esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results
### Spatial Distribution and Land Subsidence Rate
#### 3.1.5 Cirebon  
Cirebon is one of the cities in West Java Province, Indonesia, which is geologically located within an alluvial plain area. Geographically, Cirebon Regency is located at 6\({}^{\circ}\)30\({}^{\prime}\)58\({}^{\prime\prime}\)-7\({}^{\circ}\)00\({}^{\prime}\)24\({}^{\prime\prime}\) south latitude and 108\({}^{\circ}\)19\({}^{\prime}\)30\({}^{\prime\prime}\)-108\({}^{\circ}\)50\({}^{\prime}\)03\({}^{\prime\prime}\) east longitude. As of 2020, the population of Cirebon City is approximately 350,000, with a density of 8265 people per km\({}^{2}\).  
Figure 5: LS in Cianjur: (**a**) LS rate in mm/year in the Cianjur area; (**b.1**) LS time series retrieved from InSAR from 2017 to 2023; and (**b.2**) land use over the highlighted black box area (**b**).  
The analysis in this area includes Indramayu (Figure 7(a.1)), Cirebon City (Figure 7(a.2)), and Cirebon Regency (Figure 7(a.3)). Essentially, Indramayu was not included in the analysis of LS in Cirebon; however, the condition of this region has become a concern not only because its LS rate is as significant as in Cirebon, which is 60 mm/year (Figure 7(b.1)), but also several residential areas in the northern region of the city (Figure 7(c.1)) experienced flooding, and the cause is yet to be discovered.  
The rate of LS in Cirebon Regency has a rate of about 60 mm/year (Figure 7(b.2)), which is particularly noticeable in densely populated coastal areas (Figure 7(c.2, c.3). Therefore, it can be anticipated that the probable causes of LS in the area are a combination of natural factors and human activities. The observations of LS in Cirebon align with the findings by [10; 41; 42].  
An interesting event has been observed in Cirebon City, where the area affected by LS is centered around a hotel and a shopping center, with an LS rate of about 40 mm/year (Figure 7(b.3)). Meanwhile, in Cirebon Regency, it is suspected that LS is primarily caused by natural compaction processes in areas with thick sedimentary layers. Additionally, the presence of a steam power plant in the area may also contribute to the increased rate of LS in this area.
## 3 Methodology
### Point Features Extraction
#### 3.1.6 Number of Returns (\(Nor\))  
The \(NOR\) is defined as the recorded number of returns for each laser pulse. Table 2 shows the number of possible returns that can be recorded using the infrared and green wavelengths. The water point should have a single return when using the infrared wavelengths in C1 or C2 due to the recorded returns from the water surface only, and the water points should have double returns when using the green wavelength in C3 due to the recorded returns from water surfaces and benthic layers.  
The point feature was extracted considering single or double channels. For instance, the evaluation of \(ICOV\) and \(ID\) features were conducted using the data of a single channel (C1, C2, or C3), thus, different results were obtained from the three channels for each point feature. The \(HV\), \(HSD\), \(PD\), and \(NOR\) features were conducted using the combined data from two channels (i.e., C3 with C1 or C3 with C2) and, hence, two different results were obtained.
## 4 Results and Discussion  
### Gradual Microscopic Contact Behaviour of the Main Skeleton  
#### 4.1.1 Mesoscopic Contact Characteristics of the Main Skeleton  
Figure 5 shows the mesoscopic contact characteristics of the main skeletons of 70# asphalt AC-13C, rock asphalt AC-13C, and SMA-13 before loading, as well as the spatial distribution of coarse aggregates and contact points. The gradual change in the mesoscopic contact characteristics of SMA-13 main skeleton at different loading stages are shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multicolumn{4}{c}{**Quantity of contact points (total quantity)**} \\ \cline{3-5}  & & **Before loading** & **After 1-h Loading** & **After 2-h Loading** \\ \hline \multirow{2}{*}{70\# asphalt AC-13C} & Cut specimen & 207 & 233 & 257 \\  & Uncut specimen & 234 & 209 & 274 \\ \hline \multirow{2}{*}{SMA-13} & Cut specimen & 162 & 192 & 207 \\  & Uncut specimen & 155 & 203 & 219 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effect of specimen cutting on quantity of contact points.  
