esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Electrical Characterization of DBD Plasmas over the Water Surface  
The Lissajous curves were plotted for the DBD plasmas generated by four gas feedings mentioned previously, as shown in Figure 2. The applied voltage for air, nitrogen, and oxygen was adjusted to be 12 kV, whereas the applied voltage for argon was adjusted to be 6 kV to avoid arc discharge. The area of the curve determines the consumed energy per cycle time. The area of the curve was estimated for air, nitrogen, oxygen, and argon gas feedings to be 9.95 mJ, 9.82 mJ, 9.82 mJ, 4.64 mJ, respectively. By assuming the cycle time of 50 \(\upmu\)s, the consumed power was obtained to be around 200 W for each cycle time with air, nitrogen, and oxygen plasmas, whereas in the case of argon plasma, it was 93 W for each cycle time. Therefore, considering the duty cycle that is 0.2, the consumed power of DBD during the discharge (averaged) for air, nitrogen, and oxygen gas feeding is 40 W. In contrast, this value for the argon DBD is estimated to be 18.6 W. The consumed energy and power for one cycle time and averaged for each DBD were presented in Table 1.  
The Lissajous curve deviates (or rotates) a little bit with different gas feedings. This deviation indicates that using different gas feedings changes plasma conditions and thus affects the Lissajous curve. A similar deviation was observed in a DBD reactor with nitrogen and a mixture of nitrogen and oxygen species [20].  
Figure 2: Lissajous plot for air, nitrogen, oxygen, and argon DBD plasmas to calculate the consumed energy and power.
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 6 Open Issues and Future Research Directions
### Lack of Standardised Performance Metrics  
Another significant issue in video deepfake detection is the lack of standardised performance metrics, which makes it difficult to benchmark and compare different models [53]. Currently, various metrics such as accuracy, precision, recall, and AUC are used to evaluate detection systems, but each metric emphasises different aspects of model performance [65]. For instance, while accuracy measures the overall correctness of a model, precision and recall focus on its ability to handle false positives and false negatives, respectively [44]. This variation in evaluation methods leads to inconsistent and non-comparable results across studies, making it challenging to determine which models are more effective. Researchers should focus on establishing standardised performance metrics that reflect the unique challenges of video deepfake detection. A unified framework for evaluation could incorporate multiple aspects of performance, such as robustness to adversarial attacks, sen sitivity to low-quality data, and real-time processing capabilities. Additionally, researchers could explore composite metrics that combine the strengths of multiple evaluation criteria to create a more holistic view of a model's effectiveness in detecting deepfakes across various scenarios.
# Transitioning Broadcast to Cloud
## 2 Processing Chains in Broadcast and Cloud-Based Online Video Systems  
In this section, we will study commonalities and differences between processing chains in broadcast and online video systems. We focus on functions, formats, means for implementation of certain operations, and overall system characteristics such as reliability, processing granularity, and delays.  
The idealized chain of processing in broadcast distribution is shown in Figure 1, and the chain of processing in online video system is shown in Figure 2. Both are indeed conceptual and high-level.  
Figure 1: Conceptual diagram of processing operations in broadcast distribution.
## 2 Materials and Methods
### Specim Preparation and Mechanical Tests  
Four types of EAFs (shown in Figure 1) and one concrete ground with a regular plastic carpet were selected in this study to give a diversity of floor conditions. Igelkott Floor (23 mm thick, Igelkott golv AB, Stockholm, Sweden) is a synthetic rubber floor comprised of a continuous surface layer supported underneath by an array of thin rubber spikes; Kradal (13 mm thick, Classic Coachworks Pty Ltd., Mortdale, NSW, Australia) [9] is a closed celled foam based floor with a hard exterior surface of polyurethane elastomers; and SmartCells (25 mm thick, SATech, Chehalis, WA, USA) [11] is a synthetic rubber floor comprised of a continuous surface layer supported by an array of cylindrical rubber columns. Omnisports (8.5 mm thick, Tarkett Sites, Calhoun, GA, USA) [14] is a synthetic vulcanized rubber underlayment floor with a virgin rubber wear layer on top. A regular 2 mm plastic carpet on a rigid, concrete ground is set as a reference.  
