esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Permeability of Ecological Slopes under Rainfall Infiltration Conditions
## 3 Test Results and Analysis
### Calculation of Unsaturated Permeability Coefficient  
In order to study the influence of different vegetation overburden on the permeability of the shallow soil layers of slopes under rainfall, the transient profile method is used herein to calculate the permeability coefficients of the shallow soil layers of the four overburden slopes [18; 29]. The transient profile method refers to a method that utilizes the water content and matrix suction of each monitoring point to calculate the average water flow velocity and average hydraulic gradient of each monitoring point and divides the average water flow velocity by the corresponding average hydraulic gradient to estimate the unsaturated permeability coefficient of the soil.  
The unsaturated permeability coefficient varies with the variation in water content, so a small soil column of a certain time interval must be selected as the research object, and every two adjacent vertical slope surface water content monitoring points are chosen as the calculation interval here. In the process of calculating the unsaturated permeability coefficient, it is divided into three parts: the upper part of the slope surface, the middle part of the slope surface, and the lower part of the slope surface. The following is the specific calculation process of the unsaturated permeability coefficient of the shallow soil at a vertical distance of 20 cm and 40 cm from the slope surface.  
Figure 8: Matrix suction for shallow soil of four overburden slopes versus time: **(a)** soil matrix suction of the upper surface part of four overburden slopes versus time; **(b)** soil matrix suction of the middle surface part of four overburden slopes versus time; **(c)** soil matrix suction of the lower surface part of four overburden slopes versus time.
# Characterization of Deburring by Abrasive Flow Machining for AL6061
## 3 Preliminary Testing
### Test Setup (System and Abrasive Flow Medium)  
An abrasive flow medium contains abrasive particles, a viscosity controlling agent, and a base fluid. Low-flow speed and low viscosity may cause sedimentation while high-flow speed and high viscosity may overload the deburring system. Practical ranges of flow speed and viscosity were identified through two-stage testing. First, the static sedimentation test was performed for a set of media with different viscosities. Second, for the selected viscosity range, system drive tests were performed at various flow speeds with reference to the constricted channel above the protrusion. The tests were carried out on abrasive particles of grain size #100.  
In the static test, the abrasive flow media were sealed in plastic containers and observed for 72 h to monitor sedimentation. As shown in Figure 4, sedimentation was observed below 43.8 poise.  
Figure 1: Schematic diagram of the abrasive flow deburring system with the specimen.  
Figure 3: Dimensions of the protrusion with the burr edges (in mm).  
Figure 2: Specim for preliminary testing.  
In the dynamic sedimentation test, the transparent hoses were monitored for 2 h of the deburring system operation with a preliminary specimen. As seen in Figure 5a, it was possible to visually identify sedimentation. As listed in Table 1, the viscosity without sedimentation, with the widest speed range, was 43.8 poise. It was confirmed that the selected 43.8 poise fluid was operable at flow speeds of 4.5 m/s and 6.7 m/s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Viscosity** & **91.6 Poise** & **63.5 Poise** & **43.8 Poise** & **30.2 Poise** & **20.8 Poise** \\ \hline Flow speed & X1  & X & X & X \\
2.2 m/s & Flow speed & O2  & O & O & X & X \\
4.5 m/s & Flow speed & NA3  & NA & O & O & O \\ \hline \end{tabular}
\end{table}
Table 1: Dynamic sedimentation test results for different viscosity and flow speed combinations.  
Figure 4: Results of the static sedimentation test for abrasive fluids with different viscosities after 72 h (from left to right—91.6, 63.5, 43.8, 30.2, and 20.8 poise).  
Figure 5: Visual monitoring of sedimentation in the transparent connection hoses: (**a**) sedimentation; (**b**) no sedimentation.
## 4 Discussion
### Ozone Availability for OCI  
Daily column ozone fields are necessary to apply our underwater UV retrieval algorithm to OCI. OMI no longer achieves daily global coverage due to a partial instrument blockage at the Earth-view entrance port. Total column ozone is also available from the series of Ozone Mapping and Profiler Suite (OMPS) nadir mapper (NM) instrument, which will be operating on the Joint Polar Satellite System (JPSS) missions into the next decade. However, it would be preferable for our retrievals to use total ozone produced from the OCI itself. [PERSON] et al. (2019) demonstrated the feasibility of retrieving ozone from PACE, if the UV spectral range of OCI could be extended to bands centered at 322 and 325 nm. They find the quality of retrieved ozone from their approach suitable for the atmospheric correction of ocean color products, and the same is true for underwater UV irradiances.  
