esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Postural Balance Parameters
#### 2.5.4 Areas  
To assess movement variability, we calculated three distinct area measures. These measures quantify the spatial dispersion of acceleration data.  
**Area under the acceleration magnitude curve**, or area under the curve, was calculated using the trapezoidal method, as shown in Equation (7).  
\[Area=\sum_{i=0}^{N-1}\frac{(a_{i}+a_{i+1})}{2}\times dt \tag{7}\]  
where \(a_{i}\) and \(a_{i+1}\) are consecutive acceleration magnitudes, and \(dt\) is the time interval between samples. This measure quantifies the cumulative acceleration over time, providing a temporal assessment of motion intensity. However, due to the discretization inherent in the trapezoidal method, this measure is more prone to errors than spatial metrics.  
**Ellipse area** was calculated by fitting an ellipse to the acceleration data, using the standard deviations of the accelerations along the X and Y axes as the semi-axes to quantify the distribution of motion in the horizontal plane. The calculation of the ellipse area is shown in Equation (8).  
\[Area_{ellipse}=\pi\times\sigma_{X}\times\sigma_{Y} \tag{8}\]  
where \(\sigma_{X}\) and \(\sigma_{Y}\) represent the standard deviations of acceleration data on the X and Y axes, respectively. Mathematically, \(\sigma_{X}\) and \(\sigma_{Y}\) represent the spread of the data in each direction. These deviations correspond to the semi-major and semi-minor axes of the ellipse that best fits the data. This approach was expected to provide a robust indicator of data dispersion [29]. A larger ellipse area indicates greater dispersion of acceleration values, suggesting a wider range of motion or more complex movement patterns.  
**Bounding box area** represents the minimal rectangular area that encloses all acceleration data points, calculated as shown in Equation (9).  
\[Area_{box}=(max_{X}-min_{X})\times(max_{Y}-min_{Y}) \tag{9}\]  
where \(max_{X}\), \(min_{X}\), \(max_{Y}\), and \(min_{Y}\) are the maximum and minimum acceleration values on the X and Y axes, respectively. Unlike the ellipse area, which provides a more precise representation of the distribution of acceleration data, the bounding box area has been known for its simplicity and computational efficiency, which is particularly suitable for real-time applications [30]. A larger bounding box area reflects a wider range of motion across a selected plane.  
For example, Figure 2 presents the 2D projections of acceleration data in the XY plane during task execution. All in all, these area measures provide complementary perspectives on movement variability, capturing both the concentration of movement (ellipse area) and the overall range of motion across different planes (bounding box area and area under the curve).  
It is worth noting that potential multicollinearity between these area measures was addressed during statistical analysis, and only one type of the three areas ended up being used in the prediction.  
Figure 2: The 2D projection of accelerations in the horizontal (XY) plane; example for task 10. The black lines envelope the Convex Hall Area.
## 4 Experiments
### Experiments and Evaluation Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Time Consumption (s)** \\ \hline Proposed & 98.97\% & 98.95\% & 99.47\% & 99.21\% & 0.437 \\ RNN & 71.58\% & 81.29\% & 73.16\% & 77.01\% & 0.583 \\ LSTM & 83.56\% & 88.48\% & 86.67\% & 87.56\% & 0.519 \\ SVM & 69.21\% & 72.77\% & 77.22\% & 74.93\% & 1.748 \\
1D-CNN & 78.07\% & 85.12\% & 81.38\% & 83.21\% & 0.364 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of results between the proposed network and other algorithms. Figure 9: Confusion matrix of the proposed model, with 0 to 4 referring to normal and abnormal of states of the air compressor, cutting equipment, mixing equipment and stirring kettle, respectively. CNN-LSTM model is better by all criteria, reaching 98% on average and meeting the needs of fault diagnosis in actual production.
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 5 Pointing Correction Verification
### Radar QPE and Verification with External Data
#### 5.2.1 Radar QPE Methodology  
Quantitative precipitation estimates are generated for this study using the processing methodology developed for NXPol-1 and previously used to assess QPE in northern Scotland [28; 29; 30]. The methodology first corrects for PBB with a high-resolution DEM (SRTM 1-arc second global data [31]) using the open-source Wradlib processing library [32]. Figure 6 shows the terrain altitude within 150 km of the radar location along with the resulting blocked beam fraction assuming a constant elevation angle of 0.5\({}^{\circ}\) for the entire sweep. In this work, the PBB correction has been modified to allow variation within a sweep of the elevation angle, where the correction is applied with a precision of 0.1\({}^{\circ}\) in elevation. After correction for PBB, the reflectivity data are cleaned using a fuzzy-logic classifier to remove non-meteorological echoes and are then corrected for attenuation and converted to precipitation intensity using the Marshall-Palmer relationship [33]. The QPEs are solely derived using a reflectivity-only estimate of precipitation intensity rather than incorporating dual-polarisation estimators in order to reduce the complexity of the subsequent results given other radar moments are immune to partial beam blockage. Two estimates are compared here: one where PBB is corrected using the radar's recorded azimuth and elevation data as truth and one where tilt correction is applied before PBB correction. All other processing steps are identical for both estimates. As per [PERSON] et al. [28], after precipitation intensity is estimated for all radar sweeps, the volume is flattened using a fixed lowest usable elevation look-up table and then remapped from polar coordinates onto a 1 km Cartesian rectilinear grid using the area-weighted average of all 2D polar radar pixels intersecting with each Cartesian grid cell. The gridded precipitation intensities (produced approximately every 6 min) were then accumulated using a simple trapezoidal rule to produce hourly gridded accumulations that were used for comparison to raingauge observations (as detailed below). Any period of greater than 10 min without a radar scan was treated as missing.
