esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experimental Setup
#### 4.1.2 Mask Generation from the Ground Truth  
In our experiment, we combined ground-truth bounding boxes with SAM, which was inspired by UVOSAM [52]. This combination resulted in what we call a \"heuristic mask\", which is essentially a mask created using a ground-truth bounding box. The main purpose of this experiment was to evaluate SAM's performance in comparison with the ground-truth bounding boxes.  
Furthermore, we introduced the concept of the \"GTBox\" in our study, representing the ground-truth bounding box for each object. These GTBoxes are derived from the provided annotations, where for each object in every frame, we calculated the GTBoxesby determining the maximum and minimum x and y coordinates for each object. In cases where no object is present in the image frame, 'none' is returned.  
We then utilized these generated GTBoxes to assess the performance of SAM. This approach was adopted because our framework acknowledges that detectors may sometimes produce inaccurate bounding boxes or miss objects entirely. By supplying SAM with GTBoxes instead of relying solely on detectors, we can achieve the highest possible score with SAM. This score serves as an 'upper limit' benchmark for our final evaluation.
## 5 Simulation Result Analysis and Discussion
### LVRT Enhancement by the Proposed Control Scheme under an Asymmetrical Fault  
In this section, the proposed control strategy is verified under two different types of asymmetrical faults--single-line to ground fault (1 LG) and two-line to ground fault (2 LG).
# Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
## 3 Short-Term Load Forecasting Model Based on OS-ELM
### Forecasting Model  
The first two months of data is used as training data and the last month of data is used as testing data. Besides these smart meter data, the local historical temperature data are obtained from [37] (the highest temperature, the lowest temperature, and average temperature). Considering that all various factors (such as temperature, date type, etc.) have an effect on user loads, the data sample of load forecasting is constructed as shown in Table 1.  
As shown in Table 1, every data sample is composed of 12 variables (because date type has already been considered in the sample construction, date type is not added to the sample as a dimension of the data).  
After clustering the chosen experimental data, the users are classified into different clusters. The sum of the user electricity load from every cluster is taken as the partial system load of the corresponding cluster, and then load forecasting is made for every cluster. The sum of the partial system load is taken as the total system load forecast.  
The first two months of data from the sample was chosen to be the training sample and the last month of data was chosen to be the testing sample. The experimental objective was to forecast the user electricity load every 30 min within the period of one day. The specific model flowchart is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimension** & **Variables** & **Description** \\ \hline \(x_{1}-x_{4}\) & Historical Load & The load from two hours before the current time \\ \hline \(x_{5}-x_{9}\) & Historical Load & The load from 5 days with the same date type and time before the current time \\ \hline \(x_{10}-x_{12}\) & Temperature & The highest temperature, the lowest temperature, and average temperature of the current day \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample construction method.
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 3 Materials and Methods
### Study area  
The study area corresponds to the territory that extends from the O'Higgins regions to Bio-Bio (33.8\({}^{\circ}\)-38.4\({}^{\circ}\) S) in Chile (Figure 1). This area has a long history of wildfire occurrence [29], explained by climatic conditions, economic activities and population concentration [30]. Said area presents a semiarid Mediterranean climate, with most rainfalls occurring during the winter period, reaching 350-1300 mm per year, causing a long dry season with low relative humidity, high temperatures and very low precipitations [31]. Since 2010, due to global climatic change, this area has been suffering a prolonged and severe mega drought [32]. The area's natural vegetation is dominated by Mediterranean shrublands and sclerophyllous forests [33]. Moreover, it presents a high presence of land cover areas created by human action, including forest plantations, agricultural farms and wildland-urban interfaces, where human activities make them likely to suffer from wildfires. The period chosen for this study is the 2016-2017 season, during which the phenomenon of mega wildfires in Chile took place. Their magnitudes and intensities forced the creation of an additional level in the scale of measurement used for the classification of wildfires [34]. The damages recorded affected 596 thousand hectares among O'Higginsand Bio-Bio. The fire destroyed forests, plantations, agricultural farms, meadows and other vegetation masses, affecting more than 2500 homes [35].