Another possibility to obtain total column ozone is the use of monthly climatological values. However, some differences between the daily ozone and climatological amounts will inevitably exist. They will lead to errors in the surface and underwater irradiances. For the typical 5 DU (2%) ozone uncertainty, the surface UV uncertainty is 1-3% at 310 nm, increasing to 5-8% at 300 nm and 10-15% at 295 nm. The UV sensitivity to total ozone and its vertical distribution increases for high SZAs and short UVB wavelengths due to the Umkehr effect (see the discussion of Figures 3 and 4 in [PERSON] et al. (2019)).
## 3 Results  
### Vertical GRF  
The passive impact phase in SS and 90\({}^{\circ}\) LR occurred within 100 ms after ground contact, with the Fz approximately twice that of the BW (Figure 4a). The vertical GRF increased rapidly in LUTO and LUTD during contact, that is, the Fz during the push-off phase and after landing could be as large as four and eight-times the BW, respectively (Figure 4b). However, no significant differences in Fz, Gz, tP, and tG were observed between the two basketball shoes during SS, 90\({}^{\circ}\) LR, LUTO, and LUTD (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Maneuvers** & **Shoes** & **Fz (BW)** & **t\({}_{\text{F}}\) (ms)** & **G\({}_{\text{z}}\) (BW/ms)** & **t\({}_{\text{G}}\) (ms)** \\ \hline SS & L & 1.92 \(\pm\) 0.32 & 74.4 \(\pm\) 30.2 & 0.26 \(\pm\) 0.18 & 27.2 \(\pm\) 21.8 \\  & N & 2.02 \(\pm\) 0.43 & 63.3 \(\pm\) 28.1 & 0.17 \(\pm\) 0.06 & 19.7 \(\pm\) 11.4 \\  & L & 2.47 \(\pm\) 0.61 & 55.4 \(\pm\) 25.6 & 0.35 \(\pm\) 0.08 & 13.4 \(\pm\) 12.6 \\  & N & 2.25 \(\pm\) 0.71 & 54.8 \(\pm\) 26.4 & 0.21 \(\pm\) 0.07 & 13.2 \(\pm\) 9.7 \\  & L & 3.53 \(\pm\) 0.43 & 130.7 \(\pm\) 47.6 & & \\ LUTO & N & 3.55 \(\pm\) 0.67 & 124.8 \(\pm\) 42.1 & & \\  & L & 6.33 \(\pm\) 3.10 & 26.4 \(\pm\) 9.7 & 1.13 \(\pm\) 0.87 & 10.3 \(\pm\) 4.2 \\  & N & 9.72 \(\pm\) 3.42 & 28.6 \(\pm\) 13.8 & 0.93 \(\pm\) 0.32 & 11.9 \(\pm\) 4.8 \\ \hline \hline \end{tabular} Notes: GRF is ground reaction force, Fz is peak vertical GRF, Gz is peak loading rate, IF is time to peak vertical GRF, tG is time to peak loading rate, BW is bodyweight, SS is side-step cutting, 90\({}^{\circ}\) LR is 90\({}^{\circ}\) L-direction running, LUTO is lay-up toe-off, and LUTD is lay-up touch-down.  
\end{table}
Table 1: Effect of different basketball shoes on ground reaction force during side-step cutting, 90\({}^{\circ}\) L-direction running, and lay-up toe-off and lay-up touch-down maneuvers.  
Figure 4: **(a)** Vertical ground reaction force-time curves during side-step cutting (SS) and 90\({}^{\circ}\) L-direction running (90\({}^{\circ}\) LR) maneuvers; **(b)** vertical ground reaction force-time curves during lay-up toe-off (LUTO) and lay-up touch-down (LUTD) maneuvers. Notes: GRF is ground reaction force, Fz is peak vertical GRF, and BW is bodyweight.
