esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Effective Radiation Quantity on the Solar Panel and Calculation of Solar Irradiance  
To simulate solar radiation from a different angle than the photovoltaic panel, a simple program was written to obtain the practical relationship between the sun and the falling radiation. Figure 2a shows the equivalent Simulink block diagram that was used to simulate the angle between the sunlight and the photovoltaic panel based on Equation (2) [31; 32]. The fixed panel is always parallel to the ground at 45\({}^{\circ}\) c.d. 0\({}^{\circ}\) for sunrise and 180\({}^{\circ}\) for sunset. At sunrise, the sun's angle starts at 0\({}^{\circ}\), and at sunset, it is at 180 \({}^{\circ}\)C. To follow the change in the amount of radiation and temperature that falls on the ground during the day, we have created a model that simulates the evolution of the angle of the sun with time using the real data downloaded from the site PV system. Figure 2b represents the Simulink block diagram used.  
\[Gef=G*\left(\frac{-(\theta s-\theta p)+180}{180}\right) \tag{2}\]  
where \(Gef\) is the effective radiation, \(G\) is the solar radiation, \(\theta s\) is the sun angle, and \(\theta p\) is the panel angle.
## 5 Conclusions and Future Work  
In this paper, we studied the problem of parallel machine scheduling with multiple planned nonavailability periods. In the current literature, very few papers investigated this problem. The problem was formulated as a quadratic program and optimally solved using CPLEX for small- to moderately large-sized problems. In order to be able to solve large-sized problems, an algorithm consisting of two main phases was developed. The first phase searches for schedules on the basis of the \(LPT\) rule. The second aims to improve these schedules by considering simultaneous pairwise interchanges of jobs between machines. A deep computational study was conducted to test the efficiency of the proposed approach. Many datasets were carefully generated to help evaluate the algorithm. Computational results showed that the proposed algorithm generated optimal solutions for all considered problem sizes and outperformed an adapted form of a heuristic that was developed earlier by the author of this paper. Further investigation can be done to consider other criteria and more general versions of the problem, such as the dynamic case where jobs arrive one by one over the planning horizon.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(m\) & \(n\) & \(C_{max}(QM)\) & \(C_{max}(MA)\) & \(QP\)**Optimal?** & \(CPUt(QM)\) & \(CPUt(MA)\) \\ \hline
30 & 61 & 133 & 133 & Yes & 70.74 & 0.15 \\
33 & 67 & 137 & 137 & Yes & 40.19 & 0.22 \\
36 & 73 & 142 & 142 & Yes & 21.1 & 0.21 \\
40 & 81 & 140 & 140 & Yes & 44.71 & 0.31 \\
45 & 91 & 232 & 232 & Yes & 398.64 & 0.33 \\
51 & 103 & 240 & 240 & Yes & 495.44 & 0.4 \\
57 & 115 & 237 & 237 & Yes & 214.28 & 0.2 \\
62 & 125 & 336 & 336 & Yes & 263.21 & 0.2 \\
68 & 137 & 331 & 331 & Yes & 393.97 & 0.39 \\
73 & 147 & 434 & 434 & No & 3603.54 & 0.42 \\
76 & 153 & 439 & 439 & No & 3602.48 & 0.43 \\
80 & 161 & 538 & 538 & Yes & 3602.9 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of \(QM\) and \(MA\) for datasets \(DS2\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(m\) & \(n\) & \(C_{max}(MLPT)\) & \(C_{max}(MA)\) & \(C_{max}(MLPT)/C_{max}(LSHIP)\) & \(CPUt(QP)\) & \(CPUt(MA)\) \\ \hline
100 & 201 & 1120 & 880 & 1.27 & 0.47 & 0.49 \\
200 & 401 & 1090 & 1050 & 1.03 & 2.16 & 2.5 \\
300 & 601 & 1250 & 970 & 1.28 & 4.69 & 4.81 \\
400 & 801 & 1110 & 960 & 1.15 & 9.12 & 9.55 \\
500 & 1001 & 1120 & 760 & 1.47 & 16.24 & 15.48 \\
600 & 1201 & 1260 & 1240 & 1.01 & 28.24 & 24.07 \\
700 & 1401 & 1240 & 1160 & 1.06 & 41.92 & 62.24 \\
800 & 1601 & 1260 & 1110 & 1.13 & 74.97 & 48.5 \\
1000 & 2001 & 1110 & 1080 & 1.02 & 120.04 & 122.99 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of \(MA\) and \(MLPT\).  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 Conclusions  
1. This paper proposes a method for adaptive morphological feature extraction based on entropy rate superpixel seed points, combined with an adaptive morphological reconstruction (AMR) method using LOG features. It can adaptively filter out useless seed points while preserving meaningful seed points, achieving the extraction of surface micro-morphological features of contact groups.
