losses and other magnetic properties of the structure can be highly sensitive to mechanical pressures [27]. Given the challenge of eliminating various sources of stress in electrical machine cores, it becomes necessary to consider stress sensitivity curves, as shown in Figure 4, and ensure that any proposed new steel exhibits stress characteristics at least as good as those of the original material [28]. However, applying these properties directly to predict core losses is challenging due to the  
Figure 3: Morphologies of the second particle sections observed using TEM (**a**) A-0.800%Si, (**b**) B-1.570%Si, and (**c**) C-2.515%Si [22].  
random and varied distribution of stress in large cores, where localized pressures may play a significant role [27].
## 3 Results and Discussion
### Comparison with Available Studies
#### 3.3.3 California  
A neural network was used by [PERSON] et al. [19] for the Southern California region, to predict the magnitude of the seismic activities, obtaining 75.2% accuracy. GP-AdaBoost methods were employed by [PERSON] et al. [11] for the Southern California region, for seismic classification, which achieved accuracy of 86.6%. Similarly, [PERSON] et al. [9] applied the SVR-HNN approach to the aforementioned region, to predict the earthquake, and obtained an accuracy score of 90.6%. Table 10 shows that, comparing the existing studies and techniques, the proposed model performed better on the basis of its accuracy score, but also outperformed the other methods, in terms of precision and recall score. Our proposed model attained an accuracy score of 99.26%, a precision score of 99.29%, and a recall score of 95.87% for the Southern California region.  
As is evident from Tables 8-10, the performance of our proposed deep neural network model was superior to the state-of-the-art techniques and the extant literature, on the basis of standard evaluation metrics such as accuracy, precision, recall, and F1-scores.
## 4 Case Study of Data Collaboration
### Space-Time Travel Data Mining  
#### 4.2.1 Abnormal Data Filtering  
The GPS data in the framework described in this paper serves for traffic flow prediction and travel recommendation, so it is necessary to filter according to application scenarios before data collaboration, so as to reduce data processing capacity and improve processing speed. First, we need to delete fields that do not contribute to the traffic flow rate, such as acceleration. Then, we take a time interval of 1 s for sampling. In order to avoid accidental deletion caused by shortstop or red light, we choose the average duration of red light interval in China of 60 s as the time window length and consider the vehicle whose speed is 0 for 60 consecutive seconds to be stopped and delete it. At the same time, the points with excessive speed are considered error points and are deleted (Table 2 lists some filtered data and deletion reasons). By processing abnormal data, we reduce the amount of GPS data from \(3~{}\times~{}10^{8}\) to \(6~{}\times 10^{7}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Id** & **Longitude** & **Latitude** & **Time** & **Direction** & **Speed** & **Reason for Deletion** \\ \hline... &... &... &... &... &... &... \\
60015 & 120.2799744 & 31.4841888 & 27 July 2020 13:48 & 121 & 1666 & faster \\
68126 & 120.287168 & 31.4841248 & 27 July 2020 11:23 & 121 & 1666 & faster \\
22314 & 119.8930333 & 31.4895033 & 27 July 2020 05:47 & 0 & 0 & \\
22314 & 120.893033 & 32.4895033 & 27 July 2020 05:48 & 0 & 0 & \\
22314 & 121.8930333 & 33.4895033 & 27 July 2020 05:49 & 0 & 0 & Long stillness \\
22314 & 122.8930333 & 34.4895033 & 27 July 2020 05:50 & 0 & 0 & \\
22314 & 123.8930333 & 35.4895033 & 27 July 2020 05:51 & 0 & 0 & \\... &... &... &... &... &... &... \\ \hline \end{tabular}
\end{table}
Table 2: Partial filtered data and reasons for deletion.  
Figure 5: The process of data collaboration. Firstly, the original GPS data are judged to delete the abnormal data and retain the normal data. Then, the spatiotemporal distribution features were mined to extract travel hotspots and peak hours. Next, for hot intersections, UAV is used to collect traffic flow data during peak hours, and the UAV video data are processed and analyzed to mine traffic flow and capture behaviour characteristics. Finally, UE4 is used to visually model the digital twin of the target intersection.
## 5 Background Noise Elimination Method
### The Framework of the Background Noise Elimination Method  
The framework of the feature extraction method is shown in Figure 18. It divides into four steps.  
Step 1: Signal collection. Obtaining the original vibration signal by using the test rig, which is shown in Section 4.  
Step 2: Signal decomposition. According to the fitness function and ALO algorithm, the optimal parameters of VMD are automatically determined. Then, the original signal is decomposed into a series of IMF components by a parameter-adaptive VMD method.  