All the specimens were cut from related flooring products. Since the Igelkott Floor/SmartCells are multi-layer structures supported underneath by an array of rubber spikes/columns, corresponding flat dog-bone shape specimens of the rubber surface layer were implemented in tension tests with an initial cross-sectional area of 16.80 mm\({}^{2}\)/25.37 mm\({}^{2}\) and a length of 21.50 mm/20.50 mm in the gauge section, respectively. The length-to-width ratio of the samples satisfies the requirement of a tensile testing standard for rubber-like materials [32]. Since the Kradal and OmniSports are made of polymeric foams, cylindrical specimens were investigated under compression tests and prepared with a dimension of 11.60 mm/8.45 mm in thickness and a diameter of 19.30 mm/11.85 mm, respectively (Figure 2). All the compression and tension tests were conducted at room temperature using the Instron Universal Testing Machine Model 5567. The compression or tension tests for each EAF were replicated at least three times to obtain an average value.  
Figure 1: Four types of energy-absorbing flooring samples compared in this study. The image of (**d**) OmniSports is from its official website ([[https://proffs.tarkett.se/](https://proffs.tarkett.se/)]([https://proffs.tarkett.se/](https://proffs.tarkett.se/)), accessed on 22 April 2023). (**a**) Igelkott Floor; (**b**) Kradal; (**c**) SmartCells; (**d**) OmniSports.
# A Systematic Review of the Role of BIM in Building Sustainability Assessment Methods
## 4 Results
### BIM Application in BREEAM  
From the performed analysis, five papers regarding the practical application of BIM in the BREEAM method are identified in Table 2. Between 2013 and 2019, different BREEAM versions have been addressed in all five publications. With the data from Table 2, Figure 3 was organised to present the BREEAM categories that were assessed using BIM. A commonly assessed category is Energy, attended in 4 out of 5 studies, followed by the Materials category, addressed in three studies. In total, the identified studies have assessed 20 different BREEAM criteria, in the categories of Materials, Energy, Land Use and Ecology, Management, Water, Waste, Health and Wellbeing and Pollution (8 categories out of 10). The Innovation and Transport categories have no assessed criteria. A common BREEAM version was used to understand the percentage of credits available to assess with BIM. As all the addressed versions are different, it is hard to define a common percentage for all. Thus, this analysis was only made for BREEAM UK Refurbishment and Fit-out 2014, as it is the publication with most assessed criteria (eight credits). For this case, a BIM-based procedure was possible to apply for the assessment of 24% of the scheme version credits (8 out of 34). Nevertheless, identified authors usually were able to assess approximately seven credits from each BREEAM version.  
Figure 2: PRISMA flow diagram [37].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Journal** & **Title** & **BRECAM Version** & **Assessed Criteria** & **Software** \\ \hline
2013 & _Energy and Buildings_ & \begin{tabular}{c} Variations in results of building energy \\ simulation tools, and their impact on BRECAM \\ and LEED ratings: A case study [38] \\ \end{tabular} & \begin{tabular}{c} New Construction \\ Non-Domestic \\ Buildings 2011 \\ \end{tabular} & \
\begin{tabular}{c} Ene 01 \\ \end{tabular} &
\begin{tabular}{c} TAS, EnergyPlus, IES-VE \\ \end{tabular} \\ \hline
2015 & \begin{tabular}{c} _International Journal of_ \\ _Architectural Computing_ \\ \end{tabular} & \begin{tabular}{c} An “Environmental BIM” Approach for the \\ Architectural Schematic Design Stage [39] \\ \end{tabular} & \begin{tabular}{c} International 2013 \\ \end{tabular} & \begin{tabular}{c} Ene 01, Ene 04 \\ \end{tabular} & \
\begin{tabular}{c} Rovit, ArchiWIZARD, \\ Le 06 \\ \end{tabular} &
\begin{tabular}{c} Rovit, ArchiWIZARD, \\ Excel, PEREN \\ \end{tabular} \\ \hline
\begin{tabular}{c} Ene  
With regard to the software use (Figure 4), Autodesk Revit, IES-VE (IES, Glasgow, United Kingdom) and Visual Studio (Microsoft Corporation, Redmond, CA, USA) were identified as commonly applied to assess BREAM criteria (all used in 2 out of 5 studies). In total, 16 different software were used to assess 20 BREEAM criteria.  