Figure 5: Mesoscopic contact characteristics of main skeleton before loading. (**a**) 70# asphalt AC-13C; (**b**) rock asphalt AC-13C; and (**c**) SMA-13.  
The following can be observed in Figures 5 and 6.  
(1) Gradation is the main determinant of the spatial distribution of aggregates and contact points. As shown in Figure 5, in AC-13C and SMA-13 mixtures, not only are the particle size and distribution of aggregates different, but also the quantity of contact points, the uniformity of aggregates, and contact points considerably differ.  
(2) The inhomogeneous distribution of aggregates and contact points is a typical feature of an asphalt mixture because it is composed of granular materials. As a typical DSAM, the distribution of coarse aggregates and contact points in SMA-13 are inhomogeneous. For the three types of asphalt mixtures, considerable quantities of'suspended' coarse aggregates do not come into contact with other aggregates and are not involved in the formation of the main skeleton. Although AC-13C has more contact points, their distribution is more inhomogeneous than that in SMA-13; moreover, there are more'suspended' coarse aggregates in the former.  
(3) Coarse aggregates move under the action of load; accordingly, the quantity of contact points and contact position will change. Hence, the quantity of contact points will increase, and the quantity of'suspended' coarse aggregates will decrease.
## 4 Numerical Simulation on Simplified Model of Preliminary Design HE and Optimized Design HE
### Grid Independence and Validation  
Grid independence verification was carried out to eliminate the influence of the grid number on the simulation results. The average temperature on the outer surface of SCO  
Figure 8: The grid of the simplified model of HE.  
was taken as the monitoring object because it can reflect the heat transfer in HE. Taking sample 1 as an example, the grid models with 796,534, 2,544,917, 3,967,254, 5,386,445 and 7,508,074 cells were established for grid independence verification, and the results are shown in Figure 9.  
It can be seen from Figure 9 that the average temperature on the outlet surfaces of SCO\({}_{2}\) changes less when the number of cells reaches 3,967,254; accordingly, the mesh that consists of 3,967,254 cells is chosen for the following study. The same procedure is carried out for sample 2, and the mesh number is approximately 4,788,641.  
In addition to the grid independence, a suitable range of the y+ on the tube wall is also an important part to ensure the accuracy of the numerical simulation results. In this study, the flow patterns of liquid lead and SCO\({}_{2}\) are both turbulent flows, which the y+ within the range of 30-300 is suitable for such a zone. In the previous study, [PERSON] [46] verifies the turbulence model of SCO\({}_{2}\) in simulation and the results show that the error of calculation is less than 0.1 if y+ is in the range of 10-76. Therefore, y+ is controlled less than 76 in this study. The maximum value of y+ in the results is approximately 75.  
In order to verify the accuracy of the design calculation, taking sample 2 as an example, the results of the design calculation are compared with the results of numerical simulation. Table 10 shows the pressure drop and heat transfer power of sample 2 in the design calculation and numerical simulation.  
It can be seen from Table 10 that the pressure drops on the tube-side and heat transfer rate of sample 2 are in good agreement with the design calculation and numerical simulation. The error of the pressure drop on the shell-side is relatively larger, but it is still within an acceptable range.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Results** & **Pressure Drop of Tube-Side/Pa** & **Pressure Drop of Shell-Side/Pa** & **Heat Transfer Rate/kW** \\ \hline Design calculation & 1067.8 & 1283.1 & 9.35 \\ Numerical simulation & 1100.0 & 1117.3 & 9.01 \\ Relative error/\% & 3.0 & 12.9 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The comparison between the design calculation and numerical simulation.  
Figure 9: Grid independence verification.
# Representing and Inferring Massive Network Traffic Condition: A Case Study in Nashville, Tennessee
## 3 Results  
This section provides a detailed description of the results of all experiments. The first part of the study is representing the massive network using the CUR decomposition method based on the training data and inferring future conditions on the testing data. In addition, the second part is a new application scenario of indexing the network condition using the outputs from the CUR decomposition.