## 4 Mechanisms and Optimizations
### Preference-Based Scheduling Strategies
* (46) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Less pain, most of the gain: Incrementally deployable icn. _ACM SIGCOMM Comput. Commun. Rev._**2013**, _43_, 147-158. [CrossRef]
* (47) [PERSON]; [PERSON] Efficient user-assisted content distribution over information-centric network. In Proceedings of the NETWORKING 2012: 11 th International IFIP TC 6 Networking Conference, Prague, Czech Republic, 21-25 May 2012; Proceedings, Part I 11; Springer: Berlin/Heidelberg, Germany, 2012; pp. 1-12. * (48) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Naming in content-oriented architectures. In Proceedings of the ACM SIGCOMM Workshop on Information-Centric Networking, Toronto, ON, Canada, 19 August 2011; pp. 1-6. * (49) [PERSON]; [PERSON] A hybrid approach for name resolution and producer selection in information centric network. In Proceedings of the 2018 International Conference on Computing, Networking and Communications (ICNC), Maui, HI, USA, 5-8 March 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 574-580. * (50) [PERSON]; [PERSON]; [PERSON] A Tree Structure Protocol for Hierarchical Deterministic Latency Name Resolution System. _Electronics_**2022**, _11_, 2425. [CrossRef]
* (51) [PERSON]; [PERSON] Broadcast with Tree Selection from Multiple Spanning Trees on an Overlay Network. _IEICE Trans. Commun._**2023**, _106_, 145-155. [CrossRef]
* (52) [PERSON], [PERSON] [PERSON]'s conjecture and the feasibility of consistent, available, partition-tolerant web services. _ACM Sigact News_**2002**, _33_, 51-59. [CrossRef]
* (53) [PERSON]; [PERSON]; [PERSON] _Exploring Network Structure, Dynamics, and Function Using NetworkX_; Technical Report; Los Alamos National Lab (LANL): Los Alamos, NM, USA, 2008. * (54) [PERSON]; [PERSON]; [PERSON]; [PERSON] Programmable data processing method and system design for polymorphic network. _J. Commun._**2022**, _43_, 14. * (55) Alibaba. Alibaba Open Trace. Available online: [[https://github.com/alibaba/clusterdata](https://github.com/alibaba/clusterdata)]([https://github.com/alibaba/clusterdata](https://github.com/alibaba/clusterdata)) (accessed on 1 January 2018). * (56) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] CFN-dyncast: Load Balancing the Edges via the Network. In Proceedings of the 2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Nanjing, China, 29 March 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 1-6. **Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
# Interactive Part Segmentation Using Edge Images
## 2 Related Work
### Interactive Object Segmentation  
Unlike semantic segmentation methods, interactive segmentation methodsonly segment the area of the target object or the part with user interaction. In addition, they cannot recognize the classes of the selected object. However, they have the advantage of being able to segment an unlearned object or region through user input.  
DeepGrabCut [9] is one methods of segmentation of the target objects with user interaction. In DeepGrabCut [9], when the user selects the object around the target object by drawing the bounding box, the object is segmented. In the process of drawing the bounding box, the target object can be segmented easily with one operation, but it is difficult for it to be segmented precisely.  
Another interaction method is to use only the click interaction. In this method, segmentation occurs by clicking the target object area and the non-target area. The user can see the object mask predicted by the deep-learning model and finely segment the target object through additional clicks. [PERSON] et al. [10] proposed a method to solve the problem of IOU accuracy decreasing when the number of clicks increases in the case of the existing models by adding the previously estimated mask as input to the proposed deep-learning model. [PERSON] et al. [11] proposed a method of segmentation of the target object by clicking some of the points on the boundary of the target object. [PERSON] et al. [12] proposed a method for better segmentation of the target object by weighting the area the user first clicked on. [PERSON] et al. [13] proposed a more accurate object segmentation method (BRS)with refining information converted from user interaction by using the backpropagation method. [PERSON] et al. [2] proposed a faster segmentation method (f-BRS) than BRS by performing backpropagation in only part of the model. [PERSON] et al. [3] proposed a method (IOG) for segmenting the target objects through interaction that combines the advantages of the bounding box and click methods. In IOG, a user first selects the area around the target object using the bounding box, and then can segment the target object precisely by clicking additionally on the area that is an object or the area that is not an object based on the area of the mask estimated by previous interaction.  