# Detecting Disinformation in Croatian Social Media Comments
## 4 Model Evaluation on Croatian Social Media Dataset
### Expanding the Framework: Testing with croBERT
The confusion matrix for the croBERT model (shown in Figure 9), compared against the confusion matrices of the Support Vector Machine (SVM), Random Forest (RF), and neural network (NN) models, also shows significant improvements when using this approach. The croBERT model achieved a significantly higher F1 score (0.893), with an accuracy of 0.9 and recall of 0.9, outperforming the traditional machine learning models across all key metrics. While the SVM, RF, and NN models struggled with false negatives, croBERT demonstrated a more balanced performance, correctly identifying a larger proportion of true positives. For instance, SVM misclassified 260 positive samples as negative, while croBERT reduced this error considerably. Additionally, croBERT maintained a lower number of false positives compared to the RF and NN models, indicating its superior ability to distinguish between disinformation and non-disinformation content. These results highlight the effectiveness of transformer-based models in handling complex language patterns, as seen in the FRENK 1.1 dataset, which focuses on detecting disinformation in Facebook comments related to immigrants and LGBTQ+ individuals. The ROC curve for the croBERT model, shown in Figure 10, illustrates the trade-off between the true positive rate (TPR) and false positive rate (FPR) across various classification thresholds. The curve rises steeply toward the top-left corner, indicating strong discriminatory power. The area under the curve (AUC) is 0.92, reflecting the model's high ability to distinguish between disinformation and non-disinformation instances. If we compare this to the traditional models like the SVM (where the AUC was 0.78), croBERT demonstrates a substantial improvement, likely due to its contextual language understanding and pre-training on large-scale datasets. This enhanced performance confirms the effectiveness of transformer-based models in capturing the complex linguistic patterns present in the dataset. This result represents an increase of over 15 percentage points, highlighting the advantages of transformer-based architectures in capturing complex linguistic patterns and contextual relationships--critical components for accurately detecting disinformation. Additionally, croBERT achieved an accuracy of 90% and a recall of 90%, further supporting the model's ability to balance precision and recall. The loss values followed a stable downward trajectory, with the training loss reducing from 0.67 to 0.27 and the evaluation loss dropping from 0.61 to 0.34 by the final epoch, as shown in Figure 11. These results reinforce the notion that transformer models, like croBERT, outperform traditional techniques by leveraging their deep contextual understanding of language, which is especially valuable in the often subtle manipulations found in disinformation narratives. Figure 9: CroBERT confusion matrix.
# Co-Seismic Magnetic Field Perturbations Detected by Swarm Three-Satellite Constellation
## 5 Conclusions
A larger constellation of Low Earth Orbit satellites equipped with magnetometers could improve the coverage, i.e., the capability to record and deeper understand this type of signal. The recent launch of the Chinese Seismo-electromagnetic satellite in 2018 could contribute to this endeavour greatly. The following are available online at [[http://www.mdpi.com/2072-4292/12/7/1166/s1](http://www.mdpi.com/2072-4292/12/7/1166/s1)]([http://www.mdpi.com/2072-4292/12/7/1166/s1](http://www.mdpi.com/2072-4292/12/7/1166/s1)). Figures S1-S11: The output tracks where some possible co-seismic effects on magnetic field have been detected and one example where we do not found any evidence of possible earthquake-source disturbance. Conceptualization, D.M., and A.D.S. ; methodology, D.M. and A.D.S. ; software, D.M. and S.A.C. ; validation, S.A.C., G.C. and A.P. ; formal analysis, D.M. ; investigation, D.M. and A.D.S. ; resources, D.M., A.D.S., S.A.C. ; data curation, D.M. and S.A.C. ; writing--original draft preparation, D.M. and A.D.S. ; writing--review and editing, all authors; visualization, D.M. and A.D.S. ; supervision, A.D.S. ; project administration, A.D.S. and S.J. ; funding acquisition, A.D.S. and S.J. All authors have read and agreed to the published version of the manuscript. This research was funded by European Space Agency (SAFE project), Agenzia Spaziale Italiana (Limadou-science project), International Space Science Institute in Bern and Beijing (ISSI-BJ), \"The electromagnetic data validation and scientific application research based on CSES satellite\" project); the APC was funded by \"Startup Foundation for Introducing Talent of NUIST\" (Grant No. 2243141801036). We thank the European and Italian Space Agencies for funding these kinds of studies (see Funding). Part of this work has been performed for the project ISSI-BI \"The electromagnetic data validation and scientific application research based on CSES satellite\". We thank the anonymous referees and the editor, who, with their useful suggestions, have helped to improve our manuscript significantly. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results
### cfDNA Input Does Not Determine the EGFR Mutational Status  
The relationship between detection of \(EGFR\) mutations and cfDNA concentrations, a potential indicator of tumor shedding, was also examined.  