# Globally Scheduling Volunteer Computing
## 2 The Architecture of BOINC  
### BOINC Projects  
Each science project using BOINC operates its own server, using the BOINC server software. The server dispatches jobs and provides a web interface for project administrators and volunteers.  
The BOINC computing model assumes a highly heterogeneous resource pool [5]. Jobs are submitted not for specific executables but for abstract \"meta-apps\" that can include any number of concrete \"app versions\". Each app version has:  
* A \"platform\": Windows/Intel, Mac OS on Intel or Apple Silicon, Linux on Intel or ARM, Android/ARM, and so on.
* A set of additional hardware requirements, such as a GPU of a particular vendor and model, or a particular CPU feature such as SSE3.
* A set of additional software requirements, such as a minimum GPU driver version, or the availability of a VM hypervisor such as VirtualBox.
* A description of the processor usage, such as the number of CPUs or the usage (possibly fractional) of a GPU.  
For example, a project might have an Autodock meta-app, comprising several versions of Autodock for different platforms and GPUs. Many projects have meta-apps containing dozens of app versions. Some projects (like GPUGrid.net [6]) have only GPU applications; others (like LHC@home [7]) have only VM applications. Thus, a particular volunteer device may be able to run jobs for some projects and not others.  
When the BOINC scheduler dispatches a job to a client, it selects an app version to run the job with. It tries to use available resources, and it tries to choose the fastest version based on data from previous jobs.  
Jobs have storage and RAM requirements and are dispatched only to qualifying devices. They also have a FLOPs estimate and a deadline. The BOINC scheduler estimates the runtime on a device, and dispatches jobs only to devices that are likely to complete the job by the deadline.  
Because of these factors, it's possible that, at a particular time, a project has many queued jobs but none that can be dispatched to a particular device.  
Job submissions at a project may change over time. Some projects have an unbounded stream of jobs. Others may be sporadic: they might have large bursts of work, followed by weeks or months of inactivity.
# Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
## 3 Materials and Testing
### Asphalt Cement (AC)  
To evaluate the effect of asphalt cement consistency on the permanent deformation behavior of asphalt concrete mixtures, three types of asphalt cement were used in the experimental work: AC 40-50, AC 60-70, and AC 85-100. All were sourced from the Dora Refinery, located southwest of Baghdad. Table 1 illustrates the properties of the used asphalt cement.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & **ASTM** & \multirow{2}{*}{**Units**} & \multicolumn{3}{c}{**Asphalt Type**} & \multicolumn{3}{c}{**Specification Limit**} \\  & **Standard** & & **40/50** & **60/70** & **85/100** & & **(ASTM D 946)** \\ \hline \multicolumn{8}{c}{**Before the Thin Film Oven Test**} \\ \hline Penetration & D5 & 1/10 mm & 46 & 66 & 89 & 40-50 & 60-70 & 85-100 \\ Specific Gravity & D70 & — & 1.031 & 1.024 & 1.018 & — & — & — \\ Flash Point & D92 & \({}^{\circ}\)C & 322 & 296 & 291 & \(>\)232 & \(>\)232 & \(>\)232 \\ Softening Point & D36 & \({}^{\circ}\)C & 49 & 42 & 38 & — & — & — \\ Ductility & D113 & cm & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 & \(>\)100 \\ Rotational Viscosity & D4402 & Pa. Sec & 0.71 & 0.496 & 0.350 & — & — & — \\ Solubility in & & & & & & & & \\ Trichoethylene & & & & & & & & \\ \hline \multicolumn{8}{c}{**After Thin Film Oven Test, D1754**} \\ \hline Retained Penetration & D5 & \% & 62 & 65 & 71 & \(>\)55 & \(>\)52 & \(>\)47 \\ Ductility & D113 & cm & 60 & 80 & \(>\)100 & — & \(>\)50 & \(>\)75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Asphalt cement characterization.
## 1 Introduction  
Steady population growth and urbanization has generated a significant increase in the number of vehicles in cities, leading to traffic and congestion problems on roads and intersections [1, 2]. These difficulties are the result of increasing traffic demand compared to the insufficient capacity of existing roads [3].  
