esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data  
Google-Earth images have shown great potential in monitoring CHS for its global coverage, the choices of various spatial resolution, the availability of historical data, and low costs [20]. Therefore, the bi-temporal VHR Google-Earth images were used to monitor land-cover changes in the study areas. The year of 2015 was used as the benchmark year since SDGs were proposed in 2015, and the year of 2020 was chosen as the second time. The changes in cultural landscapes within five years were monitored. The core zone and the buffer zone consist of the main protected area of a CHS. The core- and buffer-zones' data [29] released by cultural-relic units were used to provide boundary information.  
Google-Earth imagery is actually a mosaic of many images with different spatial resolutions (from 15 m to 10 cm) from different time periods and multiple image providers such as Landsat satellites operated by National Aeronautics and Space Administration (NASA), the United States Geological Survey (USGS), and some commercial providers [30]. Google Earth Pro provides a tool to output the VHR imagery with three multispectral bands (i.e., red, green, and blue). The true digital number values (also including the spectral reflectance) are not available; thus, the Google-Earth imagery is not suitable for quantitative applications. Nevertheless, comparing with the costs of commercial VHR imagery and extensive image pre-processing work (e.g., image mosaicking) for global monitoring, Google Earth Pro is rather cost effective, and the output three-band VHR imagery can still be used for visual applications such as land-cover classification [25] and object recognition [27]. Google Earth Pro can output images with different levels of resolution. At the same level, the resolution of the output images may vary in different  
Figure 1: The study areas of three cultural-heritage properties.  
places, and not all the places have sub-meter images covered. Since elaborate monitoring of the cultural landscape of CHS was the primary goal in this study, we chose 1 m images for the Yungang Grottoes and the Chengcun Ancient City Ruins, and the actual resolution of the output images was 1.07 m. As the Wooden Pagoda of Yingxian has a rather small protected area and dense residential areas, we chose the sub-meter resolution image for this area, and the actual output resolution was 0.27 m. The original satellite sensors that provide these Google-Earth images can be tracked according to the covered area and the acquisition time. The data information is listed in Table 1.  
For global monitoring, the selection of resolution directly determines the processing time. If a CHS occupies a very large area, imagery with a lower resolution (e.g., 5 m) is suggested to reduce computational costs. In contrast, if a CHS occupies a small area and is in an urban environment, such as the Wooden Pagoda of Yingxian, imagery with a sub-meter resolution can be used if available.
## 4 Behavior of Distributed versus Concentrated Local Control Strategy in Different Real Grids
### Description of the Real Grids  
To see the effects of the various control strategies on a real LVG, two different grid types were selected: urban and rural. Figure 18 depicts the schematics of the different real LVGs. Figure 18a shows a simplified one-line diagram of a typical urban LVG with nine main feeders. The longest feeder is 1.27 km long, while the shortest is 0.305 km long. In this relatively large grid with a 96% cable share, 175 urban residential customers are connected. The LVG is connected to the MVG through a 20 kV/0.4 kV, 630 kVA DTR. Figure 18b presents a simplified one-line diagram of a typical rural LVG with four main feeders. The longest feeder is 1.63 km long, while the shortest is 0.565 km long. In this grid with a 59% cable share, 61 rural residential customers are connected. This LVG is connected to the MVG through a 20 kV/0.4 kV, 160 kVA DTR.  
Each prosumer is modeled by means of a ZIP load and a PV facility. The ZIP model is described as follows:  
\[P(U)=\mathrm{P}_{0}\cdot(1.2-1.17\cdot U+0.96\cdot U^{2}) \tag{23}\]  
and:  
\[Q(U)=\mathrm{Q}_{0}\cdot(4.88-10.16\cdot U+6.28\cdot U^{2}), \tag{24}\]  
where \(\mathrm{P}_{0}\) and \(\mathrm{Q}_{0}\) are the active and reactive power of the load at nominal conditions.  
Figure 17: Exchanged reactive power as a function of the bus number or feeder length for the \(Q(U)\) and \(L(U)\) control strategies.  
Figure 18: Schematic presentation of different real low-voltage grids (LVGs): (**a**) urban and (**b**) rural.  
The PV facilities are modeled with a peak active power production of 5.0 kW and an inverter rating of \(S_{inv-nom}\) = 5.0/0.9 kVA. Inverters are associated with a local control strategy as follows.