To this end, the variant calling analysis was performed by using the cfDNA variant caller plugin with parameters optimized for the \"Oncology-Liquid Biopsy\" application and in particular, variants were detected when it is found in at least 2 molecular families and when a minimum number of 3 reads with the same tag forms a functional family.  
Figure 4: Graphical representation of the Ion Torrent SuiteTM Software (TSS) sequencing data about libraries performance in all samples (n = 92). (**a**) Scatter plot of the positive correlation between the cfDNA input and the median number of DNA molecules tagged (MedMolCov) (r\({}_{\rm s}\) = 0.85; \(p\) < 0.0001). (**b**) Bar chart of the MedMolCov values divided into four ranges (<1500, 1500–2499, 2500–4000, >4000) and the relative MedReadCov values. (**c**) Scatter plot of the positive correlation between the MedReadCov and the Median Mol Cov (r\({}_{\rm s}\) = 0.57, \(p\) < 0.0001). (**d**) Scatter plot of the negative correlation between the median LoD values of each library and the initial cfDNA input (r\({}_{\rm s}\) = -0.41, \(p\) < 0.0001).  
It was observed that 61/92 (66.3%) samples were mutated in hotspot genomic positions within _EGFR_ gene (Table S1). In depth, 30/61 carried only one mutation, 30/61 two mutations, whereas only one patient (pt.43) had three mutations (Gly719 Cys, Set768 Ille and Thr790 Met). In all the patients whose plasma resulted in being informative, i.e., positive for the sensitizing EGFR mutations, the variants matched between primary tissue and the corresponding cfDNA (data not shown; see [16]).  
The cfDNA quantity, used for NGS libraries in mutated (6.37-59.80 ng) and unmutated (6.50-53.95 ng) samples was very similar. In addition, no significant correlation was found between the cfDNA input amount used for library preparation and the relative _EGFR_ mutational status (Mann-Whitney test, \(p=0.16\), Figure 5a).  
Furthermore, we showed that even the amount of cfDNA ng/mL plasma did not affect the positivity/negativity of _EGFR_ mutational testing (Mann-Whitney test, \(p=0.15\), Figure 5b).  
Together, these data demonstrate that although we need considerable yields of cfDNA to get more tagged DNA molecules and to intercept even single rare ctDNA molecules diluted within, the mutational detection rely possibly to different aspects mainly associated with biological variability intra- and inter-patients.
