esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 4 Simulation Experiment
### Multi-DAG Task Scheduling Fairness Experiment  
In this study, we needed to conduct experiments on the scheduling fairness of the four algorithms. According to previous research [21], the unfairness of the scheduling algorithm is defined as  
\[Unfairness_{M}(S)=\sum_{\forall k\in a}|Slowdown_{M}(k)-AvgSlowdown_{M}| \tag{10}\]  
where \(AvgSlowdown_{M}\) represents the average \(Slowdown_{M}\) value for all DAGs, and the smaller the value of \(Unfairness_{M}(S)\), the higher the scheduling fairness of the algorithm. The unfairness of the four algorithms is shown in Figure 7.  
The unfairness comparison diagram is shown in Figure 7. In the evaluation of unfairness performance, the average unfairness increased with the growth in DAG task numbers and decreased with the increase in node numbers. The performance of the Composite priority algorithm was the poorest. The reason for this is that the priority of the original DAG task increased during multi-DAG scheduling, which affected the performance of the algorithm compared with the Fairness algorithm. The LB-HEFT algorithm's performance was slightly better than that of the Fairness algorithm, while the performance of the Cache-Composite priority algorithm was the best. Due to the introduction of the concept of fair scheduling in the Cache-Composite priority algorithm, the difference in unfairness between the two algorithms became more pronounced compared to the Fairness algorithm. The unfairness of the Cache-Composite priority algorithm was reduced by 42.1%.
# Geosynthetic Reinforced Steep Slopes: Current Technology in the United States
## 6 Performance Measures & Cost Effectiveness
### Quality Control  
Properties of geosynthetics are classified as general, index, and performance. General properties include the polymer, mass per unit area, thickness, roll dimensions, roll weight, and specific gravity. However, index properties provide qualitative assessment through standard test procedures for utilization in product comparisons, specifications, quality control, and constructability. Index tests include uniaxial mechanical strength, multiaxial rupture strength, durability tests, and hydraulic tests. Finally, performance properties of geosynthetics include the use of soil samples for direct assessment of properties, such as in-soil stress-strain, creep, friction/adhesion, chemical resistance, and filtration. Under direction of the design engineer, performance tests are correlated to index values for use in preselecting geosynthetics for project specifications.  
Geosynthetic specifications include general requirements, specific geosynthetic properties, seams and overlaps, placement procedures, repairs, and acceptance and rejection criteria [1]. Specified general requirements include the types of geosynthetics, acceptable polymeric materials, and guidelines for the stability of the materials as well as instructions for storage and handling, roll weight, and dimensions and certification requirements. Physical, index, and performance properties are also included in the general requirements as per the specific project design. Additionally, seams and overlaps are clearly specified in all geosynthetic applications in accordance with construction requirements. Although overlaps may be increased, a minimum overlap of 30 cm is recommended for geotextile applications. However, geosynthetics are connected if overlaps will not work, and the connection material should always consist of polymeric materials that have equal or greater durability than the specified geosynthetic. Placement procedures are detailed on the construction drawings and typically include requirements for grading, ground-clearing, aggregates, lift thickness, and equipment. Repair procedures for damaged sections, acceptance and rejection criteria, and inspection procedures, including sampling and testing requirements, are also included in geosynthetic specifications to ensure successful project completion.
# Advanced Technologies for Geosite Visualization and Valorization: A Review
## 2 Innovative Technologies for Geoheritage Visualization  
### Virtual Outcrops and Virtual Geosites Building  
In order to pursue the objective of virtual outcrop and virtual geosite building, many authors in the past have employed Structure from Motion photogrammetry [61; 62]. This method is particularly useful, as it produces photorealistic 3D models [63; 64]. The creation of 3D models of outcrops consists in two main phases: (i) image collection by Unmanned Aerial Vehicles (UAVs); (ii) processing of photogrammetry and building of a 3D model.  
