esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Characteristics of Temperature Change and Mechanism Analyses  
In order to know more about the mechanism of the temperature change, \(\Delta T_{ave}\) is studied in combination with the temperature change contributed by the thermoelastic effect, friction effect, and damping effect during the loading process. It should be noted that the heat conduction effect is not analyzed here because it does not produce or release heat but only transfers heat between different particles. The microcrack number is also compared here to observe its influence on the temperature change. The curves are shown in Figure 9. According to the characteristics of temperature change, the whole process is divided into three stages: stage a, stage b, and stage c.  
Stage a: The average temperature change \(\Delta T_{ave}\) increases linearly with increasing axial strain. The axial stress also increases linearly in this stage. The temperature contributed by the thermoelastic effect grows significantly while the curves for the other effects remain about zero, indicating that the temperature rise is almost entirely caused by the thermoelastic effect. The number of microcracks is almost zero, meaning that no obvious crack propagation occurs at this stage.  
Stage b: The average temperature change \(\Delta T_{ave}\) continues to grow at a decreasing growth rate until it reaches the peak value. The thermoelastic effect still plays a dominant role in the temperature sources, and the growing trend is very similar to that of \(\Delta T_{ave}\). In this stage, the friction effect begins to produce some temperature and keeps increasing slowly, and the number of microcracks begins to increase obviously, which leads to a lot of friction.  
Stage c: The average temperature change \(\Delta T_{ave}\) decreases significantly, accompanied by a significant decrease in axial stress. The thermoelastic effect is also obviously weakened, while the friction effect and damping effect are obviously enhanced. The number of microcracks continues to increase rapidly, and the friction effect produces more heat while the local stress drop makes the temperature decrease with the rapid crack propagation.  
The stress and temperature effects at different stages are very different, owing to the different internal stress distribution and crack development at different stages. The internal meso-mechanism needs to be explored more thoroughly.  
The model contains many parameters, and different parameters have different effects on the model. Several important parameters, including the thermal conductivity, friction coefficient, thermal expansion coefficient, and particle size ratio, are considered in the following studies to understand their influences on the thermal and mechanical behaviors of the model.
# Nucleation Mechanism and Rupture Dynamics of Laboratory Earthquakes at Different Loading Rates
## 3 Results
### Effect of Loading Rate on Rupture Velocity  
Figure 14 shows the normalized rupture velocity at different loading rates. \(V_{S}\) is the S-wave velocity of the sample. Rupture velocities are calculated from the shear strain recordings. There is no clear relationship between the rupture velocity and the loading rate from Figure 14. Furthermore, the rupture velocity is distributed in the range from about 0.4 \(V_{S}\) (sub-Rayleigh wave rupture) to about 1.7 \(V_{S}\) (super-shear wave rupture), except the forbidden zone (\(V_{R}-V_{S}\)). It is also found that the ruptures are all sub-Rayleigh events [35]. Moreover, one study proposed that the dynamic rupture velocity increases with the loading  
Figure 12: Nucleation lengths as a function of loading rate. The light color symbols represent the actual values. The dark color symbols are the mean values with a 95% confidence interval. The dotted line is the fitting curve based on the actual values at different loading rates and the theoretical model.  
rate, and that the dynamic rupture velocity can exceed the shear velocity only at the fastest loading rate (1 mm/s in their work) [33]. The above results are different from our results, which may be due to the different loading ways and stress states.