## 2 Materials and Methods
### In Vivo Activity
#### 2.6.4 Magnetic Resonance Imaging (MRI)  
In the animal experiment described in Section 2.6.1, following 2 h of ischemia and 22 h of reperfusion in the right cerebral tissue of rats, the model group, LA-1 group, and loganin group underwent MRI imaging. At the designated time points, the animals from each group were placed in a transparent anesthesia chamber and anesthetized using a mixture of 4% isoflurane and oxygen. The rats were monitored to ensure they remained calm. Once the animals were tranquilized, they were quickly removed and positioned on the scanning bed. During imaging, a constant-temperature heating system maintained the animals' body temperature at 37 \({}^{\circ}\)C. A respiratory monitoring pad was placed under the abdomen to track respiratory parameters. Imaging commenced when the respiratory rate stabilized at 50 breaths per minute. The experiment utilized the Pharmascan 7T small animal magnetic resonance imaging system (Bruker, Billerica, Germany). The T2-weighted imaging parameters were set as follows: Field of View (FOV) = 3.30 cm \(\times\) 3.30 cm, Matrix (MATRX) = 256 \(\times\) 256, Repetition Time (TR) = 5600 ms, Echo Time (TE) = 36 ms, Flip Angle = 180\({}^{\circ}\), Slice Thickness = 0.7 mm, Slice Distance = 0 mm, Number of Slices = 38, Acquisition Time (TA) = 8 min and 57 s, Number of Averages = 3. After imaging, the rats were returned to their cages to recover naturally.
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 4 Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter  
### State-Augmentation  
According to Equations (2) and (3), the actual measurement \(\mathbf{y}_{k}\) received by the filter is a mixture of \(\left\{\mathbf{z}_{k-s}\right\}_{s=0}^{d}.\) The derivation of \(\mathbf{y}_{k}\) needs the state vectors \(\mathbf{x}_{k},\mathbf{x}_{k-1},\ldots,\mathbf{x}_{k-d}\), which are then joined together. The augmented state vector is as follows:  
\[\mathbf{X}_{k}=\left[\begin{array}{c}\mathbf{x}_{k}\\ \mathbf{x}_{k-1}\\ \vdots\\ \mathbf{x}_{k-d}\end{array}\right]_{n_{a}\times 1} \tag{17}\]  
where \(\mathbf{X}_{k}\) denotes the \(n_{a}=(d+1)n\)-dimensional augmented state.  
The augmentation system is expressed by the following:  
\[\mathbf{X}_{k}=F(\mathbf{X}_{k-1})+\mathbf{C}_{\xi k-1} \tag{18}\]  
\[\mathbf{y}_{k}=\mathbf{z}_{k-s}=h(\mathbf{D}_{s}\mathbf{X}_{k})+\mathbf{w}_{k-1},\;s=0,1,\ldots,d \tag{19}\]  
where \(F(\mathbf{X}_{k-1})\), \(\mathbf{C}\), and \(\mathbf{D}_{s}\mathbf{X}_{k}\) are then computed by the following:  
\[F(\mathbf{X}_{k-1})=\left[f^{T}(\mathbf{x}_{k-1}),\mathbf{x}_{k-1}^{T},\ldots,\mathbf{x}_{k-d }^{T}\right]^{T} \tag{20}\]  
\[\mathbf{C}=[\mathbf{I}_{n\times n},0,\ldots,0]^{T} \tag{21}\]  
\[\mathbf{D}_{s}\mathbf{X}_{k}=\mathbf{x}_{k-s},\;s=0,1,2,\ldots,d \tag{22}\]
However, the use of dissolved salt impregnation for hardening pure Ti and the relationship between mechanical properties and the treating process have not yet been studied in detail. In addition, there is little research on the principle of formation and diffusion of the hardened layer by boriding with different processing temperatures. In this research, the diffusion rate of B in TiB and TiB\({}_{2}\) is discussed. In addition, growth morphology (TiB), B diffusion coefficient, and diffusion mechanism were researched. Regarding wear depth, the difference between the measured value and the calculated value was analyzed to research the wear amount of the friction pair. The aim of this study was to synthesize a continuous thick boride layer on grade-2 pure Ti by boriding in a furnace using the molten salt impregnation method. The effects of the heat-treatment condition (boriding temperature) on the formation, microstructure, and mechanical and tribological properties of the borided layer were investigated. In the present study, no additive agent was used to accelerate the boron diffusion process because the additive agent may hinder the diffusion of boron atoms through the surface layer to the Ti substrate. To understand the development of the borided layer, the Gibbs free energy of possible boriding reactions with titanium was calculated using the NIST-JANAF thermochemical tables. The actual value of hardened layer thickness was compared to the calculated value to study the formation of the hardened layer. The borided layer formed on the grade-2 pure Ti surface provided a protective coating against serious wear loss during the service period. Therefore, the correlations between boriding temperature and properties, including the fricative value, are discussed in this research. The actual value of wear depth was compared to the calculated value, and the form of wear was analyzed and studied in detail.