The first phase comprises the use of UAVs, which have been used, during the last two decades, to improve understanding of several kinds of phenomena, from earthquakes [65; 66] to volcanic activity [67; 68; 69; 70], gravity-related mass movements [71; 72; 73; 74; 75] and flood hazards [76; 77]. As highlighted above, the use of drones has been complemented by Structure-from-Motion (SfM) photogrammetry [64; 78; 79], a very helpful method to improve the traditional techniques employed to collect field data in Earth Sciences. [PERSON] et al. [80] showcased a new approach that makes use of Virtual Reality (VR) on the basis of 3D Digital Outcrop Models (DOMs). The latter are constructed by photogrammetry techniques, and, lastly, game engine technologies are used to create a VR scene. The above techniques can be used for gathering images of geosites, and for building \"Virtual Outcrops (VOs)\" [81; 82], that may also be called \"Virtual Geosites\" (VGs) [83]. This advanced technique can be employed for promoting local geoineritage by (i) illustrating geosites, assessing their value; (ii) communicating geoscience topics highlighting active geological processes and (iii) engaging youths who are keen on experimenting with interactive communication techniques.
## 4 Simulation Experiment
### System and Initial Parameter Settings
#### 4.1.2 Experimental Situation  
The experiment selects the best two visible satellites LEO-1 and LEO-2 during the flight of the carrier according to the GOP selection satellite algorithm, and the corresponding PRN number is given, respectively. Due to the low orbit of the LEO satellite, according to Kepler's law of motion, it can be known that the LEO satellite moves very fast, which will lead to a very short time for the LEO satellite to pass the zenith, therefore, we must switch between satellites and we set the switching time to 5, 10, 30, 60 and 90 s, respectively [20]. In addition, to explore the navigation and positioning performance of the algorithm under different altimeter deviations, so as to provide a reference basis for practical engineering application, we divide the altimeter deviation into three scenarios: no deviation, fixed deviation \(\pm\)5 m and fixed deviation \(\pm\)20 m. See Table 3 for all parameters.
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 2 Magnetic Particle Testing
When defects are present on or near the surface of the workpiece, the discontinuity of material at the defect causes a local change in magnetic permeability, resulting in a distortion of the magnetic field and the leakage of magnetic flux from the workpiece surface, forming a leakage magnetic field. Under the influence of the leakage magnetic field, magnetic particles are attracted and gather at the defect location, forming magnetic traces associated with the defect's leakage magnetic field. These traces can highlight defects that are not easily visible, and by observing and analyzing the geometric shape of the magnetic traces, workers can identify surface defects on the workpiece. Fluorescent magnetic powder, containing fluorescent dyes, emits yellow-green fluorescence in the 510-550 mm wavelength range when exposed to ultraviolet light. This creates a high-contrast fluorescence against the workpiece's surface color, making the magnetic traces easier to observe. Especially when using fluorescent magnetic powder in a darkroom, the fluorescence of the magnetic traces exhibits extremely high contrast with the workpiece surface, significantly enhancing the sensitivity and inspection speed of magnetic particle testing. The generation of magnetic traces involves various factors, including the leakage magnetic field at the defect, the leakage magnetic field in non-defective areas, and even the natural deposition and retention of magnetic particles on the workpiece surface [13]. Generally, magnetic traces formed by the leakage magnetic field generated by defects are referred to as defect magnetic trace displays, also known as correlated displays. Correspondingly, magnetic traces formed by the attraction of magnetic particles due to larger leakage magnetic fields caused by changes in the workpiece's cross-section and material property differences are referred to as non-correlated displays. Magnetic traces not formed by the leakage magnetic field on the workpiece surface are referred to as pseudo displays. The magnetic particle information used in this paper is shown in the following Table 2.  
Figure 3: Magnetic particle testing principle. Figure 2: Manual observation of bearing defects. Different causes result in magnetic traces with distinct geometric features, which are captured by the camera and form magnetic trace images, as shown in Figure 4.