2. This paper proposes a combined surface normal contact stiffness fractal model, which is compared with data from other literature models to verify its correctness. The model's features include correcting the W-M model based on point cloud data, characterizing the distribution of micro-asperities using an island distribution function, and incorporating three contact deformation mechanisms of micro-asperities: elastic, elastoplastic, and fully plastic. It also considers the anisotropy of rough surfaces, making the model more closely aligned with the actual contact process of the contact point group.
3. This article analyzes the contact deformation process of the model, explores the properties of micro-asperity deformation, and its impact on the actual contact area, providing a theoretical basis for the correlation analysis between the surface micro-morphological characteristics of contact groups and the electrical contact performance of switching electrical appliances.
4. The content of this article requires further study. Subsequent work can integrate more morphological feature parameters and consider the effect of surface films to establish a more comprehensive and accurate contact model.  
Conceptualization, W.L. and J.S.; methodology, P.Z.; validation, P.Z. and Z.H.; formal analysis, J.S., P.Z., X.M. and Z.H.; investigation, J.S. and P.Z.; resources, D.C. and J.C.; data curation, J.S.; writing--original draft, J.S.; writing--review and editing, W.L.; supervision, W.L. and H.W.; project administration, J.S.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Shijiazhuang Universities in Hebei (241130161A), the Innovative Research Group Project of the Natural Science Foundation of Hebei Province (E20202142), and the Hebei Province Graduate Innovation Funding Project (CXZZSS2024012).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author due to confidentiality restrictions.  
Author [PERSON] was employed by the Guilin Electrical Equipment Scientific Research Institute Co., Ltd. [PERSON] was employed by the China Railway Shenyang Group Co., Ltd. The remaining author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
## 5 Results and Analysis
### Ablation Study  
#### 5.3.1 Validation of the Effectiveness of Implicit Degradation Representations-Guided Reconstruction  
Implicit degradation representation \(E\) estimated by the degradation estimation sub-network guides the reconstruction of blurred images by the deblurring process. In order to verify the contribution of implicit degradation representation \(E\) to the final deblurring performance, ablation studies are designed in this paper. Specifically, only the deblurring process is retained on the original network framework of IDR\({}^{2}\)ENet, and the high-dimensional deblurring subnetwork is made to take only real blurred image as input, at which time the network framework is shown in Figure 13. This is noted as IDR\({}^{2}\)ENet-Q. The results of retraining on the GoPro dataset with exactly the same experimental settings as IDR\({}^{2}\)ENet are shown in Table 3.  
As shown in Table 3, the performance of IDR\({}^{2}\)ENet-Q is reduced by 0.41 dB/0.012 compared to the original IDR\({}^{2}\)ENet, which proves that IDR\({}^{2}\)ENet can better reconstruct deblurred images and achieve a higher performance guided by the implicit degradation representation \(E\) when facing complex degradation in real blurred images.
# Performance Analysis of Post-Quantum Cryptography Algorithms for Digital Signature
## 3 Post-Quantum Cryptography  
Quantum computers represent a breakthrough in the computing domain. In contradistinction to classical devices, they use the principles of quantum mechanics to process information. The smallest data unit in conventional computers is called the bit; it can take the value either 0 or 1. However, in quantum computers, this is replaced by qubits, which can be in a superposition of states 0 and 1. This allows them to perform many operations simultaneously, which leads to increased speed. The challenges faced by current prototypes of quantum computers are their stability and scalability. In order to maintain the quantum state, it is necessary to maintain extremely low temperatures as close as possible to the absolute zero. Subsequent factors that negatively affect qubits are all kinds of vibrations or magnetic fields [8]. Even though quantum computers still require a lot of research, they pose a potential threat to current cryptography. Therefore, there is a reason for the formation and development of post-quantum cryptography.  
Figure 1: Digital signature scheme.
## 5 Environmental Applications
### Solid Samples  
Sampling of soil and sediments as well as other solid materials like manure, activated sludge, and biochar by SPME is usually performed by HS methods. In some applications extraction of analytes has been assisted by heating of the sample [84, 377, 378, 384, 385, 386] or cooling the fiber [54], use of microwaves [381]; use of pressurized fluid extraction [95], or addition of small amounts of organic solvents prior to SPME [84, 384]. Most applications published on SPME analysis of solid environmental samples involve the use of commercially available SPME fibers. A novel sol-gel permethylated-\(\beta\)-cyclodextrintrim/hydroxyl-termination silicone oil coated fiber has been used to extract polybrominated diphenyl ethers from soil [384]. With this coating, improved fiber stability has been achieved at analytical characteristics similar to commercially available fibers (RSD \(<\) 10%, LOD 13.0-78.3 pg/g, recovery \(>\) 78%, r \(>\) 0.999).  