## 3 Results and Discussion
### Microalgal Growth
Another explanation for the low biomass concentrations and productivities achieved may be related to the phenomenon of flakes formation observed within the cultivation period (autofloculation). Cells' agglomeration can affect the accurate measurement of OD\({}_{680}\) and, on the other hand, it can reduce light absorption efficiency by cells incorporated within flakes, thus resulting in lower photosynthetic activity. In this study, this phenomenon occurred due to the increase of culture pH (from 7.8 to 8.6) or due to the presence of certain compounds in the effluent, which can induce a change in the surface charge of the cells and affect suspensions' stability [22]. Despite the low microalgal growth rates, the flakes formation enables a cost-effective biomass removal after effluent remediation. The density similar to water and small size of microalgal cells difficult the harvesting process and make this step one of the most expensive within microalgal biomass production processes [22; 23]. However, when cells agglomerate, an increase in \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Assay**} & **(NO\({}_{\text{3}}\)+NO\({}_{\text{2}}\))-N** & **PO\({}_{\text{4}}\)-P** & \(\mu\) & \(X_{\text{max}}\) & \(P_{\text{max}}\) & \(P_{\text{aver}}\) \\  & **(mg\({}_{\text{8}}\) L\({}^{-1}\))** & **(mg\({}_{\text{8}}\) L\({}^{-1}\))** & **(d\({}^{-1}\))** & **(mgdw L\({}^{-1}\))** & **(mgdw L\({}^{-1}\) d\({}^{-1}\))** & **(mgdw L\({}^{-1}\) d\({}^{-1}\))** \\ \hline
1 & 34.2 & 12.7 & 0.093 \(\pm\) 0.007 a ** & \(136\pm 1\) a  & \(9.8\pm 0.2\) a  & \(6.22\pm 0.09\) a  \\
2 & 28.3 & 8.55 & 0.11 \(\pm\) 0.01 ab  & \(191\pm 10\) ab  & \(15\pm 2\) ab  & \(11\pm 1\) ab  \\
3 & 21.5 & 6.04 & 0.136 \(\pm\) 0.004 bc  & \(229\pm 16\) b  & \(24\pm 7\) ab  & \(15\pm 1\) b  \\
4 & 16.7 & 4.22 & 0.134 \(\pm\) 0.002 bc  & \(249\pm 14\) b  & \(30\pm 3\) b  & \(16\pm 1\) b  \\
5 & 12.7 & 4.01 & 0.16 \(\pm\) 0.02 c  & \(231\pm 31\) b  & \(30\pm 6\) b  & \(15\pm 3\) b  \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specific growth rates (\(\mu\), in d\({}^{-1}\)), maximum biomass concentrations (\(X_{\text{max}}\), in mgdw L\({}^{-1}\)), and maximum and average biomass productivities (\(P_{\text{max}}\) and \(P_{\text{aver}}\), in mgdw L\({}^{-1}\) d\({}^{-1}\)) determined for _C. vulgaris_ grown in raw and diluted secondary-treated paper industry effluent their density and size is observed, contributing to higher settling rates and allowing biomass recovery using the least expensive harvesting method: sedimentation.
# Towards Hierarchical Workflows in SysML to Support Virtual Validation of Technical Systems
## 4 Method
### The Framework of Virtual Validation Workflows Based on the Functional Architecture
#### 4.1.2 Structure of Virtual Testing Workflows Based on Domain Model Classification  
As mentioned in Section 4.1.1, \"Action 1: Virtual Testing Workflows\" aims to check if the function is fulfilled by the corresponding solution. This section looks into the structure of \"Action 1\", describing in detail how to arrange the virtual testing workflows to achieve this aim.  
As demonstrated in [21], a technical system requires different virtual tests. Therefore, multiple virtual testing workflows need to be created for a complex technical system with different test purposes. Executing these virtual testing workflows comprehensively can detect failure risks (such as bearing fatigue analysis) and analyze the performance (such as WT economic analysis) of system solutions. The testing results provide evidence that the system solution fulfills the corresponding function.  
\"Action 1\" invokes the standardized activity that contains the actions which are divided into the following two parts in Figure 4:  
Part 1 consists of multiple actions that invoke the virtual testing workflows with different test purposes to virtually test the solution. These actions are designed independently of each other and invoke the corresponding virtual test workflows in parallel. As an example, to check whether the solution \"Bearing\" fulfills the corresponding function \"Support rotation with low friction\", fatigue testing and smearing testing on the solution are required. The testing results of each virtual test should be verified against design requirements and the verification values can be exported through the output pin around the action. The specific method for developing a virtual testing workflow will be introduced in detail in Section 4.2.  