## 4 Results
### Impacts of LUCC on GPP
The medium GPP values (400-600 gC m\({}^{-2}\) year\({}^{-1}\)) were found on the edge of the metropolitan areas or the water bodies, where land cover may change during the year. The slope of the linear regression on the time series of GPP_ref at each pixel served as the indicator of the change rate of GPP. The spatial pattern of the change rate shown in Figure 11b generally agrees with the LUCC map shown in Figure 8. The most prominent decreases in GPP occurred around metropolitan areas and a few water bodies, where cropland and forest were transformed into built-up areas. Significant decreases can also be found in the northwest of the study area, because cropland was transformed to ponds for aquatic plants and fisheries. Large areas of increases in GPP can be found in the southeast of the study area, where patches of cropland were transformed into forest. Interestingly, a significant increase in GPP throughout the 14 years can be found along the Huyu highway (through the central study area), along which the green \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Grass** & **EBF** & **DBF** & **Crop** & **Built-Up** & **Bare** & **Water** \\ \hline
**Grass** & 0 & \(-5.9\) & \(-5.5\) & \(-42.2\) & 2.5 & 3.5 & 8.5 \\
**EBF** & 7.6 & 0 & 0 & 121.8 & 0.1 & 1.0 & 12.2 \\
**DBF** & 6.7 & 0 & 0 & 71.5 & 2.4 & 6.8 & 52.7 \\
**Crop** & 46.0 & \(-93.3\) & \(-65.4\) & 0 & 0 & 374.2 & 356.5 \\
**Built-up** & \(-8.3\) & \(-0.7\) & \(-17.1\) & \(-272.8\) & 0 & 0 & 0 \\
**Bare** & \(-5.9\) & \(-1.6\) & \(-13.2\) & \(-464.7\) & 0 & 0 & 0 \\
**Water** & \(-7.6\) & \(-9.1\) & \(-63.9\) & \(-361.2\) & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The cumulative gain (positive values) and loss (negative values) of the annual GPP_ref (Gg C) associated with land-use/cover change (LUCC) in each year from 2000 to 2013. Values smaller than 0.1 Gg C were set to 0. Figure 10: Time series of gain and loss of GPP_ref associated with the conversion from cropland to built-up areas, the conversion from forest (including EBF and DBF) to cropland, and the conversion from cropland to forest (including EBF and DBF). Figure 11: The map of GPP_ref in the study area in 2000 (**a**); and change rate of GPP_ref from 2000 to 2013 (**b**). The change rate was calculated as the slope of the linear regression of the annual GPP_ref per pixel. The slope of the linear regression that was not significant (\(p\) > 0.1) was set to 0.
## 2 Materials and Methods
### Two-Way Fluid-Structure Interaction  
To solve fluid-structure coupling problems [18], one must first derive fluid and structure solutions and establish their governing equations, which include fluid equations such as the continuity and momentum conservation equations and solid governing equations derived from [PERSON]'s second law. In fluid-structure coupling, the pressure is the physical quantity obtained after the fluid calculation, while the displacement is the physical quantity obtained after the structural calculation. The key lies in establishing a connection between the two. Based on the method used to establish this connection, the fluid-solid coupling solution methods can be categorized into the direct solution and separate solution methods. The direct solution method establishes a fluid-solid coupling control equation that links fluid control equations and solid control equations. This ensures that the physical quantities at the interface between the fluid domain and the solid domain, such as force and displacement, remain consistent. As a result, a large matrix equation that encompasses both fluid and solid variables is formed, allowing for its resolution in the same solver. The separate solution method eliminates the need for fluid-solid coupling control equations. The fluid solver (Fluent) and structural solver (Mechanical) are utilized to solve for the fluid and structural domains, respectively. Subsequently, the transfer of data between the two takes place.  
While direct solution methods offer higher computational accuracy, solving complex, large-scale fluid-structure interaction problems poses challenges in terms of convergence and resource consumption. Therefore, to address the fluid-structure-soil interaction problem presented in this paper, a separation approach provided by Ansys was employed. This approach involves the use of the two-way fluid-structure coupling method, as illustrated in the Figure 1 below.