Concerning the preferred journal by researchers, _Automation in Construction_ has been chosen for the publication of 2 out of 5 studies. Only one publication was found for the remaining three journals.
## 4 Simulation Results and Analysis
### Error Distribution Analysis  
Figure 12 presents the comprehensive error distribution across all test points and estimates. The histogram reveals a right-skewed distribution, typical for error measurements in localization systems. The mean error (red dashed line) is slightly larger than the median error (green dotted line), consistent with the right-skewed nature of the distribution. This suggests that while most errors are small, occasional larger errors pull the mean higher. Most errors are concentrated in the left part of the distribution, indicating that the majority of estimates have relatively small errors, which is a positive indicator of the system's overall accuracy. The long tail to the right shows that larger errors do occur, albeit less frequently. These could represent estimates at the edges of our test area, where the system's performance is expected to degrade. The peak of the distribution occurs at a low error value, further confirming the system's overall accuracy for most measurements.  
Figure 11: Detailed view of localization results for a single position at (1.0 m, 1.0 m). The thin red cross represents the actual position. Red dots are estimated positions. The blue dashed circle shows the maximum error, and the green dotted circle shows the average error.
# Fate of Soil Carbon Transported by Erosional Processes
## 1 Introduction  
Accelerated soil erosion by water and wind is a predominant process that impacts the soil carbon (C) budget [1] through a range of interacting processes [2]. The severe problem of accelerated soil erosion is widely regarded as a global menace, which is also threatening the agricultural resource base [3] that sustains planetary processes [4] and generates numerous ecosystem services. Erosional processes include breakdown of structural aggregates, and selective removal and redistribution of the sediment and displaced C on the landscape. Displaced C may be mineralized or buried [5] depending on the site-specific conditions. Whereas reaggregation and deep burial may stabilize the displaced C against microbial processes [6], decomposition induced by aggregation breakdown and alterations in soil moisture and temperature regimes over the landscape may affect the emission of carbon dioxide (CO\({}_{2}\)) under aerobic environments [7] and those of methane (CH\({}_{4}\)) and nitrous oxide (N\({}_{2}\)O) under anaerobic conditions [8]. Therefore, erosion-induced transport of soil C followed by its redistribution en route to depositional/depression site(s) may lead to emission of all three greenhouse gases (GHGs). With a large amount of sediment and sediment-laden C being transported over the landscape, and the magnitude of sediment being increased because of anthropogenic activities, it is critically important to understand the complex and interactive processes affecting the fate of C being displaced and transported over the landscape (e.g., sediment) and emission of GHGs to the atmosphere (i.e., windborne dust). Emissions of all three GHGs by erosional processes, at all landscape positions from summit to foot slope and into the atmosphere, must be understood, quantified, and accounted for the purpose of compiling the soil C budget under diverse land use and soil management systems.  
[PERSON] et al. [9] reported that transport of soil sediments by erosional processes increased SOC mineralization by 43% under Mediterranean conditions. The global menace of soil erosion (hydric and aeolian) is projected to be aggravated by climate change because ofthe increase in climatic erosivity [10] and the frequency of extreme events [11; 12]. Therefore, the objective of this review article is to deliberate the fate of soil C being transported over the landscape, identify ecosystem conditions that lead to either mineralization and emission of GHGs or to sequestration of the transported C and its stabilization, understand the net effect of erosion/deposition processes as a source or sink of GHGs, explain the effect of global warming on erosional processes, and outline some researchable priorities. Rather than being a comprehensive and inclusive review of the available literature, this article merely provides some pertinent examples of the processes, factors and causes which control the fate of carbon being transported by erosional processes.