## 5 Conclusions  
Based on the rutting, moisture-induced damage, and workability test results, the following conclusions were drawn:  
* The mix design properties (VTM, VFA, and VMA) of CB, CRMB, and PMB mixtures were well within the requirements. A lower density was observed with the CRMB and PMB mixtures compared to the CB mixture.
* The workability and viscosity results indicate that the addition of modifiers reduces the pumping ability and workability of the asphalt binder and mixtures, respectively. Further, the CRMB exhibited a better rutting resistance and was less workable compared to the PMB mixture.
* The rut depth and dynamic stability results indicate that the addition of modifiers increases the rut resistance due to the fact that the CRMB and PMB mixtures exhibited higher VMA values, and also the addition of modifiers resulted in a stiffer mixture. However, CRMB exhibited a better rutting resistance compared to the PMB mixture.
* The MSCR results indicate that the addition of modifiers resulted in changing the vehicular loading grading to a higher traffic loading condition. Further, the addition of modifiers resulted in an increase in the stress sensitivity towards the varied axle loading pattern, but this was within the requirements.
* The ITS results indicate that the addition of modifiers increases the resistance of the moisture-induced damage of HMA mixtures, which is attributable to the higher binder content, which increases the film thickness, making the mixture rut-resistant. Further, the PMB mixtures had increased resistance to moisture-induced damage, while the effect of CRMB mixtures was negligible compared to that of CB mixtures. However, the ISR values of the CB, CRMB, and PMB mixtures fulfilled the minimum TSR requirements.
* The BBS results indicate that the addition of modifiers increases the bond strength of the asphalt binders. Further, the CRMB and PMB binders had a negligible effect on the bond strength properties.
* Additionally, the effects of modifier types were found to be statistically significant in relation to the workability, rutting, and moisture-induced damage properties.  
**Author Contributions:** Conceptualization, methodology, original draft preparation, conducting experimental tests--J.H.; writing--review and editing, Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Biomarkers Analysis
#### 3.3.7 C-Reactive Protein (CRP) Levels  
Estimated marginal means of C-reactive protein (CRP) concentrations at baseline (t1), 1 h (t2), 2 h (t3), and 4 h (t4) are presented in Table 10 and Figure 8. Baseline CRP levels were low and comparable across groups: CTRL (0.192 \(\pm\) 0.045 mg/dL), OLT (0.259 \(\pm\) 0.057 mg/dL), and OLB (0.250 \(\pm\) 0.045 mg/dL).  
CRP concentrations remained stable throughout the 4 h observation period, with minimal fluctuations within each group. For instance, in the CTRL group, CRP values varied slightly from 0.192 \(\pm\) 0.045 mg/dL at baseline to 0.190 \(\pm\) 0.039 mg/dL at 4 h. Similar stability was observed in the OLT and OLB groups.  
No significant group-by-time interaction was found (\(p\) = 0.854), indicating no differential temporal effect among groups. The overlapping 95% confidence intervals across all time points further support the consistency of CRP levels over time (e.g., t4: CTRL [0.110, 0.270], OLT [0.142, 0.371], OLB [0.155, 0.327]).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **CRP** & **CRP** & **CRP** & **CRP** & **95\% Confidence Interval** & **\(p\) Value** \\  & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **Time** & **Lower** & **Upper** \\  & **at Baseline (t1)** & **at 1 h (t2)** & **at 2 h (t3)** & **at 4 h (t4)** & **Time** & **Bound** & **Bound** \\ \hline CTRL & 0.192 \(\pm\) 0.045 & 0.192 \(\pm\) 0.042 & 0.190 \(\pm\) 0.041 & 0.190 \(\pm\) 0.039 & t1 & 0.099 & 0.284 \\  & & & & t2 & 0.104 & 0.280 \\  & & & & t3 & 0.106 & 0.275 \\  & & & & t4 & 0.110 & 0.270 \\ \hline OLT & 0.259 \(\pm\) 0.057 & 0.256 \(\pm\) 0.056 & 0.255 \(\pm\) 0.055 & 0.257 \(\pm\) 0.055 & t2 & 0.141 & 0.371 \\  & & & & t3 & 0.140 & 0.369 \\  & & & & t4 & 0.142 & 0.371 \\ \hline OLB & 0.250 \(\pm\) 0.045 & 0.252 \(\pm\) 0.043 & 0.249 \(\pm\) 0.044 & 0.241 \(\pm\) 0.042 & t1 & 0.158 & 0.343 \\  & & & & t2 & 0.163 & 0.341 \\  & & & & t3 & 0.158 & 0.340 \\  & & & & t4 & 0.155 & 0.327 \\ \hline \end{tabular}
\end{table}
Table 10: Postprandial changes in serum CRP levels.  