Part 2 includes the actions called \"Check all verification results\" and \"Save requirement satisfaction results\". The action \"Check all verification results\" checks the results of each virtual test. The inputs of this action connect the outputs of the \"Virtual testing workflow\"  
Figure 4: The workflow to check if the solution fulfills the function based on virtual tests.  
actions through object flows. Only when each virtual testing result is \"True\", the solution can be considered feasible, that is, it fulfills the corresponding function. Otherwise, the solution under the current parameter setting is considered not to fulfill the function. The action \"Save satisfaction results\" saves the result of whether the solution fulfills the function of the system model.
## 3 Results
### Outcome Measures  
Mean FPA measurements were highest in the toe-out condition and most negative during toe-in gait. On average IMUs responded with a magnitude of around 15 degrees to both conditions, while the PSW measured nearly 3 degrees less (Table 2). In total, 7974 steps were detected. While, 63 steps were removed from the dataset, as they fell outside the acceptable range of values (between \(-40^{\circ}\) and \(40^{\circ}\)); i.e., almost physically impossible in healthy people; 86% of these removed steps were measured by the PSW.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Instrument** & **Variable** & **Baseline** & **Toe-Out Gait** & **Toe-In Gait** \\ \hline \multirow{3}{*}{IMUs} & Mean FPA \(\pm\) SD & 5.6 \(\pm\) 4.9 & 19.9 \(\pm\) 6.3 & \(-9.9\) \(\pm\) 6.8 \\  & (95\% CI) & (4.0: 7.1) & (17.9: 21.9) & (\(-12.1\): \(-7.7\)) \\  & Min; Max & \(-2.0\): 19.5 & 8.6; 31.5 & \(-29.1\): 5.5 \\ \hline \multirow{3}{*}{PSW} & Mean FPA \(\pm\) SD & 5.2 \(\pm\) 5.5 & 17.3 \(\pm\) 6.0 & \(-7.2\) \(\pm\) 5.4 \\  & (95\% CI) & (3.5: 7.0) & (15.4: 19.2) & (\(-9.0\): \(-5.5\)) \\  & Min; Max & \(-4.5\): 21.4 & 9.1; 33.5 & \(-18.4\): 6.4 \\ \hline \multirow{3}{*}{IMU-PSW} & Mean AFPA between & & & \\  & systems \(\pm\) SD & 0.3 \(\pm\) 3.6 & 2.6 \(\pm\) 3.7 & \(-2.7\) \(\pm\) 4.1 \\  & (95\% CI) & (\(-0.82\): 1.5) & (1.4: 3.8) & (\(-4.0\): \(-1.3\)) \\  & Min; max & \(-11.3\): 7.3 & \(-4.4\): 11.7 & \(-13.0\): 5.2 \\  & \(p\)-value & 0.57 & \(p\) \(<\) 0.001 & \(p\) \(<\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean foot progression angle (FPA), standard deviation (SD), 95% confidence intervals (CI), minimum and maximum values and mean difference in FPA between the two systems in three conditions: baseline gait, toe-out gait, and toe-in gait. All values are in degrees for IMUs (Opal sensors) and the PSW (ZenoTM walkway). The calculations are based on 7974 steps by 20 participants.
## 3 Application to Field Data
### Background Noise Data Acquisition  
The point of noise data acquisition is located in the karst development area of Jinshui Street in Wuhan, China, with roads and villages evenly distributed around the acquisition point, and rich noise sources (Figure 3). The instrument used in this research was the three-component McGEIS-AT seismic station produced by Japan's OYO company, with a main geophone frequency of 2 Hz. There are no specific requirements of the frequency-Bessel calculation for the observation system, which can be either randomly distributed or linearly arranged. A total of 7 three-component seismic stations were arranged on the site to observe in a linear arrangement (Figure 4), with a station spacing of 5 m, sampling rate of 4 ms, and acquisition time of 30 min. After the completion of each observation duration, the overall arrangement was moved forward by 1 station spacing and the collection continued for 30 min.  
Figure 2: Dispersion spectrograms calculated by different methods. (**a**) Dispersion spectrum calculated by the spatial autocorrelation (SPAC) method. (**b**) Dispersion spectrum calculated by the frequency-Bessel method.  
A total of 45 sets of background noise data were collected this time. The observation system was designed to facilitate the comparison of the actual dispersion curves calculated by the spatial autocorrelation method and the frequency-Bessel transform.  