The main fields of SPME applications to solid environmental samples are analysis of pesticides [378, 379], PAH and related compounds [380, 381, 382], phenols [94, 383], perfluorocarboxylic acids [95], and organotin compounds [84] in contaminated soils and sediments (see Table 4). Since SPME is generally organic solvent free, rapid, and sensitive, it allows the detection of trace amounts of substances that are short-lived during biotransformation, thus, providing insight into degradation pathways. For example, [PERSON] _et al_. [383] developed a negligible depletion-SPME (nd-SPME) method to measure free concentrations of 4-t-octylphenol, nonylphenol and bisphenol A in order to determine aerobic biodegradation kinetics in activated sludge [383]. In nd-SPME, the extracted amount of analytes is so small that the depletion (_i.e._, change of concentrations) of the analytes does not lead to a change in reaction kinetics or other relevant conditions in the sample. Further applications of SPME to solid environmental samples are characterization of VOC and odorous compounds in soil, manure, compost, and biochar samples [373, 374, 375, 376, 377].
# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
###### Abstract  
Despite significant challenges, the text-guided remote sensing image generation method shows great potential in many practical applications such as generative adversarial networks in remote sensing tasks; generated images still face challenges such as low realism, face challenges, and unclear details. Moreover, the inherent spatial complexity of remote sensing images and the limited scale of publicly available datasets make it particularly challenging to generate high-quality remote sensing images from text descriptions. To address these challenges, this paper proposes the RSVQ-Diffusion model for remote sensing image generation, achieving high-quality text-to-remote-sensing image generation applicable for target detection, simulation, and other fields. Specifically, this paper designs a spatial position encoding mechanism to integrate the spatial information of remote sensing images during model training. Additionally, the Transformer module is improved by incorporating a short-sequence local perception mechanism into the diffusion image decoder, addressing issues of unclear details and regional distortions in generated remote sensing images. Compared with the VQ-Diffusion model, our proposed model achieves significant improvements in the Frechet Inception Distance (FID), the Inception Score (IS), and the text-image alignment (Contrastive Language-Image Pre-training, CLIP) scores. The FID score successfully decreased from 96.68 to 90.36; the CLIP score increased from 26.92 to 27.22, and the IS increased from 7.11 to 7.24.  
remote sensing image; transformer; diffusion model; image generation; VQ-Diffusion Model; 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 1-10. External Links: Document Cited by: SS1.
## 4 Results and Discussion
### Flow Field Analysis of Case 2  
From Figure 4, it can be seen that the test bench has an influence on the structure of the whole flow field by dividing the airflow that eventually forms into two vortex rings. To know its actual influence on the lift and torque performance of the rotor is important because it may affect the accuracy of the experiment. Therefore, the simulations with the  
Figure 5: Mach Number streamline (**a**) and velocity streamline (**b**) of case 1.  
Figure 6: Slice contours of pressure (**a**) and Mach number (**b**) of the flow field.  
test bench removed were performed to explore its influence on the flow field and the lift and torque performance.  
Figure 8 presents the streamline and slice Mach number contours of the flow field of case 2. It can be seen that, without blockage of the test bench, the airflow moves directly down to the bottom of the chamber after passing the rotor disc and then flows around the wall, forming a giant vortex ring. The vortex ring diminishes into a smaller ring within the big ring and then disappears. Some air is drawn by the low-pressure zone created by the rotor disc and participates in the circulation. From the slice Mach number contour, it can be seen that, compared with case 1, case 2 has a larger maximum Mach number but smaller vortex intensity. Therefore, it has a weaker low-pressure zone over the upper surface of the blades.
## 6 Conclusions  
The results provided by the use of our simulation design tool are collected and demonstrated in comparison with the outcome results provided by the use of five professional tools. They appear in significant convergence for the same received signal level, indicating the validity, sustainability, and suitability of this tool, proving it to be ready for use in real implementations.  
The results of the theoretical calculations shown in Figures 8 and 9 show two important areas of verification. As a rule, when the distance increases, the received signal level decreases. Also, the path loss is observed to increase as the frequency increases, as is verified by propagation theory.  
In addition to the uses of performing necessary calculations that have already been described, the tool can also offer the user the ability to easily scan the IP addresses of devices in the internal network, enabling complete administrative control of a local network with predetermined static IP addresses. It is also worth mentioning that industrial tools with similar purposes that are currently available often require extensive training or significant financial investment. In the future, this Python GUI script could be enhanced by including system noise calculations and other similar features to determine various signal losses. Furthermore, this scenario could be built upon a web framework such as Django, Flask, or Falcon. To further enhance the practicality and flexibility of the proposed tool, researchers could incorporate a wider range of signal processing applications and develop in parallel an interactive virtual lab that could provide a hands-on learning experience.  