Figure 8: Postprandial CRP concentrations (Mean \(\pm\) SE) over time (baseline, 1 h, 2 h, 4 h) in control (CTRL), olive leaf tea (OLT), and olive leaf biscuit (OLB) groups.  
Overall, CRP levels remained low and unchanged during the study, suggesting no acute inflammatory response related to the interventions.
## Appendix A Rotation Matrix  
In three-dimensional Euclidean space, one coordinate system can reach another coordinate system through some rotations; this process is called coordinate transformation. The rotation of a coordinate system about one of its axes leads to a new coordinate system, and this process is called primitive rotation. In this paper, if the tether coordinate system is rotated by \(\alpha\) angles about the Y-axis to form a new frame in the inertial coordinate system, the rotation can be represented by a matrix, which is called the rotation matrix \(A_{y}(\alpha)\). The mathematical expression of \(A_{y}(\alpha)\) is  
\[A_{y}(\alpha)=\begin{bmatrix}cos(\alpha)&0&-sin(\alpha)\\ 0&1&0\\ sin(\alpha)&0&cos(\alpha)\end{bmatrix} \tag{20}\]  
Similarly, if the tether coordinate system is rotated by \(\theta\) angles around the Z-axis, its rotation matrix is  
\[A_{z}(x)=\begin{bmatrix}cos(x)&sin(x)&0\\ -sin(x)&cos(x)&0\\ 0&0&1\end{bmatrix} \tag{21}\]  
Just like tether L\({}_{1}\), some transformations are used to gain the motion of tether L\({}_{2}\)  
\[\begin{split}& T_{1}=A_{y}(\alpha_{2})A_{z}(\theta_{2})A_{z}(- \theta_{1})A_{y}(-\alpha_{1})\left[\begin{array}{c}T_{1}\\ 0\\ 0\end{array}\right]\\ &=\left[\begin{array}{c}(\cos(\alpha_{1})\cos(\alpha_{2})\cos(\theta_{1}- \theta_{2})+\sin(\alpha_{1})\sin(\alpha_{2}))T_{1}\\ (\cos(\alpha_{1})\sin(\theta_{1}-\theta_{2}))T_{1}\\ (\cos(\alpha_{1})\sin(\alpha_{2})\cos(\theta_{1}-\theta_{2})-\cos(\alpha_{2}) \sin(\alpha_{1}))T_{1}\end{array}\right]\\ \\ &\frac{G_{1}}{m_{1}}-\frac{G_{2}}{m_{2}}=\omega^{2}l_{2}\left[ \begin{array}{c}1-3\cos^{2}\theta_{2}\cos^{2}\alpha_{2}\\ 3\cos\theta_{2}\sin\theta_{2}\cos\alpha_{2}\\ -3\cos^{2}\theta_{2}\sin\alpha_{2}\cos\alpha_{2}\end{array}\right]\end{split} \tag{10}\]  
Substituting Equations (11), (12) and (10) into Equation (11), we obtain equations for angle \(\theta_{2}\), angle \(\alpha_{2}\), and \(l_{2}\) that describe the pitch motion, the roll motion, and the change in tether 2 in the orbital plane as Equations (12)-(14).
## 2 Related Work  
A number of rat sleep scoring methods have been proposed. [PERSON] introduced the first algorithm for rodent sleep stage scoring [9]. In most of the existing methods, a subset of data for each subject's sleep stage analysis is prepared with human interaction to identify wake-sleep state cut-off or to perform manual sleep scoring, but the employed techniques or methods can differ. Manual sleep scoring is annotated by the sleep expert, whereas machine and deep learning models have the ability to classify the sleep stages automatically without intervention by a human. The conventional sleep scoring methods extract frequency domain features by employing the fast Fourier transform [10; 11; 12].  
In contrast, in unsupervised learning, the sleep stages are discriminated by using rules, possibly embedded in a trained artificial neural network or in a Bayes net. Few unsupervised algorithms have been proposed, but they can be computationally intensive and have only been tested on a limited number of animals. The various classification models used to handle features and sleep stages include Support Vector Machines [11], Hidden Markov Model, and LSTM (which is designed for time series and employs sleep transition rules to obtain a high level of accuracy) [13].  