Intersections in urban centers are critical points where there is an accumulation of vehicles competing for right-of-way. Currently, traffic management at these intersections is performed in a static manner. The times assigned to traffic signals are constant and do not adjust to changing traffic conditions in real time. This results in suboptimal performance, as traffic signals may be assigning unnecessarily long or short green times, which interferes with efficient traffic flow [4, 5, 6].  
Inefficient traffic management has a significant impact on the quality of life of citizens; long waits, delays and traffic jams generate considerable time loss and increase driver stress. In addition, traffic congestion also has a negative impact on the environment due to the emission of polluting gases and additional fuel consumption [7, 8].  
To address this problem, it is necessary to look for innovative solutions that optimize traffic management at intersections. In this sense, this article proposes a mathematical model based on queuing theory for the analysis of vehicular flow in one of the most congested arteries of Panama City. These mathematical models allow performing stability analysis and estimating the optimal capacity of the queuing system to achieve a more efficient traffic management. In addition, the use of artificial intelligence (AI) offers new opportunities to improve the accuracy and predictive capacity of traffic management models. AI can analyze large volumes of real-time data and adjust traffic signal timings dynamically according to traffic conditions, which can significantly improve the performance and efficiency of traffic management at intersections [9; 10; 11].  
After the application of the method (for a large number of cases and under real traffic restriction systems), efforts are made to obtain temporal sequences for traffic lights that allow them to operate in a coordinated manner and allow the passage of a large number of vehicles through the intersection, thus reducing waiting times and queue lengths, allowing the proposed model to serve as a basis for the approach of similar problems and for the creation of better methods. The results may be of interest to researchers and practitioners in the field of transportation engineering, and the methodology and approach of the paper may serve as a valuable reference for similar studies in other cities facing similar problems.  
The rest of the paper is as follows: Section 2, Related Research; Section 3, Theoretical Context; Section 4, Methodology; Section 5, Problem Definition and Motivation; Section 6, Mathematical Model; Section 7, Results and Discussion; Section 8, Conclusions; and finally in Section 9, Future Work.
## 4 Discussion
### Effects of Extreme Climatic Conditions on NEE Prediction at Biome Level  
Extreme climatic conditions can affect the growth and development of vegetation by altering its physiological processes [54]. This is also reflected in the data-based ML method as the input variables under extreme weather can affect the model driving. However, this influence needs to be related with different extreme conditions as well as different biomes. This corresponds to the environmental conditions required for different biomes. The forest and SAV sites used in the study are mostly located at middle and high latitudes, and they are adaptable to low temperatures, so extreme cold has little effect on their prediction results. Both extreme heat and extreme dryness have effects on vegetation growth in different biomes, but the effects of extreme dryness are more pronounced. Extreme wetness has little or no effect for forest, but does not apply to CRO.  
### Effects of Extreme Climatic Conditions on NEE Prediction at Biome Level  
Both RF and XGBoost methods had good prediction accuracy for NEE, which shows that the tree-based ML methods have an advantage in CO\({}_{2}\) flux prediction. In general, the XGBoost model has better prediction results than RF in all biomes with higher R\({}^{2}\) and lower RMSE; XGBoost offers more efficient computation than RF. For XGBoost, the number of training samples and training duration show a highly linear relationship (y = 0.0004x \(-\) 0.226, R\({}^{2}\) = 0.998, \(p\) < 0.0001; x refers to the number of training samples, and y refers to the training duration which unit is minute), while RF shows a significant exponential relationship (y = 10\({}^{-6}\)x1.979, R\({}^{2}\) = 0.992, \(p\) < 0.0001) (Figure S4). That means, XGBoost can get better performance than RF with less computational cost. Moreover, the XGBoost algorithm is complex with many hyper-parameters, and the prediction accuracy of XGBoost may still be improved after an optimized hyper-parameter. However, due to the inability of the tree-based ML methods in extrapolation, the model predicted that NEE is smaller than the observational results. In conclusion, the XGBoost method shows better prediction accuracy and computational efficiency in predicting NEE.