# Classification of EEG Signals for Prediction of Epileptic Seizures
## 7 Results and Discussion

Table 6: Comparison of various deep learning models for feature extraction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Sensitivity (\%)** & **Specificity (\%)** & **Average Prediction Time** \\ \hline
[36] & 83.8 & 83.5 & 19.8 min \\ \hline
[37] & 70.5 & 75 & 1 min \\ \hline
[38] & 86.7 & 86.67 & 45.3 min \\ \hline
[39] & 81.2 & 84 & 5 min \\ \hline
[41] & 87.8 & 85.8 & - \\ \hline
[42] & 89.5 & 89.75 & - \\ \hline
[43] & 90.3 & 85.2 & 22.63 min \\ \hline
[44] & 81 & 61 & 38.35 min \\ \hline
[45] & 80.54 & 80.50 & - \\ \hline
[46] & 76.8 & 90 & - \\ \hline
[52] & 98.51 & 97.78 & - \\ \hline
[74] & 89.0 & 80.2 & - \\ \hline
**Proposed Method** & **93.8** & **91.2** & **19.5 min** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The \(k\)-fold cross-validation results of the proposed method. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Preprocessing** & **Feature Extraction** & **Classification** & **Sensitivity** & **Specificity** \\ \hline NOW interictal & & & \\ OW preictal (15 s overlap) & Custom-CNN & ANN & 89.34 & 85.4 \\ STFT BPF/BSF & & & \\ \hline NOW interictal & & & \\ OW preictal (15 s overlap) & Custom-CNN & DT & 81.2 & 76.4 \\ STFT BPF/BSF & & & \\ \hline NOW interictal & & & \\ O.W preictal (15 s overlap) & Custom-CNN & LSTM & 92.8 & 90.7 \\ STFT BPF/BSF & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of various classifiers used in the proposed system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**\(k\)** & **1** & **2** & **3** & **4** & **5** \\ \hline Sensitivity (\%) & 93.3 & 93.7 & 94 & 93 & 94.2 \\ \hline Specificity (\%) & 91 & 92 & 91 & 91.1 & 91.5 \\ \hline \(k\) & **6** & **7** & **8** & **9** & **10** & **Average** \\ \hline Sensitivity (\%) & 94.5 & 92.3 & 93.3 & 94.3 & 95.4 & 93.8 \\ \hline Specificity (\%) & 94.4 & 90 & 91 & 90 & 90 & 91.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of Sensitivity, Specificity and Average Anticipation time with recent techniques proposed by researchers. The receiver operating characteristics curves of state-of-the-art systems and the proposed technique are shown in Figure 6. The sensitivity of the system is plotted against the FPR in these receiver operating characteristics curves to compare the overall performance of several state-of-the-art approaches. This allows us to determine whether or not the performance is satisfactory. If an increase in sensitivity does not result in an increase in false positive alarms, the system is said to be working well. The suggested system clearly beats the current state-of-the-art systems in terms of achieving high TPR with low FPR. It is concluded from the aforementioned evidence that the system proposed in this study is an effective seizure prediction method.
## 8 Conclusions and Further Developments
* Diversified post-occupancy surveys on perception of the luminous environment (or visual comfort rating) could be used for training algorithms and defining prediction models resulting in average virtual occupant typology. Or, with such databases, occupants' preferences profiling can be carried out to adjust traditional or new visual comfort performance metrics [128]. * Furthermore, utilizing personalized and complex visual comfort performance metrics would yield the analysis more complete. These could include demographics and spatial dependent factors by fine-tuning the correlating between body response (in this case the eye for visual comfort, using \(d\) and/or \(DEO\)) with environmental parameters (i.e., \(E_{v}\), \(E_{R}\) or \(E_{p}\), and \(DGP\)). * To assure that such calculations are done within applicable computational time, annual and spatial simulations could be: (i) performed using daylight coefficients, or cubic illuminance, instead of rendered-image-based analysis [72]; (ii) they can be structured in a way that these can be parametric so they can be run in parallel through cloud computing services [74]; (iii) Or, as a mid-term solution, simulations can be decomposed in such a way that many smaller and more specialized cores can be used to process the computational task (delivering higher computational performance) in GPUs [75]. In conclusion, this work is foreseen as a catalyzer to modernize the traditional way of assessing visual comfort, estimating visual perception and rating the lighting provision. Transiting from monitoring/studying the environment onto monitoring/studying the occupant response or occupant interactions itself. Motivating the inclusion of the occupant demographic features could render the analysis complete and more accurate. Thus, boosting the capacity of designer to predict the actual performance of buildings and to reduce the performance gap issues stemming from the occupants visual dissatisfaction. Conceptualization, J.D.B.C. and T.P. ; methodology, J.D.B.C. and T.P. ; validation, J.D.B.C., T.P., M.K. and G.L. ; formal analysis, J.D.B.C. ; investigation, J.D.B.C., T.P., M.K., G.L., A.G.M. and A.S.; resources, J.D.B.C., T.P., A.G.M. and A.S.; data curation, J.D.B.C., T.P., M.K., G.L., A.G.M. and A.S.; writing--original draft preparation, J.D.B.C. ; writing--review and editing, J.D.B.C., T.P., M.K., G.L., A.G.M. and A.S.; visualization, J.D.B.C. and T.P. ; supervision, T.P., M.K., G.L. and A.G.M. ; funding acquisition, J.D.B.C., T.P., M.K., G.L., A.G.M. and A.S. All authors have read and agreed to the published version of the manuscript. This research was funded by ABC Department--Politecnico di Milano (finanziamento delle attivita per la valorizzazione della ricerca e TM DABC). The author [PERSON] acknowledges the financial support from the Slovenian Research Agency (research core funding No. P2-0158). The author [PERSON] acknowledges the financial support from the Norwegian Research Council (research project FRIPRO-FRINATEK no. 324243 HELIOS). Not applicable. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:
## 5 Case Study
### Definition of Requirements  
For this use case, the following functional requirements must be satisfied by the application pipeline:  
1. Video capture with quality higher than 720p at 15 fps;
2. Multichannel audio capture;
3. Object tracking of vehicles;
4. Transmission of traffic analytics and raw multichannel audio and video of accident near misses via limited cellular plan.  