The above methods allow a user to accurately segment the target object with less interaction. However, in all of the existing methods, if the part of an object is segmented with these models trained using color images, the IOU accuracy is greatly reduced.
###### Abstract  
Compared to analog beamforming, digital beamforming offers better self-calibration and lower sidelobe performance, which has a profound impact on improving low Earth orbit receiver performance. The Digital Beamforming (DBF) module in the low Earth orbit satellite broadcast signal reception terminal can use digital phase shifting to compensate for the phase differences caused by path and spatial distance variations due to inconsistent Radio Frequency (RF) channel delays. This compensation ensures in-phase summation, thereby achieving maximum energy reception in the desired direction. Although DBF has gained widespread attention in the radar field due to its unique functions and advantages, its application is limited by beamforming accuracy and gain. Therefore, with the development of DBF technology, how to improve its accuracy and gain has also attracted extensive attention both domestically and internationally. To address this issue, this paper proposes a beamforming method based on a cap-shaped array for low Earth orbit satellite broadcast signal reception and processing terminals. The method combines prior information and spatial domain search for beam control, and employs a lookup table for beam synthesis. It derives formulas for the Signal-to-Noise Ratio, noise figure, processing flow of the beamforming network, and the determination of beamforming weights for the spherical antenna array. The paper presents a beam control approach that combines prior information with spatial domain search, along with an implementation process for beam synthesis using a lookup table. It also details the corresponding Field-Programmable Gate Array (FPGA) implementation process. Finally, the beamforming algorithm is experimentally validated, and error analysis is conducted. The experimental results show that the measured beamforming sensitivity at all incident angles is below \(-133\) dBm and the G/T values are all greater than \(-9\) dB/K, the beam uniformity at three operating frequencies is less than \(3^{\circ}\), and the measured errors in pitch and azimuth angles are both below \(2^{\circ}\). The beam pointing error is also below \(2^{\circ}\).  
low Earth orbit satellites; spherical antenna array; digital beamforming; prior information; spatial search; lookup table; theoretical proof and implementation +
Footnote †: journal: Electronics Letters
# Deformation Law and Control Limit of CRTSIII Slab Track under Subgrade Frost Heave
## 2 Plastic Damage Analysis Model of Slab Tracks
### Finite Element Model of Slab Track
#### 2.2.3 Material Properties and Load Conditions  
Referring to [41; 42; 51], the material parameters of the track structure are shown in Table 2.  
The load condition has a significant influence on the track's force and deformation [34]. In this study, the object is the track irregularity and damage caused by the subgrade frost heave. The effect of the train load will be investigated in subsequent studies. In order to reveal the influence of subgrade frost heave on track irregularities and damage, the gravity of the track is the main load. Gravity acceleration is applied to the track and foundation. We performed static analysis to research the irregularity and damage of the track under gravity load.  
Subgrade frost heave as a form of load is applied to the subgrade surface. We utilized the node displacement load in the finite element software to produce different frost heave loads. In this study, subgrade frost heave load includes two parameters: frost heave wavelength and frost heave amplitude.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Elastic Modulus (GPa)** &
\begin{tabular}{c} **Linear Expansion** \\ **Coefficient (m/\({}^{o}\)C)** \\ \end{tabular} & **Poisson Ratio** & **Density (kg/m\({}^{3}\))** \\ \hline Rail & 205.9 & \(1.18\times 10^{-5}\) & 0.3 & 7830 \\ Track slab & 36.0 & \(1.0\times 10^{-5}\) & 0.2 & 2500 \\ Self-compacting concrete & 32.5 & \(1.0\times 10^{-5}\) & 0.2 & 2450 \\ Base plate & 32.5 & \(1.0\times 10^{-5}\) & 0.2 & 2500 \\ Subgrade & 0.18 & - & 0.25 & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Track structure material properties.  
Figure 5: Schematic diagram of door-type reinforcements. (**a**) Schematic diagram of space location; (**b**) size of rebar (unit: mm).