Figure 4: Schematic layout of field data acquisition. (**a**) Diagram of station observation system. (**b**) Wave diagram of data collected by 7 seismic stations.  
Figure 3: The geological map and location of survey lines in the study area.
# Laser Synthesized Graphene and Its Applications
## 3 Applications of Laser Synthesized Graphene
### Electrocatalysis  
One interesting application for LSG that has been proposed is where this is employed as catalyst for electrochemical reactions, i.e., used as an electrocataly. In particular, oxygen evolution reactions (OER) are of special interest because of the technological implications regarding water splitting or fuel cells [83,84], to name a few. Typically, the most efficient materials are noble metals and metal oxides [85]. It has already been mentioned that cedar wood impregnated by metal salts solution is an effective source for LIG-metal nanocomposites, as the laser not only converts cedar wood into graphene but also reduces metal ions into nanocrystals. Such material shows electrocatalytic activity towards OER [45]. The interest in this process is in using, instead of noble metals, more common ones such as Ni and Fe, as well as an overpotential just below 300 mV (at 10 mAcm\({}^{-2}\) current density) [45]. A significant step further was achieved by [PERSON] et al., who proposed a metal-free electrocataly using an oxidized LIG, in which the oxidization, with further introduction of oxygen-containing functional groups, was achieved by oxygen plasma treatment [86]. Such functional groups, introduced by the plasma treatment, provide active sites as well as facilitate the adsorption of reaction intermediates, thus lowering the activation energy for the OER. The authors report that LIG itself is already active for OER, but introducing the oxygen-containing groups produces a significantly lower onset potential of 260 mV [86]. H\({}_{2}\)O\({}_{2}\) has also generated technological interest as part of rocket propellants and fuel cells, besides a more traditional use as a strong oxidant and disinfectant. [PERSON] et al. propose the use of sulfonated LIG for the electrocatalytic production of H\({}_{2}\)O\({}_{2}\) in Na\({}_{2}\)SO\({}_{4}\) and NaCl as electrolytes [42].
# Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks
## 2 Related Work
[PERSON] et al. [19] utilized multicast addresses and installed the rules in advance based on the conditions of mobile users to decrease the number of rules for the Internet of Vehicle (IoV) scenarios. Although these papers [11; 12; 18; 19] optimized the number of flow rules in the devices, they did not consider the controller's response delay. This means that the flow rules were proactively installed even when the controller could guarantee the requirements of flow rule installation. This can result in inefficiency of the resource utilization. [PERSON] et al. [20] also predicted the future location and AP load using a recurrent neural network (RNN) and then migrated the virtual AP to achieve seamless handover. [PERSON] et al. [21] introduced a flow rule deployment policy according to the flow table occupancy rate, mobility of vehicles, and control channel capacity. In addition, an optimal selection for the flow rule path in the fog computing of the offloading scenario was considered to reduce the average delay and energy consumption based on the flow rule capacity constraint [22]. Moreover, [PERSON] et al. [23] invoked global topology discovery processes to detect the mobility and updated flow rules proactively based on the centralized global network view. [PERSON] et al. [24] configured an integer linear program to handle the flow table resource allocation problem when the traffic was generated in a bursty on-off manner. However, these papers [20; 21; 22; 23; 24] utilized the proactive rule placement method only. Consequently, if a prediction error occurred which was a natural problem of the prediction algorithms [18], high resource usage (i.e., repetitively cached at multiple forwarding devices) could be experienced, which could block the additional flow rule installations. To handle the above problems, this paper considers the response delay of the controller, which can be a key performance metric to determine whether a proactive flow rule cache is required or not. In addition, since the prediction error of the proactive flow rule cache method can lead to a waste of resources, the reactive flow rule cache method can be efficient if the controller's response delay meets the delay requirements of the flows. Therefore, this paper proposes a hybrid flow rule cache scheme which proactively as well as reactively caches the flow rules based on the response delay of the controller and the delay requirements of the flows. In the proposed scheme, the reactive flow rule cache method is preferred to prevent the waste of flow rule caches when the delay requirement of flows can be guaranteed by the response delay of the controller. On the other hand, the proactive flow rule cache method is utilized to remove the flow rule installation procedure, which can be a time-consuming task when the controller is overloaded.