A major constraining factor in this application is the reliance on data limited cellular connectivity. This excludes the possibility of retrieving continuous video and multichannel audio data for the post hoc detection of accident events, which would exhaust cellular plan data limits of \(\approx\)50 GB/month very quickly. To alleviate the impacts of this constraint, we can leverage recent advancements in compact but high-power compute devices to push the event detection processes to the sensor itself, so only salient events are transmitted over constrained network. This is a common need in longitudinal sensing research, where in practice the large majority of collected raw data contains few or no events of interest.
## 3 Proposed Methods
### Stereo Taillight Matching  
As mentioned in Section 1, the existing stereo matching approaches have a limitation on computational complexity. In our proposed stereo matching method, we are only concerned about determining the one-to-one correspondence of the interesting objects, which are taillight regions, whereas the remaining part of the image is ignored, as illustrated in Figure 12. Compared to existing studies [21; 22; 23; 24], it significantly reduces the computational complexity, which is mandatory for real-time applications.  
Figure 11: Detection result: (**a**) before taillight verification; (**b**) after taillight verification.  
In reality, it is not able to ensure that the correspondences for every taillight can always be found out. Sometimes, one taillight appears on one image but cannot be observed in another image. For example, in Figure 13, a taillight denoted by the green arrow in the right image is fully occluded in the left image.  
Figure 12: Taillight correspondence on the two images (the corresponding taillights are marked by the same label): (**a**) left image; (**b**) right image.  
In another case, a taillight that is close to the image border of one image is likely to be out of view of the other image, as illustrated in Figure 14.  
The above phenomena indicate that the stereo triangulation approach cannot always be applied, as it requires the pixel coordinates on two different images. This issue can be resolved by keeping track of the taillights frame-by-frame. At the instant the taillight is missed from the image, its current position can be predicted when considering the position in previous frames. However, the tracking process is beyond the scope of this study. Therefore, we attempt to determine all possible correspondences from the list of detected taillights in the two images and ignore some cases when there is a taillight in one image that cannot be found in another image.  
The remainder of this subsection is organized as follows. In Section 3.2.1, we introduce a procedure for matching the taillights in the left and the right images. In Section 3.2.2, a list of relevant features to the matching procedure is explained. Additionally, the stereo taillight matching classifier based on the gradient boosted tree is proposed in Section 3.2.3.
# Analysis of Life Cycle Environmental Impact of Recycled Aggregate
## 5 Discussion  
Recently, the application of recycled aggregate has been expanded to major concrete structure elements, such as the columns and beams of construction structures. For concrete, the major material used in the construction industry, environmental impact analysis is required because it discharges many substances with a high environmental load over its life cycle from production to construction, maintenance, and destruction/disposal. Concrete is a mixture of cement, aggregate, and admixture. Cement production consumes a large amount of energy during the processes of extracting limestone and clay and manufacturing clinker. Moreover, soil erosion or ecosystem destruction may occur in the process of collecting the necessary aggregate. Energy is also consumed in the course of transporting materials, such as cement and aggregate, to concrete manufacturers, and when producing concrete in batch plants, various high-environmental-load substances are discharged into the air, water, and soil.  