# Towards Intelligent, Programmable, and Open Railway Networks
## 2 ETCS Levels and Architecture
The dispatcher control enables the input, query, and activation of temporary data, such as changes in ETCS modes, emergency breaks, speed restrictions, etc. The access to maintenance provides access to and storage of log files, retrieval of information for malfunctions, and RBC software configurations/updates/restart. The RBC's user interfaces make train tracking possible. Depending on the existing trackside infrastructure, the operation of the RBC may be partially or fully automated. The tendency is towards the full automation of the RBC operation based on embedded intelligent decisions. The specifications do not define any details on the implementation of RBC, just the operating modes and scenarios, and based on operational processes, the railway operator determines the RBC requirements. So, the current RBC implementations are proprietary and monolithic solutions. A possible future evolution of the RBC architecture may be related to disaggregation. Horizontal disaggregation can be achieved through the separation of time-tolerant RBC functions from RBC time-sensitive RBC functions using open interfaces. The separation of the RBC's functionality aims at running different control loops with distinct latency require ments. Interface openness can be achieved through application programming interfaces (APIs) that facilitate interoperability. The valuable benefits of APIs are related to a low cost and easy implementation, more flexible functionality, cloud-based deployment, etc. Vertical disaggregation decouples hardware and software by applying the virtualization of RBC functions. Function virtualization can be achieved by transitioning from software running on proprietary and specialized hardware to software running on general-purpose hardware. Among the advantages of function virtualization are the reduction of maintenance costs, easier function upgrades, scalability, greater agility, and flexibility. The disaggregation of software and data channels by introducing AI/machine learning (ML) techniques and connectivity to external contextual data sources can contribute to the optimization of railway operations. Currently, the AI technologies that are most often used on railways include ML, robotics, and natural language processing [21]. In the rest of the paper, the focus is on ML management in railway transport when used to perform tasks or to make predictions based on experience or example data. In this context, ML can be used for sensing and prediction. For example, sensing can be useful in image recognition for dangerous situations and image analysis for obstacle detection, fault detection, and maintenance of tunnels, bridges, buildings, etc. Prediction is useful in, e.g., preventive maintenance of the equipment, and it can improve railway safety, punctuality, reliability, and availability. The next section presents a brief description of the research related to the implementation of AI with a stress on ML in the railway sector.
# Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
## 3 Basic Concepts for Virtual Belt Algorithm
### Virtual Path and Virtual Grid  
As shown in Figure 5, there are a VP and a VG in this figure. VP is a directed continuous curve with a _start point_ (\(p_{s}\)) and an _end point_ (\(p_{e}\)). The length of VP is denoted by \(l_{p}\). VG is a grid which consists of four edges in geometry space. A VG moves circularly from \(p_{s}\) to \(p_{e}\) along a VP with a constant speed of \(v_{g}\). \(l_{g}\) and \(l_{w}\) mean the length and width of the VG, respectively. The nearest edge to \(p_{s}\) is defined as the _start edge_ of the VG. The start edge is represented by \(e_{s}\) as shown in Figure 5.  
The motion of a VG is plotted in Figure 6. At the time of \(t_{0}\), the center of \(e_{s}\) coincides with \(p_{s}\). The VG is called to be _born_ at the time of \(t_{0}\). The center of \(e_{s}\) will coincide with \(p_{e}\) at the time of \(t_{d}\). Where \(t_{d}=t_{0}+t_{c}\). \(t_{c}=\frac{l_{p}}{v_{g}}\) is defined as the _time circle_ of the VG. \(t_{g}=\frac{l_{g}}{v_{g}}\) is defined as the _grid time_ of the VG. At the time of \(t_{d}\), the center of \(e_{s}\) coincides with \(p_{e}\) and the VG disappears, which is called the _death_ of the VG. The VG will appear and move forward to \(p_{e}\) at the time of \(t_{d}\) as well. Thus, the VG moves circularly from \(p_{s}\) to \(p_{e}\) with the time circle of \(t_{c}\). Consider a set of VGs. If these VGs are of the same speed and size, and the lengths of the VPs of these VGs are equal as well, these VGs are called _uniform_. A VG can be viewed as a virtual container that appears at \(p_{s}\), and moves to \(p_{e}\) with the speed of \(v_{g}\). When a VG arrives at \(p_{e}\), the VG returns to \(p_{s}\) in no time. The VG repeats the same motion along the time.  
Figure 4: Working process of the IMU.  
Figure 5: Virtual path and virtual grid.