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 3 Rotordynamic Coefficient Identification Based on Orbit Decomposition Method
### Fluid-Induced Force Superposition
\right] \tag{14}\]  
In Figure 6 and Equations (13) and (14), the tangential force \(F_{t}\) and radial forces \(F_{r}\) are positive when they are in the same directions as the forward whirl and radial displacement, respectively. Conversely, the tangential force \(F_{t}\) and radial forces \(F_{r}\) are negative when they are in the opposite directions of the forward whirl and radial displacement, respectively. Figure 5: Fluid-induced force superposition based on orbit decomposition method. Substituting Equations (13) and (14) into Equation (12), the time-dependent fluid-induced forces of the elliptical orbit model can be calculated as:  
\[\left[ \begin{array}{c}F_{x}\\ F_{y}\end{array} \right]=\left[ \begin{array}{c}F_{rf}\cos(\Omega t+\alpha)-F_{ tf}\sin(\Omega t+\alpha)+F_{rb}\cos(-\Omega t+\alpha)-F_{tb}\sin(-\Omega t+\alpha)\\ F_{rf}\sin(\Omega t+\alpha)+F_{tf}\cos(\Omega t+\alpha)+F_{rb}\sin(-\Omega t+ \alpha)+F_{tb}\cos(-\Omega t+\alpha)\end{array} \right] \tag{15}\]  
For the circular orbit whirl model, the rotor whirls at a speed of \(\Omega\) around the stator center \(o_{s}\), and rotates at a speed of \(\omega\) around the rotor center \(o_{r}\). As shown in the left of Figure 7, the rotor location and fluid domain change with time when observed from a stationary frame. Transient analysis and moving mesh are needed. However, observed from the frame of reference rotating at a speed of \(\Omega\), the rotor location remains unchanged, as shown in the right of Figure 7. The unsteady problem becomes the steady one by solving the flow field in the rotating frame of reference. As the radial and tangential forces of the circular orbit model can be calculated with the steady method, the fluid-induced forces of the initial elliptical orbit model can be obtained with the steady method. The time-consuming transient computations can be avoided.
# Research on the Trajectory and Operational Performance of Wheel Loader Automatic Shoveling
###### Abstract  
In the automatic shoveling operation of wheel loaders, the shovel trajectory has a significant influence on the operation's performance. In order to obtain a suitable shovel trajectory and optimize the automatic shovel performance of the loader, we developed a test platform for the operational performance of loaders. Nine parallel shoveling trajectories of different depths were designed according to the coordination shoveling method. The formula for calculating the operational performance is established. The automatic shoveling test is performed according to the designed trajectory to obtain the real-time shoveling parameters, which are then combined with the calculation formula to calculate the operating parameters of the loader. Finally, the actual range of operational performance parameters is calculated by the normal distribution. The test results show that the trajectory with a shovel depth of 400 mm is the optimal trajectory. It was also verified by comparing manually controlled shoveling with it. With only a 1% difference in the full bucket rate, the operation time of automatic shoveling was 15.3% less than manually controlled shoveling, fuel consumption was 4.7% less, the energy consumption of practical work performed was 10.7% more, and maximum operation resistance was 20.5% lower. Therefore, the operational performance of the loader following this trajectory for shoveling meets the actual requirements.  
wheel loader; shoveling trajectory; automatic shoveling; normal distribution; operational performance
## 3 Results & Discussion
### Experimental Results  
The contribution of LiDAR features was clarified by using two training scenarios. In the first scenario, the deep residual U-Net model was trained using satellite images only, while the second scenario includes the feature-wise fusion of optical and LiDAR datasets. In both scenarios, the U-Net architecture is integrated with ResNet-18, ResNet-34, ResNet-50, and ResNet-152 backbones for the feature extraction, leading to eight different model training setups. The results obtained from the analyses are given with the models' prediction metrics in Table 1. The model with the best results for each scenario is indicated in bold.  