As the concrete production process impacts the environment in a variety of ways, it was necessary to assess various environmental impact categories as shown Figure 4. First, for concrete with a compressive strength of 24 MPa, a life cycle assessment was performed according to the volume fraction (substitution rate) of recycled aggregate (R.G) used instead of natural aggregate. As the volume fraction increased, GWP, one of the environmental impact categories, increased by 11-34% compared to when only using natural aggregate. This is because CO\({}_{2}\), CH\({}_{4}\), and N\({}_{2}\)O emissions, which all have a major effect on GWP, are larger during the production of recycled aggregate than during the production of natural aggregate. As the volume fraction of recycled aggregate increased to 10%, 20%, and 30%, the ADP decreased by 9-29% compared to the ADP of the concrete with natural aggregate only. This is because antimony (Sb), hard coal, and crude oil emissions, which affect ADP, were smaller during the production of recycled aggregate than during the production of natural aggregate.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
### Survey Methodology
#### 1.4.6 Screening and Selection  
The initial number of screened or collected papers was \(n=370\), which was then, according to a critical evaluation, narrowed down to the mentioned scope, resulting in a total of \(n=83\) papers and, specifically, \(n=25\) papers for a critical analysis of recent work on Arabic OCR and text correction.
## 3 Results
### EEG Power Spectrum at Pre and Post Priming Intervention
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multicolumn{2}{c}{**\(T\)**} & \multicolumn{2}{c}{**\(T\times G\)**} \\ \cline{2-7}  & \(F\) & \(\boldsymbol{\eta_{p}}^{2}\) & _Bout-Bin_ & \(F\) & \(\boldsymbol{\eta_{p}}^{2}\) \\ \hline REST EC & theta & 3.98 & 0.06 & \(-0.050\) & 4.86 \({}^{\text{a}}\) & 0.08 \\ alpha & 5.04 \(\ast\) & 0.08 & \(-0.061\) & 0.01 & \(<\)0.001 \\  & SMR & 4.67 \({}^{\text{a}}\) & 0.09 & 0.054 & \(<\)0.001 & \(<\)0.001 \\ beta & 4.45 \({}^{\text{a}}\) & 0.07 & \(-0.033\) & 2.13 & 0.04 \\ \hline REST EO & theta & 8.89 \(\ast\) & 0.13 & 0.056 & 0.64 & 0.01 \\ alpha & 18.17 \(\ast\) & 0.24 & 0.096 & 0.07 & 0.001 \\  & SMR & 33.62 \(\ast\) & 0.38 & 0.015 & 0.61 & 0.01 \\  & beta & 5.21 \(\ast\) & 0.08 & 0.033 & 0.01 & \(<\)0.001 \\ \hline NFT EO & theta & 4.41 \({}^{\text{a}}\) & 0.07 & 0.039 & 0.77 & 0.01 \\ alpha & 20.67 \(\ast\) & 0.26 & 0.109 & 0.65 & 0.01 \\  & SMR & 0.02 & \(<\)0.001 & 0.015 & 0.02 & \(<\)0.001 \\ beta & 0.79 & 0.01 & 0.012 & 0.17 & 0.003 \\ \hline \hline \end{tabular}
\begin{tabular}{c} REST, the rest task; NFT, the alpha neurofeedback training; EC, eyes closed, EO, eyes open; \(T\), time; \(G\), group, Bout-Bin, the difference between log\({}_{10}\) means from the 60 subjects. Significant tests are marked with asterisks (\" \(p<0.05\), \"\(p<0.01\), \"\(p<0.01\)).  
\end{table} 
Table 2: RM ANOVA of pre-(Bin) and postintervention (Bout) power spectra for three conditions. Figure 4: EEG power spectra at Bin and Bout. Estimated marginal means are log-transformed absolute power (\(\mu\)V\({}^{2}\)) with 95% confidence intervals. During REST EC, both groups show reductions in alpha, CG also has reductions in theta, while EG increases SMR. While for the REST EO, both groups show up-regulation of alpha, similarly to the NFT EO task.
## 6 Evaluation Metrics  
To quantitatively assess the performance of the implemented MPPT algorithms under various operating conditions, six key metrics were employed. These metrics evaluate accuracy, stability, and dynamic response characteristics, providing comprehensive insights into each algorithm's capabilities.