esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Parallel Implementation of Forward Modelling
This operation is carried out in an automatic way according to an OpenMP schedule; in general, the schedule selected by the compiler is optimal. 2. Every thread independently finds the best prism in its own search subspace. 3. The algorithm finds the prism which reduces the most the misfit between observed and computed data, which is selected as the best prism in the parallel iteration. 4. This process is repeated until a maximum number of iterations is reached. This process is illustrated in Figure 3, and the design of the algorithm is performed using [PERSON]'s methodology [55]: Partitioning, Communication, Agglomeration, Mapping. Once this cycle is finished, this optimal prism is taken as part of the grown body, restarting the cycle with updated search subspaces for each thread, considering the last updated prism as not modifiable. Performance tests of the parallelization scheme were performed in both the synthetic and real data models. We present performance results for gravimetric, magnetic and gradiometric inversions, considering the prisms and observations mesh configurations taken in the previous sections. The technical specifications of the workstation where the tests were performed are:  
* Two X5650 Intel Xeon (R) processors with six 2.66 GHz physical cores per socket (12 physical cores), 24 logical cores with HyperThreading (HT) activated,
* 48 GB of RAM memory,
* Red Hat 7.7 Operating System,
* GNU FORTRAN Devtoolset-6. In the field of SMP parallel computing, thread affinity restricts execution of certain threads to a subset of the physical processing units in a multiprocessor computer. Depending upon the topology of the machine, thread affinity is always important in terms of performance, particularly when the HT technology is enabled, which basically means that one CPU physical core can attend two threads at the same time, sharing the same Arithmetic-Logical Unit (ALU). Two common affinity setups for the workstation with two sockets and the HT enabled used in this research, are depicted in the Figure 4.  
For the performance tests, threads from 1 to 24 were spawned to cover the total number of logical processors; creating more than 24 threads would have consequences as a drop in performance. The results of the tests show that the distribution of the threads using the scatter affinity configuration is slightly better; in this configuration the threads are assigned first to a physical core and then to the corresponding logical cores in a consecutive way up to reaching (in this case) 24 threads (see Figure 4b). In such way, even though we tested both configurations, from here on we only show performance results for the inversion processes obtained with a scatter affinity setup. With HT technology enabled, it is necessary to create two threads and assign them to the same core to obtain a performance gain. According to the scatter affinity, the threads 0 and 12 are assigned to the same physical core, sharing the resources of the core, but switching between them very quickly. Figure 3: Parallelization scheme for the inversion process by growth of bodies.
# Composite Films of Polydimethylsiloxane and Micro-Graphite with Tunable Optical Transmittance
## 3 Results and Discussion  
### Transmittance Characterization for Different Concentrations  
The transmittance distribution is an important property of each sample and this distribution was measured using a spectrophotometer (PerkinElmer Lambda 1050) over the 300-1000 nm range. As shown in Figure 2, the four transmission lines illustrate one pure PDMS film sample and three composite film samples of PDMS and microcrystalline graphite powder with different concentrations. For the different sample concentrations, which were 0%, 0.15%, 0.25% and 0.42%, the mean transmittance values over the 300-1000 nm range were \(T_{0}=93.9\pm 4.1\%\), \(T_{1}=32.3\pm 0.8\%\), \(T_{2}=10.4\pm 0.6\%\) and \(T_{3}=1.4\pm 0.2\%\), respectively. These results illustrate that each line shows an approximately constant level. The relative standard deviation (i.e., the ratio of the standard deviation to the corresponding transmissivity) of the sample is extremely low at approximately 0.01-0.07. This indicates that the transmittance shows almost no dependence on the wavelength of the incident light.  
Figure 1: (**a**) Sample of pure polydimethylsiloxane (PDMS) (thickness \(h_{0}=1\) mm), placed on top of paper with printed colored fonts. (**b**) Sample of the dyed PDMS (thickness \(h_{0}=1\) mm), placed in the same position as that of the sample in (a). (**c**) Formation of the composite film observed under an optical microscope.  
Figure 2: Four transmission lines for the pure PDMS sample and the three concentrations (0%, 0.15%, 0.25% and 0.42%) of microcrystalline graphite powder composites in the polydimethylsiloxane (PDMS) samples within the 300–1000 nm wavelength range.  
In accordance with the Beer-Lambert law,  
\[\text{T}=10^{-kch_{0}} \tag{1}\]  
where T is the transmittance of the sample, \(k\) is the absorptivity, \(c\) is the microcrystalline graphite powder concentration and \(h_{0}\) is the sample thickness. If \(k\) and \(h_{0}\) are both constants, then T and c will have an exponential relationship.  