Figure 17: The flow chart of the proposed ant lion optimization-variational mode decomposition (ALO-VMD) method.  
Figure 18: The framework of the feature extraction method.  
Step 3: Signal reconstruction. Combining the advantages of kurtosis index and correlation coefficient, an efficient weighted kurtosis (EWK) index is used to obtain the reconstructed signal, as shown in Equation (38). The intrinsic mode functions (IMF) component with EWK index greater than zero is selected as a sensitive component, and the rest are noisy components [52].  
\[EWK=WK-\frac{1}{K}\sum_{j=1}^{K}WK\ \ \ \ \ i,j=1\text{ {to} }K \tag{38}\]  
Step 4: Reconstructed signal demodulation. The reconstructed signal is demodulated by the Hilbert envelope, and the faulty feature frequencies are detected from the envelope spectrum.
###### Abstract  
Reliability analysis and trade-offs between safety and cost with insufficient data represent an inevitable problem during the early stage of structural design. In this paper, efficient uncertainty theory-based reliability analysis and a design method are proposed under epistemic uncertainty. The factors influencing the structure are regarded as uncertain variables. Based on this, a new metric termed uncertain measure is employed to define an uncertainty reliability indicator (URI) for estimating the reliable degree of structure. Two solving methods, namely, the crisp equivalent analytical method and uncertain simulation (US) method, are introduced to calculate the URI and acquire reliability. Thereafter, a URI-based design optimization (URBDO) model is constructed with target reliability constraints. To solve the URBDO model and obtain optimal solutions, crisp equivalent programming and a genetic-algorithm combined US approach are developed. Four physical examples are solved to verify the adaptability and advantage of the established model and corresponding solving techniques.  
structural reliability; uncertainty quantification; epistemic uncertainty; uncertainty theory; reliability-based design optimization
## 4 Methodology
### Data Processing and Analysis  
The evaluation results from the 48 parks, along with corresponding on-site photographs, were meticulously recorded in Excel spreadsheets and organized into archival folders to facilitate the systematic analysis and interpretation of the collected information.  
Quantitative analysis was performed on the Excel data, utilizing IBM SPSS Statistics 26 to derive descriptive statistics, including means, standard deviations, and frequency distributions. These statistical measures provided a comprehensive overview of the evaluated parks' performance in terms of inclusivity and its various dimensions.  
Based on data processing, the researchers initially interpreted the degree of inclusiveness of urban parks in Hangzhou through an overall score assessment. Then, a detailed description of the parks' design inadequacies was given by analyzing individual indicators. Further, potential factors that might influence the inclusivity index scores were speculated upon, employing correlation studies. These factors encompass temporal elements, geographical location, urban planning, and land area utilization, among others. Lastly, this study conducted a macro- and micro-level analysis of the causes generating these variables.
# Experimental Investigation of TR-UHPC Composites and Flexural Behavior of TR-UHPC Composite Slab
## 3 Stacking and Bending Tests on BFT-UHPC Pedestrian Walkway Thin Slabs
### Bending Test
#### 3.4.3 Crack Width Development  
The maximum crack width of a conventional rectangular RC slab with the same reinforcement ratio can be calculated using the crack width prediction formula for concrete bending structures specified in GB50010-2010 [36]. The crack width of the test slab is then compared to this value.  
\[\omega_{max}=1.9\psi\frac{\sigma_{s}}{E_{s}}\bigg{(}1.9c_{s}+0.08\frac{d_{eq}} {\rho_{te}}\bigg{)} \tag{5}\]  
\[\psi=1.1-0.65\frac{f_{tk}}{\rho_{te}\sigma_{s}} \tag{6}\]  
where \(\psi\) is the strain inhomogeneity coefficient of longitudinal tensile reinforcement between cracks; \(f_{tk}\) is the tensile strength of UHPC, which is 8.5 MPa; \(\sigma_{s}\) is the stress of steel bar in the support section under the corresponding load bending moment; \(E_{s}\) is the elastic modulus of reinforcement, which is 200 GPa; \(c_{s}\) is the thickness of the protective layer of the outermost steel bar, which is 35 mm; \(d_{eq}\) is the equivalent diameter of longitudinal reinforcement in the tension zone, which is 8 mm; \(\rho_{te}\) is the longitudinal tensile steel reinforcement ratio calculated from the section area of effective tensioned concrete, which is 1.4%.  
Table 10 shows the calculation results for crack width. It is evident that the maximum crack width of the BFT-UHPC slab was lower than that of the RC slab at each loading stage. At the yield load (84.2 kN), the maximum crack width of the BFT-UHPC slab was only 35% of that of the conventional rectangular RC slab. This indicates that the BFT-UHPC slab has a superior crack resistance.