Conceptualization, K.Z., A.S. and G.D.; Formal Analysis, K.Z., A.S. and G.D.; Methodology, K.Z., A.S. and G.D.; Writing--Original Draft Preparation, K.Z. and A.S.; Writing--Review and Editing, K.Z., A.S. and G.D.; Visualization, K.Z., A.S. and G.D.; Supervision, K.Z., A.S. and G.D.; Project Administration, G.D.; Funding Acquisition, G.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement**: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Figure 9: Comparison of different simulation tools at 11 GHz.
## 2 Data Collection

Table 2: Dataset Variables: Symbol, Explanation, Data Types, and Units. Pedestrian avoidance behavior was determined by manually observing video footage. Specifically, avoidance was identified when a pedestrian, during interactions with vehicles, suddenly changed their walking route or came to an abrupt stop. These behaviors indicate a deliberate attempt to avoid potential collisions or perceived danger. To ensure labeling consistency and accuracy, a random subset of the annotated data was independently cross-validated by a second annotator. Any discrepancies between annotators were discussed and resolved to maintain high labeling reliability. From the preliminary statistical analysis of the dataset, we observed the following: In 42% of the samples, vehicles traveling in the same direction were encountered (S = 0.42), and in 46% of the samples, vehicles traveling in the opposite direction were encountered (O = 0.46). In the remaining 12% of the samples, no vehicles were encountered. This indicates that pedestrians typically face vehicle interactions from both directions. The average STV (same-direction traffic volume) is 2.4 vehicles per second, while the average OTV (opposite-direction traffic volume) is 3.6. Regarding pedestrian grouping, only 38% of the samples (\(p\) = 0.38) indicate that pedestrians walk together, implying that most pedestrians walk alone. The average SV (same-direction speed) is 4.32 m/s, and the OV (opposite-direction speed) is 4.50 m/s, showing that vehicles generally travel at relatively low speeds in these areas. The mean road W (width) is 4.75 m, reflecting a moderate width. The mean value for pedestrian A (avoidance behavior) is 0.52, indicating that pedestrians avoid vehicles in about half of the cases. This detailed description clearly defines the variables' types (binary vs. continuous) and units, providing a comprehensive understanding of the dataset used in our analysis. These 12 features are selected for training the model because they accurately reflect pedestrian behavior and decision-making in real traffic environments. They include whether pedestrians encounter vehicles in the same or opposite direction, the type of vehicles (cars or electric bicycles), traffic volume, vehicle speed, and the presence of walking companions. Traffic volume and speed are crucial for predicting whether pedestrians need to take evasive actions, while the number of companions influences pedestrian movement and avoidance decisions. These factors comprehensively describe the interaction between pedestrians and vehicles, enabling the model to predict pedestrian behavior in various traffic scenarios better. Figure 1: _Cont._
###### Abstract  
The industry foundation classes (IFC) data model is the most important data schema in ensuring the interoperability of the information generated throughout the lifecycle of facilities. However, because the current IFC model is focused on buildings, there are limitations when this model is applied to bridge structures. This paper proposes a method that enables the information modeling of steel box girder bridges based on the current IFC. To select the required and core items, we classify the components of a steel box girder bridge by the design stage with reference to engineering documents. To generate functional meanings of each bridge component, we develop the rules of the unique identifier and information reassignment, and then apply a semi-automated naming algorithm. The generated bridge information model was used to confirm the functional semantic meanings of individual components, and it was checked whether additional external information, such as carbon emissions, could be linked for specific bridge components. It was observed that information retrieval and extraction for components is possible through a semantic-based query to the generated IFC-based bridge information model.  
Keywords:industry foundation classes (IFC); bridge information modeling (BrIM); IFC user-defined property sets; automated naming algorithm; model-based object query +
Footnote †: journal: Applied Sciences
## 3 Research Design, Methodology, and Data Collection
### Data Collection Method  
* The survey questionnaire will be administered online, using a secure and user-friendly survey platform.
* This approach allows for wider reach, easier data collection, and the ability to reach respondents in different geographic locations.
## 2 Analysis
### Analytical Model
#### 2.2.2 Network Deployment Efficiency Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Macro BS (1 kW)** & **Pico BS (250 mW)** \\  & **[5]** & **[5]** \\ \hline _Equipment purchase (CAPEX)_ & 50,000.00 & 5000.00 \\ _Equipment installation (CAPEX)_ & 120,000.00 & 3000.00 \\ _Maintenance (OPEX)_ & 2500.00 & 250.00 \\ _Site rental (OPEX)_ & 10,000.00 & 1000.00 \\ _Backhaul rental (OPEX)_ & 40,000.00 & 10,000.00 \\ _Energy consumption (OPEX)_ & 3500.00 & 200.00 \\ \hline _TOTAL_ & 226,000.00 & 19,450.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Capital expenditure (CAPEX) and operating expenses (OPEX) in 2-tier Heterogeneous Cellular Network (HCN), also commonly referred to as HetNet, (HetNet).