We fit a relationship curve between the transmittance and the powder concentration using the experimental data obtained, as shown in Figure 3, where \(k=3.24\) and \(h_{0}=1\) mm. As the figure shows, the experimental data are in good agreement with the values that were theoretically predicted using the Beer-Lambert equation.
## 4 Data Collection and Preparation
### Final Preparation of the Training and the Testing Datasets  
The input datasets for both crash likelihood and crash severity models were split into two subsets: (a) training dataset, which was used to train the models and contained 75% of features (data records), and (b) testing dataset, containing 25% of features. The training and testing datasets were separated using stratified sampling, which ensured that the sameproportion of output class labels was preserved in both the training and testing datasets as in the original data.  
For the crash severity model training, the ROSE transformation was applied to the original data to generate a synthetic training dataset used specifically for training the crash severity prediction models. When implementing ROSE, different probability values for the minority classes in the original dataset were evaluated (e.g., 0.3, 0.4, 0.5). The probability of 0.5 was selected as it provided the best model performance in terms of sensitivity and AUC values. The number of crash records (features) in the training datasets for each class before and after the ROSE transformation, as well as the size of each class in the testing datasets, are summarized in Table 4. The Figure 4 illustrates the effect of the ROSE transformation on balancing the majority class (PDO crashes) and minority class (injury/ fatal crashes) in the training dataset. The figure shows the graphs of training data points before the ROSE transformation (on the left-hand side) and after the ROSE transformation (on the right-hand side), using as an example the relationship between the standardized speeds and V/C ratios in the data. It should be noted that any two variables could be used for the visual representation as the aim of the figure is to illustrate the change in the number of cases belonging to the minority class. As can be observed in the graphs, there is a significant increase in the number of minority class cases after applying ROSE, providing a more balanced dataset to be used in model training.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Models/Corresponding Classes**} & \multicolumn{2}{c}{**Training Dataset**} & \multirow{2}{*}{**Testing Dataset**} \\ \cline{2-3}  & **Before ROSE** & & \\ \hline Crash Severity Dataset & 7616 & 12,719 & 2539 \\ PDO Crashes & 6009 & 6614 & 2003 \\ Injury/Fatal Crashes & 1607 & 6105 & 53 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Size of input datasets for the crash severity models.  
Figure 4: Standardized average speed vs. standardized v/c ratio in the crash injury severity dataset: before ROSE (**left**) and after ROSE (**right**).
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 1 Introduction
### Organization of This Paper  
The remainder of the paper is organized as follows: The noise-limited PL is explained in more detail in Section 2. Section 3 lists the path loss, antenna models, and three examples of PL distributions used in this study. In Section 4, the PL model is fitted to simulated training data. The PL model fitting results are in Section 5. Positioning with or without the censored PL is examined in Section 6 and the positioning simulation results are presented in Section 7. Finally, conclusions are given in Section 8.
## 4 Results and Experiments
### Dataset Description and Preprocessing Overview  
This study employed the CICIDS2017 and NF-BoT-IoT-v2 datasets, both established as pivotal benchmarks for IDSs, encompassing a broad spectrum of network behaviors and attack patterns. However, these datasets present inherent challenges that include missing entries, redundant data, outliers, and imbalances across classes. In this study, to mitigate these issues, a series of preprocessing techniques were implemented that included handling missing values, eliminating duplicates, addressing anomalous data points, and correcting class distribution disparities. These measures ensured that the datasets were effectively prepared for accurate and robust model evaluation in both binary and multi-class classification contexts.
## 3 Experimental Results
### Progressive Breakage Process
As the delay time increased, the independent action time of the stress wave from borehole-A (shown in Figure 1) was increased before detonating borehole-B. The crack gradually appeared preferentially as the minimum burden of borehole-A. In addition, the direction of the main crack extension on the slope changed from horizontal to vertical. When a longer delay time was used, the breakage patterns of the bench slope are consistent with the studies of [PERSON] et al. [37, 48]. In their single-hole blasting experimental results, the opening velocity of vertical cracks was faster than the horizontal cracks. The results agree with the observation in this paper that as the delay time increases, the slope breakage process increasingly tends to be single-hole blasting, leading to an increase in the opening velocity of vertical cracks. The progressive breakage processes presented the effect of delay time on the fragmentation of bench specimens. As the delay time increased, the breakage pattern of the bench slope transformed from horizontal cracks dominated to vertical cracks dominated.