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 3 Results and Discussion
### Discussion
Table 5 presents the macro-averaged \(F_{1}\) results of the discussed systems over the original DESED Public evaluation set and its degraded versions, showing that the improvement on performance obtained when combining several single-resolution systems into a multiresolution system does not always hold when facing very adverse conditions. Likely, this effect is due to the way in which our multiresolution systems are obtained. An average fusion of the scores of different models can result in more accurate scores when the individual scores are precise enough. On the other hand, in scenarios where the individual systems perform worse, the average fusion is not able to obtain better results. Nevertheless, under these adverse settings, multiresolution systems always hold better macro-\(F_{1}\) than the average result of the single resolution systems. In this sense, our multiresolution approach provides an improved robustness against these very adverse distortion and overlap scenarios. The best performing system across all the proposed degradations is \(5 res\), with an average macro-\(F_{1}\) of \(28.99\%\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Event Class** & **Public Evaluation** & **Overlapped Set** \\ \hline Alarm/bell/ringing & 0.16 & 0.78 \\ Blender & 0.11 & 0.76 \\ Cat & 0.06 & 0.76 \\ Dishes & 0.55 & 0.88 \\ Dog & 0.17 & 0.81 \\ Electric shaver/toothbr. & 0.30 & 0.80 \\ Frying & 0.30 & 0.80 \\ Running water & 0.12 & 0.74 \\ Speech & 0.41 & 0.79 \\ Vacuum cleaner & 0.05 & 0.72 \\ \hline Global (event-wise) & 0.31 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Value of overlap ratio (\(\rho\)) per class in each dataset. The overlap level of a class in a dataset is computed as the average \(\rho\) across all the events belonging to that class, where for each event \(\rho\) is the ratio between its overlapped time and its total length. The global \(\rho\) of each dataset is computed as the event-wise average.
## 4 Results and Discussion  
This article has presented the results of experimental studies of a multi-opening orifice with substitute constriction factor \(\beta=0.5\), mounted in a DN50 hydraulic measuring channel. Based on the obtained averaged results from experiments, flow characteristics were determined and described with a simplified power equation with an exponent close to 0.5, and with the determination coefficient close to 1, which indicates a very good adjustment of the designated model (trend line) to the averaged results obtained from the experiment. Based on calculations from averaged measurement data, the value of discharge coefficient \(C\) was determined in a progressing turbulent flow for low Reynolds numbers (\(Re=4700\)-19,500) in laboratory conditions.  
Figure 4 presents values of relative expanded uncertainty of discharge coefficient \(U(C)/C\) based on experimental studies, in the form of dots, for the multi-opening orifice (Figure 1) within the range of volume stream change \(q_{v}=0.167\)-0.683 dm\({}^{3}\)/s.  
When analyzing the presented graph, we may state that the relative expanded uncertainty of determining discharge coefficient \(U(C)/C\) does not exceed 1.25% for volume stream \(q_{v}\geq 0.35\) dm\({}^{3}\)/s in the studied measurement range. On the other hand, Figure 5 presents values of discharge coefficient \(C\), determined based on the conducted experimental studies, in the function of Reynolds numbers (\(Re=4700\)-19,500) with bars representing errors that result from calculating its expanded uncertainty \(U(C)\) with coverage factor \(k_{p}=2\) and confidence level \(p=0.95\).  
When analyzing the graph in Figure 5, we can assume that, for Reynolds numbers ranging \(9800\leq Re\leq 19,500\), the values of the discharge coefficient are close to a straight line, and the values only change a little. This allows us to assume the constant average value of discharge coefficient at \(C=0.6286\) and bars representing errors of determining its absolute expanded uncertainty do not exceed \(\pm 0.0076\).  
Figure 4: Distribution of relative expanded uncertainty of flow ratio \(U(C)/C\) dependent upon volume stream \(q_{v}\).
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.5 V2X Communication  
Vehicle-to-everything (V2X) communication is being utilized to develop automotive technology with low latency, higher data rates, and reliability due to the advantages of better traffic information systems, autonomous vehicles, and more dependable safety services. Various examples of communication between vehicles and pedestrians, vehicles and infrastructure, and vehicles and vehicles are shown in Figure 7. Due to its extensive transmission range and low end-to-end latency, D2D communication for cellular networks is better suited for V2X communication [92]. The high mobility in V2X communication  
Figure 6: D2D Communication Scenarios.  
will support 5G vehicle network nodes to move at the fastest possible rates, thereby complementing road and traffic safety services.