Numerous studies have pursued automated sleep scoring. The authors [14] proposed MC-SleepNet, which is based on the convolutional neural network and long-short-term memory (LSTM) and attained 96.6% accuracy. Another author [8] introduced a convolutional neural network (CNN) architecture for automated sleep scoring in mice and accomplished an F1-Score of 0.95. The study [15] compared machine and deep learning models for automated sleep scoring of rodents, both mice and rats. They obtained accuracies within 0.77-0.83 for the rodent with a convolutional neural network (that was compared to Random Forest (0.78-0.81)). Another approach known as sleep scoring artificial neural network for rodents achieved 96.8% accuracy [16].  
To the best of our knowledge, numerous studies have discussed sleep stage classification accuracy, but the relationship between the sleep stages and model performance is not discussed. Furthermore, the effect of electrode position on model performance is not dealt with in the literature. A hybrid approach combining CNN and LSTM for automatic sleep stage classification is discussed in [17].  
In order to fill the gap, we have employed the LSTM neural network to improve the classification accuracy of binary or multiple sleep stages, along with comparison of single or multiple electrode positions.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 6 Conclusions  
### Summary and Conclusions  
This paper introduces UC-CMAF (User Comment-Guided Cross-Modal Attention Fusion), a novel framework designed for interpretable multimodal fake news detection in the digital era. Our primary contribution lies in developing a pioneering fusion architecture that effectively integrates news text, images, and user comments through adaptive cross-modal attention mechanisms. Critically, we treat user comments as a primary modality rather than auxiliary data, recognizing their essential role in understanding the broader social context of information dissemination.  
The experimental evaluation demonstrates UC-CMAF's superior performance across multiple benchmark datasets. Our framework achieves remarkable results with 92.7% accuracy and 89.4% F1 Score on the ReCOVery dataset, and 93.1% accuracy with 90.9% F1 Score on MM-CoVaR, significantly outperforming existing state-of-the-art methods. Beyond performance metrics, UC-CMAF addresses the critical demand for explainable AI through comprehensive multi-head attention heatmaps and comment importance visualizations, while maintaining robust performance against adversarial attacks with defense effectiveness rates ranging from 81.1% to 91.3%.  
Our comprehensive ablation studies provide compelling evidence that user comments contain unique contextual and social information that extends far beyond traditional content-based analysis capabilities. This finding validates our architectural decision to elevate user comments to primary modality status and underscores the importance of incorporating social validation signals into misinformation detection systems. UC-CMAF's consistent superior performance across diverse datasets, coupled with its robust interpretability mechanisms and strong adversarial resistance, represents a significant advancement in the field of multimodal fake news detection.  
The framework successfully demonstrates how AI systems can effectively combine rigorous content analysis with social validation while maintaining comprehensive interpretability--a critical requirement for deployment in real-world scenarios where trust and transparency are paramount. This work establishes several important directions for future research: (1) development of real-time processing capabilities for immediate misinformation detection, (2) cross-platform generalization to handle diverse social media environments, and (3) implementation of adaptive mechanisms capable of responding to emerging manipulation techniques.  
By successfully integrating social context with sophisticated multimodal analysis, UC-CMAF offers a new paradigm for developing robust, explainable verification systems capable of combating increasingly sophisticated misinformation campaigns. As digital misinformation continues to evolve in complexity and scale, frameworks like UC-CMAF provide essential foundations for maintaining information integrity while preserving the transparency and accountability necessary for fostering public trust in AI-assisted fact-checking systems.
# AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
## 4 AIS Solutions' Implementation
### IoT System Architecture  
In this work, we used AWS to create the desired architecture for the implementation. We introduce the main services used in this section and a brief description of each service [35].  
* Amazon Elastic Compute Cloud (Amazon EC2) provides scalable computing capacity in the AWS Cloud. Users have total control over the EC2 configuration as it is not an AWS managed service.
* Virtual Private Cloud (Amazon VPC) creates a logically isolated virtual network when launching AWS resources. This service provides an extra layer of security to the implementation by using public and private subnets, and network access control list configuration.
* AWS IoT Core enables IoT devices connected to the AWS cloud. It supports a large number of devices and messages by providing reliable and secure services.
* Device Gateway: the entry point for IoT devices connected to AWS
* AWS Lambda runs programming code in a serverless computing service in response to events and automatically manages the underlying computing resources.
* Cloudwatch: monitoring and management services for AWS resources.  
In this implementation, since we had total control over the configuration of the EC2, we used it to configure the IoT device to be connected to the network. We created two system architectures (see Figures 4 and 5) using AWS to run this experiment to mirror real system scenarios and conduct a performance analysis.
