esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 5 ROS Robot Experiment  
### Multi-Robot Formation Experiment  
The experiment in the real scene in this study was based on the TurtleBot3 robot, which is a mobile robot platform based on the ROS (robot operating system), with low cost and easy secondary development. Each TurtleBot3 robot can independently establish a local map of its surrounding environment through the gmapping function package provided by ROS, which provides SLAM for lidar, so that the robot can establish a grid-based 2D map according to the input and attitude lidar data. Taking the laboratory map as the designated area \(Q\) of the robot formation, we set the condition that there were no obstacles in this area, as shown in Figure 16a. Four TurtleBot3 robots were randomly placed on the map. The robot completes its positioning using SLAM technology. The experimental results are shown in Figure 16b. The experiment was set so that when the Voronoi cost function reaches the optimal solution, the robot will reach the desired formation position under the  
Figure 15: Robot formation after the introduction of the insert construction method: (**a**) \(k=36\); (**b**) \(k=109\); (**c**) \(k=280\); (**d**) position error; (**e**) \(h_{i}(t)\) of each robot; (**f**) cost function.  
density function, that is, the target formation is formed. The difference from the simulation experiment is that the density function was not initialized to a constant in the initial stage of the experiment, and the target expected formation was determined first.
###### Abstract  
In this paper, we investigate the performance of a two-way hybrid satellite multi-terrestrial cooperative network with hardware impairments (HIs). Particularly, opportunistic relay selection scheme is employed in the considered network, which can substantially enhance the spectral efficiency and spatial diversity gain. By considering both the amplify-and-forward (AF) and decode-and-forward (DF) relay protocols, the closed-form expressions of the outage probability (OP) and throughput for the system are derived. Furthermore, in order to observe the effects of HIs level and the number of terrestrial relays on the system performance at high signal-to-noise ratios (SNRs), the asymptotic expressions of the system performance are also derived. Finally, computer results are presented to reveal the correctness of the analytical results.  
s 2018 18 March 2018; Accepted: 12 May 2018; Published: 15 May 2018
# Activation Analyses of Disposal Options for Irradiated Be\({}_{12}\)Ti
## 3 Modelling Methodology
### Computer Codes and Nuclear Data  
The activation simulations are performed in two steps: (1) a particle transport simulation to assess the neutron spectra in the different locations, (2) inventory calculations based on the neutron spectra to quantify the generation of the radioactive nuclides.  
Step 1 in this procedure requires neutron spectra in all geometry cells where further activation analyses will be carried out. To this end, the set of the geometry cells of interest must be specified in the MCNP input file to tally the neutron spectra. Special attention should be paid to the accuracy of the nuclear responses. This can be achieved by applying the proper variance reduction technique, if necessary. Neutron flux intensities and spectra required for the activation analysis were computed with MCNP6.2 [13], making use of the HCPB DEMO geometry model described above, the CCFE 709 neutron energy group structure [14] to address a fine resonance structure in nuclear data at low-neutron energies and the JEFF-3.3 nuclear cross-section data [15]. In the present study, only the activation of the Be\({}_{12}\)Ti blocks was analyzed. Therefore, the tallying of the neutron spectra was performed in different geometry Be\({}_{12}\)Ti cells that belong to the different poloidal segments of the blanket structure. The statistical uncertainty in all calculations of the total neutron flux does not exceed 1%. For some low-energy bins, for instance, 1\(\cdot\)10\({}^{-7}\)\(\cdot\)1\(\cdot\)10\({}^{-4}\) MeV, the relative error was calculated to be less \(\sim\)10%. Normalization of the nuclear responses was carried out to the DEMO fusion nuclear power of 1998 MW, that results in a 7.094\(\cdot\)10\({}^{20}\) s\({}^{-1}\) neutron source intensity.  
To facilitate the activation analyses, an interface code was developed and applied to numerous activation calculations. It makes use of the MCNP input file for neutron transport calculations as a main repository to obtain full information on materials used in the calculations. The interface assures a proper preparation and an execution of the big amount of the FISPACT II [14] inventory calculations according to the geometry cell numbers specified in the MCNP neutron transport calculations. The FISPACT II code involves a TENDL activation data library which is TENDL-2021 in the present version [16]. For the numerous activation calculations, the interface automatically prepares the FISPACT input files for each geometry cell using a proper fine material composition and the irradiation scenario. It then automatically submits a batch job for each geometry cell for an execution. The whole procedure is fully parallelized making use of an MPI technique. The inventory results of the FISPACT outputs are collected and processed by a versatile post-processing code.