## 2 Materials and Methods
### Over/Under-Regulation Case  
Some innovative frequency regulation schemes allow over or under-regulation as a DoF. In such a case, D is no more a single value but an interval of values [D\({}_{\text{min}}\), D\({}_{\text{max}}\)]. This means that the droop curve shown in Figure 1 is modified as depicted in Figure 5. The over/under-regulation admits different dP for the same dF within the pink area. dP that can be applied is in the following interval.  
\[(\text{dF/F}_{\text{n}})\times(\text{P}_{\text{n}}/\text{D}_{\text{max}}) \times 100\leq\text{dP}\leq(\text{dF/F}_{\text{n}})\times(\text{P}_{\text{n }}/\text{D}_{\text{min}})\times 100, \tag{4}\]  
where D\({}_{\text{min}}\) is 0.060% and D\({}_{\text{max}}\) is 0.090%. The flexibility offered by the variable droop in the pink area can be exploited to restore the SoC without incurring in NPP. This scheme is applied, for instance, for provision of FCR in Germany [29].  
Figure 4: Schematization of state-of-charge (SoC) restoration strategy in the No Degrees of Freedom (DoF) case, with time on the x-axis: (**a**) SoC evolution; (**b**) power requested for FCR before, during and after the service interruption; (**c**), restoration power. Positive power means discharge. Red trend highlights that all the power requested while service is interrupted results in nonperformance (NP) and is accounted for NPP. Power is expressed per unit with respect to the nominal power of the battery.  
The droop value variable in time \(\rm D_{var}(t)\) adopted varies as a function of the instantaneous SoC(t) (Equation (5))  
\[\rm D_{var}(t)=D\times(1+R_{o/u}\times(SoC(t)-SoC_{target})/50), \tag{5}\]  
where the over/under-regulation ratio \(\rm R_{o/u}\) is the amount of energy that can be over/under-regulated as a percentage of regulating power: \(\rm R_{o/u}\) is 18%. It is coherent with FCR in Germany (where over-regulation only is allowed) [29].
# Situationally Sensitive Path Planning
## 4 Methodology
### Global Environment Layer
#### 4.1.1 [PERSON]'s Algorithm for Path Planning: A Review  
We base our global layer on [PERSON]'s algorithm (Algorithm 1) (we refer to this as \"Dijkstra\" from here on out), which is one of the most well-known and widely used shortest path heuristics in computing. [PERSON] greedily calculates the minimal distances between a source node and all other nodes on a weighted graph. The term \"greedy\" here broadly describes a class of functions that makes optimal choices in graph space (with respect to the current state and knowledge of the system at each time step) without reconsidering previous steps. [PERSON]'s algorithm operates on a weighted graph with edges that each maintain a scalar distance value. At the start of its computing, [PERSON] sets the initial distances for all vertices: 0 for the source node, and infinity (or some large scalar value) for all other nodes. At each time step, the heuristic then carries out the following:  
1. Selects, among a subset of unvisited nodes, the node that has the smallest distance cost. In the first algorithm step, this would default to the source node, which has a distance of 0.
2. For the selected node, considers all its unvisited neighbors and updates their distance values through the selected node. This is a form of knowledge updating whereby if a previous time step has already set a distance cost to an unvisited node but the new distance cost through the selected node is smaller, the smaller distance cost (through the selected node) overwrites the larger distance cost.
3. Marks the selected node as \"visited\", and loops back to Step 1, then continues looping until no more vertices remain in the unvisited set.  
The original version of [PERSON]'s algorithm produces the path cost from the source node to all other vertices but does not provide the shortest path itself. The shortest path variant of Dijkstra can be straightforwardly implemented by storing not just the shortest distance cost to each node but also a reference to a previous node that produced the shortest distance. Provided with both a source and target node, Dijkstra can return the shortest path by moving backwards from the target node to the source node after all distance calculations. Furthermore, this variant of Dijkstra can terminate the distance calculation loop early if the target node is visited, as there is no need to continue updating distances if the system has managed to reach the target node.
# Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners
## 5 Calculation Algorithm Possibilities in Commercial Treatment Planning Systems
### Pinnacle System  
The Pinnacle system (Philips Radiation Oncology Systems, Fitchburg, MA, USA) is based on a CCC fluence model developed by the Madison group in Wisconsin [95]. The point dose kernels are based on the EGS4 Monte Carlo code [11] and a unique kernel is used for the primary and scatter doses. To speed up the computation, the 'fast convolve' and 'adaptive convolution' methods have been implemented in Pinnacle as faster versions of the CCC model.  
For electron beams, Pinnacle employs a standard PBA based on the Hogstrom model, the so-called Fermi-Eyges-Hogstrom (FEH) model [37]. This algorithm was implemented in C language, based on the FORTRAN public domain version of the FEH code developed at the M.D. Anderson Cancer Center, Houston, TX, USA.  
For proton therapy, the Pinnacle dose calculation algorithm uses an improved Bortfeld-Bragg peak fitting function and enhanced multiple Coulomb scattering based on Monte-Carlo-simulated data that boosts accuracy over dose algorithms that use a water equivalent thickness (WET) approximation alone. The dose calculation includes all components of the delivery that are traversed along the proton beam's path, including line components (range modulator wheels, degraders), beam limiting devices (apertures), patient-specific modifiers (compensators) and the patient's anatomy. Pinnacle proton planning is used in European centers, e.g., the Rutherford Cancer Centers in the UK.
# Technology for Green Hydrogen Production: Desk Analysis
## 2 Background of Analysis
### Energy Production from Fossil Fuels and Renewable Sources
The majority of hydrogen is produced from natural gas without Carbon Capture, Utilization and Storage (CCUS) technologies, accounting for 62% of total hydrogen production [82]. This method, known as steam methane reforming (SMR), is the most common due to its cost-effectiveness and the widespread availability of natural gas. Approximately 18% of total hydrogen production is hydrogen produced in refineries as a by-product of naptha reforming, which is then used in various refinery processes such as hydrocracking and desulfurization. Hydrogen production from coal represents 19% of the total production. This method, known as coal gasification, is less environmentally  
Figure 3: Electricity production from fossil fuels, nuclear, and renewable, World [81]. Figure 2: Primary energy consumption by source, World, 2022 [80]. friendly than SMR due to higher carbon emissions. Less than 1% of crude oil was also used to produce hydrogen. Low-emission hydrogen production, which accounts for less than 1% of the total, is almost entirely from fossil fuels with CCUS. This indicates that while there is a push towards cleaner hydrogen production, it is still in its infancy. Hydrogen produced via water electrolysis, using electricity, is a very small fraction of the total, but it saw a significant increase of almost 20% from 2020. This growth reflects the increasing deployment of water electrolyzers, driven by the need for green hydrogen in various sectors, including transportation, industry, and power generation. The data shows a strong reliance on fossil fuels for hydrogen production, with natural gas being the dominant source. The shift towards cleaner production methods, such as water electrolysis powered by renewable energy, is growing but still represents a minor part of the overall hydrogen market. The increasing interest in hydrogen as a clean energy carrier and its potential role in decarbonizing various sectors underscores the need for further development and deployment of low-emission hydrogen production technologies. This includes scaling up electrolysis powered by renewable energy sources to produce green hydrogen, as well as enhancing CCUS technologies to reduce the carbon footprint of hydrogen production from fossil fuels. Renewable energy plays a crucial role in reducing the production cost of green hydrogen using electricity generated from renewable sources, despite the RES barriers [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 74; 75; 3; 11; 12; 76]. The integration of renewable energy into green hydrogen production significantly reduces costs by providing a low-cost and low-emission source of electricity, enabling scalability and flexibility, and leveraging policy support and technological advancements [74; 75; 76]. As the renewable energy sector continues to grow and mature, the production cost of green hydrogen is expected to decrease further, making it an increasingly viable alternative to fossil fuels in various applications.