# Transfer Learning Model for Crack Detection in Side SlopesBased on Crack-Net
## 4 Experiment
### Model Stability and Reproducibility Analysis  
To comprehensively assess the stability and reproducibility of Crack-Net under various experimental conditions, we performed 50 independent runs for each of five representative scenarios: baseline, test-time augmentation (TTA), MC dropout, weight noise perturbation, and threshold variation. We conducted statistical analyses of key performance metrics. The mean, standard deviation, and coefficient of variation (CV) for F1, AP, and mIoU are summarized for all experimental settings in Figure 11. All CV values remain below 0.017, indicating excellent model stability. Notably, the baseline model achieves CVs of 0.007 for F1 and 0.010 for AP, while the TTA strategy further reduces the CV of F1 to 0.006, demonstrating highly consistent performance across different scenarios. Furthermore, the MC dropout and weight noise experiments confirm the model's robustness to uncertainty and parameter perturbations. The slightly higher CV observed for mIoU highlights the inherent complexity of pixel-level crack segmentation.  
Figure 12 shows the distribution of F1, AP, mIoU, and precision for each experimental condition using box plots. The TTA and baseline methods display the most concentrated performance distributions, characterized by narrow interquartile ranges and the absence of significant outliers, which further confirms the reliability of the model architecture and transfer learning strategy. In contrast, the distributions for MC dropout and weight noise are somewhat wider, indicating increased performance variability when uncertainty or parameter perturbations are introduced. Nevertheless, the overall variability remains well controlled. In summary, Crack-Net exhibits outstanding stability and reproducibility across diverse practical scenarios, providing strong statistical evidence for its deployment in engineering applications such as slope crack detection.  
Figure 11: Performance and stability comparison across experimental settings.
## 3 Results and Discussion
### Most Effective Color Features Using the Hybrid ANN-SA Method  
The 27,687 apples objects segmented in the previous step were manually labeled by a human operator in one of the four ripening classes defined (i.e., unripe, half-ripe, ripe, and overripe). The hybrid ANN-SA technique defined in Section 2 was used to select the most effective set of color features. A total of 19,092 samples were used for training, 4091 for validation and 4091 for testing, being all sets disjoint. As a result, six parameters were selected as the most effective features: excess  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predicted/Real Class** & **Apple** & **Background** & **All Data** & **Segmentation Error by Class (\%)** & **Segmentation Accuracy (\%)** \\ \hline Apple & 27,274 & 413 & 27,687 & 1.49 & 98.43 \\ Background & 298 & 17,321 & 17,619 & 1.69 & 98.43 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix, segmentation error, and accuracy of the segmentation algorithm for identifying background objects and apples on the test set.  
red (EXR); normalized red of RGB (R\({}_{\text{n}}\)); mean of L in HSL color space; standard deviation of Cb in YCbCr; color index for extracted vegetation cover in L*u*v* (CIVE); and red-blue color contrast (RBI).  
As could be expected, the obtained results remark the importance of the red channel to distinguish the state of ripeness of the apples. However, this channel should not be used by itself, but compared to the other channels; for example, by using the normalized red (R\({}_{\text{n}}\)) or comparing it to the blue (RBI) or the green channel (EXR). Other selected features indicate the importance of the luminance of the objects.  
Unlike the steps of apple segmentation and object classification, that must be applied to all the frames captured by the drone in a practical use of the system, the selection of the most effective color features is a process that should only be applied once. In other words, these six features should be applied to all the video sequences obtained under similar conditions, without needing additional training.
# BWLM: A Balanced Weight Learning Mechanism for Long-Tailed Image Recognition
## 3 Method
### Preliminaries  
For training datasets with long-tailed data distribution, long-tailed image recognition attempts to learn high-performance classification models. Formally, we define \(D=\{x_{i},y_{i}\},i\in\{1,2,\ldots,n\}\) as the long-tailed training set, where data sample \(x_{i}\) is labeled as \(y_{i}\in[1,2,\ldots,C]\), \(C\) is the total number of classes, and \(n_{j}\) is the number of samples in class \(j\), where \(\sum_{j=1}^{C}n_{j}=n\). \(\mathrm{IF}=\frac{max(n_{j})}{min(n_{j})}\) measures the degree of imbalance in the long-tailed training set. For long-tailed image recognition, \(\mathrm{IF}\gg 1\). The estimated class probabilities of the model are \(P=\{p_{1},p_{2},\ldots,p_{C}\}^{\top}\), where \(p_{i}\in[0,1]\forall i\). We assume learning a classification network \(f(\cdot;\Theta)\), where \(\Theta=\{\theta_{i\ell}\}\) is the \(k^{th}\) filter weights at layer-\(l\). We denote \(\theta_{j}\) as the classifier weights of class-\(j\) and optimize \(\Theta\) by minimizing the loss \(L\) throughout the entire training set \(D\) to train the network:  
\[\Theta^{*}=\underset{\Theta}{arg\underset{\Theta}{min}}F(\Theta;D)\equiv\sum_ {i=1}^{n}L(f(x_{i};\Theta),y_{i}) \tag{1}\]  
The naive classifier is influenced by the long-tailed distribution of \(D\), and the classifier weights \(\theta_{j}\) leans more towards common classes. Therefore, our motivation is to learn a balanced classifier by transferring the weights of common classes to rare classes, balancing the performance of the classifier by combining the advantages of two losses and further balancing classifier weights of common and rare classes by regularizing classifier weights. The specific implementation process can be seen in Figure 2.
# Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images
## 5 Experiments and Results
### Main Contributions  
The present work offers several contributions to the state of the art. The research questions proposed in Section 3 are answered. The most important contributions are listed below.  
* To the best of the authors' knowledge, it is the first time that the YOLO model is used to detect sunspots in images from the OGAUC spectroheliograph. The results show that the YOLO model is up to the task and achieves performance slightly higher than the methods previously used.
* Pre-processing and marking images for deep learning is a tedious and very time-consuming task. Therefore, the authors make the dataset available for other researchers. It is available at Kaggle ([[https://www.kaggle.com/datasets/aaisiec/ogauc-solar-images-marked-for-yolo-cnn-cycle-24/](https://www.kaggle.com/datasets/aaisiec/ogauc-solar-images-marked-for-yolo-cnn-cycle-24/)]([https://www.kaggle.com/datasets/aaisiec/ogauc-solar-images-marked-for-yolo-cnn-cycle-24/](https://www.kaggle.com/datasets/aaisiec/ogauc-solar-images-marked-for-yolo-cnn-cycle-24/)) (accessed on 26 April 2023)).
## 2 Results and Discussion
### Discrimination Between CP and GCP Based on Multivariate Analysis
The root-mean-square error of cross-validation (RMSECV) was used to evaluate the deviation between predicted and observed Y values, by summarizing the cross-validation residuals of observations in the dataset, and had a measurement unit of 0.164. A plot of the coefficients (shown in Figure 3b) displayed regression coefficients related to scaled and centered X-variables, which enabled the OPLS model to be rewritten as a regression model. This scaling of the data made the coefficients comparable, expressing how strongly the Y-variable was correlated with the systematic part of each X-variable. With the established OPLS-DA model, more sample data could be imported for discrimination. Figure 3: Orthogonal partial least-squares-discrimination analysis (OPLS-DA) models for the classification of CRP. (**a**) Score plot of the OPLS-DA results. Circles represent GCP samples, and triangles represent CP samples. (**b**) Plot of the coefficients for the OPLS-DA. The constant not displayed in the plot was 1.150. To screen potential chemical markers that contributed to the differences between CP and GCP samples, seven compounds with variable importance in projection (VIP) scores greater than 1.2 were listed (shown in Table 3), demonstrating that they possessed the most influence on the discrimination between both types of CRP.
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 2 Materials and Methods
### Modeling
#### 2.4.2 Data Compilation  
An initial training data set was created by combining the observed temperatures and the focal buffers. This process involved the extraction of values from each of the 150 focal buffers at the locations of each of the observed temperature points. First, all 20 m\({}^{2}\) band derivatives were resampled to 10 m\({}^{2}\) in order to assess all variables on a pixel-by-pixel basis. In order to ameliorate some of the resolution discrepancies between the 1-s point collection, and 10 m\({}^{2}\) (such as instances where multiple points would fall in the same pixel), bilinear interpolation was performed when assessing focal buffer values for each point. This practice recalculates values based on neighboring pixel values in a weighted manner dependent on the location of the temperature observation point within the pixel. The output of this analysis was a table with 151 variables (observed temperature and the values of each focal buffer) and a row for each observation. Overall, nine tables were created: for each study area, we built a separate table (and subsequent model) for each temperate collection period (morning, afternoon, and evening).
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 2 Related Works
### Single-Stage Track-by-Attention Methods  
Single-stage track-by-attention methods are one of the recent trends in multiple object tracking. It simplifies the complicated association part from the track-by-detection paradigm, forming an end-to-end architecture that only contains few hyper-parameters.  
MOTR [11] and TrackFormer [28] both introduce such end-to-end architecture by processing object and track queries with the same decoder and then matching them with different strategies. During training, outputs from object queries are matched only with newborn tracklets and outputs from track queries are matched by looking up their instance IDs in ground truth objects. MOTRv2 [29] further utilizes the existing detection model by using cosine positional embeddings of object boxes predicted by a high-quality pretrained detection model to initialize the object query. However, because of the order-independent characteristic of the transformer, all queries are equally processed with the same decoder weights in these models, discarding the fact that they are optimizing towards different targets. Although information can be shared to remove duplicate detection and improve track quality, the model has to distinguish the query type implicitly to optimize for different targets, leading to sub-optimal performance.  
Compared with end-to-end methods, we separate the feed-forward network and detection heads to decompose object queries and track queries, allowing the model to learn different representations for different optimize targets.