## 5 Conclusions and Future Recommendation  
A novel two phase reverse image search methodology, namely RIS-DUGL, based on deep learning techniques and transfer learning, is proposed. RIS-DUGL uses two deep neural network models sequentially. The first phase works in an unsupervised manner by using one of the deep generative models called Sparse AE and performs representational learning. In the second phase, the final feature extraction is done using a deep convolutional neural network called VGG-16. The proposed method exploits RGB channel-wise information, which provides rich representational learning and enhances the generalization ability by overcoming various content-based image retrieval (CBIR)/digital image challenges. RIS-DUGL practically employs the effective transfer learning approach by fine-tuning the already trained similar model to enhance the efficiency and reduce the trouble of training a model from scratch. This research activity is evaluated by a hybrid dataset comprising of 12 diverse categories collected from three publicly available datasets and using the most stable cosine distance as a similarity measure. Experiments reported 98.46% accuracy. To check the model's effectiveness, 5% noise (Gaussian, Salt &Pepper, and Speckle) is added in the hybrid dataset, which reported 97%, 96%, and 97% retrieval accuracy, respectively. The future direction is to adapt the trending Deep Learning Models (such as Generative Adversarial Networks (GANs)) to exploit further achievements in RIS-DUGL. Furthermore, a large-scale dataset, including many examples, may be used with training instances.  
Conceptualization, A.K. ([PERSON]); data curation, S.A.Q.; formal analysis, A.K. ([PERSON]); funding acquisition, M.A. ([PERSON]); investigation, S.M.; methodology, M.I.; project administration, A.S.; resources, A.M.; supervision, M.R.A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData will be available upon request and can be accessed in collaboration with the corresponding author.  
The authors express their gratitude towards PIEAS Artificial Intelligence Centre (PAIC), DCIS: PIEAS, Department of INFS, UMT for providing infrastructure and a conducive environment for this research.  
The authors declare that they have no conflict of interest.
## 2 Layered Fault Tree Model for Smart Grids
### The Layered Fault Tree
#### 2.2.1 Construction of the Primary Fault Tree  
The primary fault tree is the outage-event tree for a specific load point, which is currently concerned. The top event of the fault tree should be the outage event of the concerned load. For a specific form of smart grid architecture, events that should be included in the primary fault tree are listed as follows:  
* Information subsystem failure.
* Communication subsystem failure.
* Intelligent substation failure.
* Protection subsystem failure.  
* Power supply failure.
* Failures of other devices depending on the architecture.  
The structure connected to the secondary fault tree is shown in Figure 2. An inhibit gate symbolized by a hexagon is used as the logical operation connected to the event of Utility Supply Failure, which is a condition event in form of ellipse. The triangle \(\alpha\) is used to draw forth the secondary layer of fault tree considering local supply in islanded mode.  
From the perspective of time, the event of Utility Supply Failure can be seen as the trigger of secondary fault tree. Under the circumstance that utility supply is failed, the secondary fault tree is linked upward. Then only when the event of Local Supply Failure occurs, can the top event be led to. With the utility supply recovered, link between the two layers of fault tree is disconnected and the considered local framework will go on working with utility supply, so the inhibit gate used here cannot be simply transformed to an AND gate, which is a common method to handle inhibit gates in traditional fault trees.