## 5 Conclusions
### Practical Implications  
The article's examination of cyberattacks and cybersecurity in smart grids, solving the MCDM problem using AHP for optimal cybersecurity options, and exploring the potential benefits and challenges of AI, has several practical implications:  
* **Enhanced Awareness and Preparedness**: the article's overview of smart-grid components, applications, and the types of cyberattacks helps raise awareness about potential vulnerabilities. Grid operators and cybersecurity professionals can use this knowledge to enhance their understanding of the risks and develop proactive measures to protect smart-grid infrastructure.
* **Prioritizing Optimal Cybersecurity Options**: the application of MCDM-AHP to find the optimal cybersecurity option provides a systematic approach for decision-making. Grid operators and policymakers can use this methodology to evaluate and prioritize various cybersecurity options based on multiple criteria, thereby allocating resources efficiently to the most effective solutions.
* **Strengthening Cybersecurity Measures**: the implications of cybersecurity for smart grids underscore the need for robust security measures. Grid operators can leverage the insights from this article to implement access controls, authentication protocols, encryption mechanisms, and other cybersecurity solutions to safeguard critical infrastructure against cyber threats.
* **Leveraging AI for Improved Security**: understanding the difference between AI and exploring its potential and challenges enables grid operators to make informed decisions about leveraging these technologies. By using AI algorithms for threat detection, anomaly detection, and incident response, smart grids can enhance their resilience and response capabilities.
* **Addressing the Limitations and Exploring Alternatives**: Acknowledging the limitations of MCDM-AHP and considering alternative MCDM techniques allows decision-makers to make more comprehensive assessments. This knowledge empowers them to evaluate different cybersecurity options and make well-informed decisions, considering a broader range of factors and trade-offs.  
By embracing these practical implications, stakeholders in the smart-grid domain can enhance their cybersecurity posture, minimize vulnerabilities, and improve the overall resilience and reliability of the grid infrastructure in the face of evolving cyber threats.  
This research was supported by the Laboratory of Renewable Energies and Advanced Materials (LERMA) and the College of Engineering and Architecture of the International University of Rabat (IUR).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
Data Availability Statement: The data used in this study, along with the details of the methodology adopted, are comprehensively described in the methodology section (Section 2) of this article  
The author extends sincere gratitude to the editorial office and the dedicated reviewers for their insightful feedback and meticulous review, which have enhanced the clarity of this article.  
The author declares no conflict of interest.
## 3 Results and Discussion
### Photocatalytic Evaluation
From the provided photocatalytic experiments, it was determined that the limit of detection (LOD) for samples with MG was 0.0014 mg/L and for MB was 0.0071 mg/L. Interestingly, there is no residual Ag from the photocatalysis of both dyes using visible light (concentrations measured were below the LOD value). In all studied solutions of Methylene blue, the silver measured is below the LOD of the analytical procedure used at the 1 st, 2 nd, 3 rd, or 4 th h Residual silver is found after the photocatalytic decomposition of MG under UV light using TP2 and Ag, 10\({}^{-2}\)/TP2 films (from 0.0076 \(\pm\) 0.0005 to 0.014 \(\pm\) 0.001 mg/L), as well as TP4 and Ag, 10\({}^{-2}\)/TP4 (0.0076 \(\pm\) 0.0005 to 0.013 \(\pm\) 0.001 mg/L), which could be ascribed to a non-homogenous distribution of silver. Finally, the photocatalytic decomposition of dyes in tap water is also tested (Figure 10). Compared to pure films, the Ag-modified films exhibited higher activity; with Ag, 10\({}^{-2}\)/TP4 film demonstrating the highest activity in photocatalysis in both types of water. The pure and Ag-modified samples exhibited a lower rate of pollutant degradation in distilled water due to variations in the pH levels. Despite slight differences, both waters have different pH values, with tap water (pH = 7.39) having a marginally higher pH than that of distilled water (pH = 6.5). This indicates a higher concentration of OH\({}^{-}\) in tap water, resulting in more OH\({}^{\bullet}\) and thus more effective degradation of pollutants. The TOC percentages for dye mineralization in tap and distilled water were determined (Figure 11). In tap water, TP4 showed higher TOC conversion rates compared to those of TP2 for both MG and MB, while in distilled water, similar trends are observed. The introduction of Ag\({}^{+}\) into TiO\({}_{2}\) increased the TOC conversion rates, with higher rates observed for films with higher concentrations of silver and surfactant molecular mass. However, comparing the TOC analysis to the UV-vis spectroscopy revealed lower val  
Figure 10: Degradation of the dyes in distilled and tap waters under UV illumination. Figure 9: Photostability of TP4 and Ag, 10\({}^{-2}\)/TP4 on the decomposition of MG (**a**) and MB (**b**). ues, indicating the involvement of complex intermediate steps and products in the dye breakdown process [39; 40]. These intermediate products, upon interaction with with OH\({}^{\bullet}\) produced during photocatalysis, exhibit unique oxidation potentials.