# Integration of EMU Overall Design Model Based on Ontology-Knowledge Collaboration
###### Abstract
In complex equipment research, the National University of Defense Technology introduced KGs into complex equipment requirement perception and development research, constructing a requirement KG ontology and conducting research based on new energy vehicles, proving that the proposed method can not only simplify the requirement analysis of complex equipment and achieve automated requirement analysis to a certain extent but also lay the foundation for knowledge management of complex equipment throughout its lifecycle. It can be seen that KGs have efficient data integration capabilities and advantages in visualization display, intelligent reasoning, and easy maintenance and updating. Therefore, to achieve the complex overall design of an EMU, this paper proposes an EMU overall design model integration method based on an ontology-knowledge collaboration. The main contributions to this paper are as follows:  
1. This paper develops an EMU ontology based on the top-level ontology to capture requirements and architecture. The EMU functional meta-model and physical architecture meta-model are built based on the EMU MBSE model and their ontology models are generated. 2. A model integration method based on ontology-knowledge collaboration is proposed. Utilizing the ontology construction tool Protege, the requirements, architecture, and traceability of an EMU are established top-down by constructing a top-level domain ontology framework. On this basis, a knowledge graph is constructed to address issues such as semantic heterogeneity and diverse data sources in an EMU, thereby achieving knowledge reusability. 3. Taking the PIS and TS systems of an EMU as examples, the SysML models are used as input. The requirements ontology is constructed through ReqIF, the architecture ontology is established using GOPPRR and MDA meta-model construction methods, and the traceability ontology is built using elements of the traceability matrix. Finally, the knowledge graph is constructed bottom-up using the Neo4j database. These two EMU systems are used to verify the feasibility and effectiveness of the proposed method in the overall integration of an EMU.
## 5 Simulation Setup
### Parallax Measurement Setup  
In this section, we show effectiveness of CS over a range of \(\delta_{\tau}\) and \(\delta_{\beta}\) as described in equations. We vary \(\Phi\), the space-flight instrument orbital phase to span over a range of values of \(\delta_{\tau}\) and \(\delta_{\beta}\). Our microlensing parallax, \(\pi_{E}\), is given by Equation (20). From [8], we make the same assumptions of the source being located in the galactic bulge at 4 kpc, lens at 8 kpc with a relative lens-source speed of 200 km/s to obtain the value of \(\pi_{E}\).  
Hence, in a simple case, with origin as the center of the satellite trajectories, we can write  
\[\delta_{\tau} = \pi_{E}\epsilon_{\parallel}cos\Omega \tag{17}\] \[\delta_{\beta} = \pi_{E}\epsilon_{\perp}sin\Omega \tag{18}\]  
To generate our parallax measurements, we make the assumptions as in [8]:  
1. Source is in the galactic bulge: \(D_{s}\) = 8 kpc
2. \(D_{L}\) = 4 kpc
3. \(\mu_{rel}=200\dfrac{km}{s}\)  
We can write \(t_{E}\mu_{rel}=\theta_{E}\)[21, 22].  
\[\pi_{E} = \dfrac{AU(D_{LS})}{t_{E}\mu_{rel}D_{L}D_{S}} \tag{19}\] \[= \dfrac{AU(0.000624s)}{t_{E}} \tag{20}\]  
We can use Equations (11) and (13) with the given value for \(\pi_{E}\) in Equation (20). Our simulations vary the value of \(R\) and \(\Phi\) to determine the effect of CS reconstruction on photometric curves with a microlensing parallax. We use \(R\) values as shown in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Traditional Detectors** & **CS Detectors** \\ \hline CCD Detectors & Typically designed with spatial light modulators and photodiode \\ \hline Pixel by pixel readout of the image & Total power reflected from the matrix projected onto the image is measured \\ \hline Digitization of each pixel readout & Digitization of the total power read \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between traditional detectors and CS detectors.  
The different R values approximately correspond to the type of orbit the constellation could be in: low earth orbit, geosynchronous orbit, and solar orbit, respectively [8,23]. We use eight equally space \(\Phi\) values. Hence, our results could show the effect for any constellation spaced at the given \(\Phi\) values: \(\frac{\pi}{8}\), \(\frac{\pi}{4}\), \(\frac{3\pi}{8}\), \(\frac{\pi}{2}\), \(\frac{5\pi}{8}\), \(\frac{3\pi}{4}\), \(\frac{7\pi}{8}\), \(\pi\).  
A \(t_{E}\) value of 1 day depicts photometric curves due to free floating planets.