The results clearly showed that the integration of optical satellite imagery with LiDAR features enhances the performance of road segmentation by 1% to 5% in all models. For each backbone configuration, the scenario with the LiDAR data performed better than satellite image-only training. On the other hand, the ResNet-152 backbone performed better for feature extraction in U-Net architectures, indicating that increasing the depth of the model increased the performance of segmentation as well.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model No**} & \multirow{2}{*}{**Input Data**} & \multirow{2}{*}{**Backbone**} & \multicolumn{5}{c}{**Metrics**} \\ \cline{3-6}  & & & **Precision** & **Recall** & **F1-Score** & **IoU** & **Kappa** \\ \hline
1 & Image & & 0.828 & 0.817 & 0.820 & 0.711 & 0.790 \\ \hline
2 & Image + LiDAR & & **0.842** & **0.820** & **0.824** & **0.739** & **0.799** \\ \hline
3 & Image & & 0.825 & 0.825 & 0.823 & 0.717 & 0.796 \\ \hline
4 & Image + LiDAR & & **0.839** & **0.855** & **0.845** & **0.748** & **0.821** \\ \hline
5 & Image & & 0.854 & 0.82 & 0.834 & 0.735 & 0.810 \\ \hline
6 & Image + LiDAR & & **0.865** & **0.829** & **0.845** & **0.750** & **0.821** \\ \hline
7 & Image & & 0.862 & 0.809 & 0.830 & 0.732 & 0.810 \\ \hline
8 & Image + LiDAR & & **0.880** & **0.852** & **0.863** & **0.781** & **0.843** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The prediction statistics of all models.
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 3 Experimental Verification
### Simulation with Specified Traffic
#### 3.2.2 Scenario II, Unexpected Braking in Target Lane  
In this scenario, the front vehicle in target lane \(V_{tF}\) take an emergency brake at the start of lane change. The deceleration of brake is selected as \(-4\) m/s\({}^{2}\), \(-5\) m/s\({}^{2}\) and \(-6\) m/s\({}^{2}\) respectively, and the duration of brake is 3 s. Here, \(V_{H}\) can choose to continue the lane change by reduce the speed, or change back to the original traffic gap and wait for future opportunities. Simulation results are compared in Table 4.  
The results with \(-6\) m/s\({}^{2}\) emergency braking is shown in Figure 8. In the simulation C, the end speed of \(V_{H}\) after lane change is too fast, causing a collision with \(V_{tF}\) at \(t=5.15\) s. In the simulation B, \(V_{H}\) attempts to return to original lane \(L_{1}\) after the unexpected event, but fails since it is too close to the front vehicle \(V_{sF}\). So \(V_{H}\) can only take a hard brake stop to avoid the accident. In the simulation A, \(V_{H}\) returns to \(L_{1}\) successfully with minimal speed change, and continue the lane change to the traffic gap in the head of \(V_{tF}\).  
Figure 6: The traffic scenario used for lane change simulation. \(V_{H}\) is changing from \(L_{1}\) to \(L_{2}\). The traffic vehicles in \(L_{1}\) and \(L_{2}\) are labeled \(V_{sF}\), \(V_{sR}\), \(V_{tF}\) and \(V_{tR}\). \(d_{sR}=d_{tF}=30\) m, \(d_{sF}=d_{tR}=20\) m. All vehicles have the same initial speed of 18 m/s.  
Figure 7: Simulation results for Scenario I. From top to bottom, the graphs of host vehicle \(V_{H}\) are: 2-D trajectory, longitudinal velocity, longitudinal acceleration, lateral position, lateral velocity, lateral acceleration and the distance to the nearest front/rear vehicle.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Traffic Scenario** & **A** & **B** & **C** \\ \hline Scenario I, & \(-2\) m/s\({}^{2}\) & ✓ & ✓ & ✓ \\ Scenario I, & \(-3\) m/s\({}^{2}\) & ✓ & \(\circ\) & \(\times\) \\ Scenario I, & \(-4\) m/s\({}^{2}\) & \(\circ\) & \(\circ\) & \(\times\) \\ Scenario II, & \(-4\) m/s\({}^{2}\) & ✓ & ✓ & ✓ \\ Scenario II, & \(-5\) m/s\({}^{2}\) & \(\circ\) & \(\circ\) & \(\times\) \\ Scenario II, & \(-6\) m/s\({}^{2}\) & \(\circ\) & \(\times\) & \(\times\) \\ Scenario III, & \(+2\) m/s\({}^{2}\) & ✓ & ✓ & \(\times\) \\ Scenario III, & \(+3\) m/s\({}^{2}\) & \(\circ\) & \(\circ\) & \(\times\) \\ Scenario III, & \(+4\) m/s\({}^{2}\) & \(\circ\) & \(\times\) & \(\times\) \\ \hline \end{tabular}
\end{table}
Table 4: Experimental Results of Different Scenarios.