## 4 Influence of Different Parameters on Pipeline Vibration Characteristics
### Influence of Constraint Stiffness on Natural Frequency of the Multi-Branch Pipeline  
The fixed constraint of the pipeline is changed to elastic constraint. Different restraint stiffnesses (\(10^{3}\), \(10^{4}\), \(10^{5}\), \(10^{6}\), \(10^{7}\), \(10^{8}\), \(10^{9}\), \(10^{10}\) N/m) are applied. Table 10 shows the first three natural frequencies of the pipeline at different constraint stiffness. Figure 19 shows that with the increase in restraint stiffness, the first three-order natural frequencies of the pipeline gradually increase. When the stiffness is less than \(10^{5}\) N/m, the pipeline is less constrained, resulting in a slow increase in the natural frequency. The elastic constraint gradually approaches the fixed constraint when the stiffness increases to \(10^{9}\)-\(10^{10}\) N/m. At this point, the increase in the natural frequency becomes slow and gradually approaches the value of the natural frequency at the fixed constraint. Within this constraint stiffness range, The first three-order natural frequencies change by 2803.88%, 3374.03%, and 556.29%, respectively. When elastic restraint is applied to the pipeline, appropriate restraint stiffness shall be selected on the basis of avoiding resonance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Elastic Constraint Stiffness/(N/m)** & **1 st Mode/Hz** & **2 nd Mode/Hz** & **3 rd Mode/Hz** \\ \hline \(10^{3}\) & 6.19 & 19.14 & 174.16 \\ \(10^{4}\) & 18.56 & 58.35 & 187.38 \\ \(10^{5}\) & 41.28 & 174.24 & 284.26 \\ \(10^{6}\) & 54.35 & 364.56 & 605.45 \\ \(10^{7}\) & 68.94 & 436.25 & 780.55 \\ \(10^{8}\) & 130.31 & 526.37 & 893.49 \\ \(10^{9}\) & 171.26 & 621.44 & 1058.44 \\ \(10^{10}\) & 179.75 & 664.93 & 1143.15 \\ \hline \hline \end{tabular}
\end{table}
Table 10: First three natural frequencies of the pipeline at different constraint stiffness.  
Figure 18: First three-order natural frequencies of the pipeline at different pipe diameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pipe Diameter/mm** & **1 st Mode/Hz** & **2 nd Mode/Hz** & **3 rd Mode/Hz** \\ \hline \(6\) & 94.136 & 384.12 & 759.53 \\ \(8\) & 118.53 & 509.11 & 945.35 \\ \(10\) & 156.47 & 633.38 & 1155.10 \\ \(12\) & 194.94 & 756.93 & 1367.25 \\ \(14\) & 232.97 & 872.45 & 1561.11 \\ \(16\) & 270.53 & 981.35 & 1740.41 \\ \(18\) & 304.96 & 1082.54 & 1923.21 \\ \(20\) & 340.28 & 1178.19 & 2084.58 \\ \hline \hline \end{tabular}
\end{table}
Table 9: First three-order natural frequencies of the pipeline at different pipe diameters.
# Resilient Modulus--Physical Parameters Relationship of Improved Red Clay by Dynamic Tri-Axial Test
## 3 Analysis of Results
### Prediction Models of Resilient Modulus  
The concept of a resilient modulus has been used to represent the non-linear stress-strain characteristics of subgrade soils. From the above analysis, it can be concluded that the resilient modulus of improved red clay has a strong dependence on the stress state. Several prediction models have been developed in the past to express the relationship between resilient modulus and stress state. The prediction models of non-linear resilient modulus mainly include the single factor model related to bulk stress or deviatoric stress [36], the composite model related to deviatoric stress, and mass stress [38], the composite model related to shear stress and confining pressure [40], and the composite model related to confining pressure and deviatoric stress [41]. In this paper, three prediction models frequently cited were chosen to analyze the influence of deviatoric stress and confining pressure on the resilient modulus of the improved red clay.
# Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing
## 2 Related Work
### Sp
#### 2.1.3 Placement Action  
In this step, the selected VM (service) in the previous step is placed on another VM. CDCs and DDCs need to place and migrate to minimize resource wastage, balance workloads, maximize energy efficiency, and save cost.  
In this step, the proper VM is selected to place for resource management and efficiency service operation in CDCs and DDCs. Due to its importance, the problem of VM placement in the CDCs and DDCs became the focus of practical studies, and numerous VM placement methods have proposed algorithms for decisions, which included VMs (or services) to be placed (or migrated).  
In previous work, for various user requirements, VM placement schemes have been proposed considering different metrics such as resource utilization, response time, etc. [3; 4; 9], according to the objective.  
Due to the scaling of cloud infrastructures and dynamic workload, when services are placed or migrated, considering the target machine is another challenge. Accordingly, various research studies have been proposed for decision making for selecting between VMs and physical machine (PM) [9].  
Figure 1: Placement process.  
The decision for the target machine has to consider multi-metrics such as resource utilization, energy consumption, and latency [12; 16], according to user requirements.  
For selecting a source machine and target machine, it is important to detect an over-load and under-load state [17]. When a server does not have enough resources for the operating service, it is called a hotspot, which means it is overloaded and needs to place some services to another server [17]. Hotspots contribute to performance degradation as well as reduce energy efficiency. In addition, if the server state detected is a cold spot, it also contributes to performance degradation as well as reduced energy efficiency.  
Thus, when a service is placed, it aims to find the best target machine, which leads to the improvement of performance, and energy efficiency of the CDCs and the reduced operation cost of the CDCs and DDCs. Large-scale CSPs face the challenging problem of how to place resources over areas so as to reduce the total cost while providing an adequate level of performance, by taking into account the dynamic demand.
## 3 Pseudolite-Based Positioning System for Legacy GNSS Receivers
### System Configuration  
The proposed system is composed of four main parts: the pseudolites, the data server, the reference station, and the user terminal. Figure 1 illustrates the total system configuration.  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline \begin{tabular}{c} **Ephemeris** \\ **Data** \\ \end{tabular} & \begin{tabular}{c} **Definition** \\ \end{tabular} & \begin{tabular}{c} **No. of Bits** \\ \end{tabular} & \
\begin{tabular}{c} **Scale Factor** \\ **(LSB)** \\ \end{tabular} &
\begin{tabular}{c} **Units** \\ \end{tabular} \\ \hline \(\dot{M}_{0}\) & Mean anomaly at reference time & 32 & 2\({}^{\text{\textascifract}}\) & Semi-circles \\ \(\Delta n\) & Mean motion difference from computed value & 16 & 2\({}^{\text{\textascifract}}\) & Semi-circles/sec \\ \(e\) & Eccentricity & 32 & 2\({}^{\text{\textascifract}}\) & Dimensionless \\ \(\dot{A}^{{}^{1/2}}\) & Square root of the semi-major axis & 32 & 2\({}^{\text{\textascifract}}\) & Meters\({}^{\text{\textascifract}}\) \\ \(\Omega_{0}\) & Longitude of ascending node of orbit plane at weekly epoch & 32 & 2\({}^{\text{\textascifract}}\) & Semi-circles \\ \(i_{0}\) & Inclination angle at reference time & 32 & 2\({}^{\text{\textascifract}}\) & Semi-circles \\ \(\omega\) & Argument of perigee & 32 & 2\({}^{\text{\textascifract}}\) & Semi-circles \\ \(\dot{\Omega}\) & Rate of right ascension & 24 & 2\({}^{\text{\textascifract}}\) & Semi-circles/sec \\ \(\dot{i}\) & Rate of inclination angle & 14 & 2\({}^{\text{\textascifract}}\) & Semi-circles/sec \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected ephemeris parameter of GPS L1 signal.
# QiOi: Performance Isolation for Hyperledger Fabric
## 2 Background
### Linux CPU Scheduler  
In this subsection, we first explain the scheduling mechanism of CFS and then the parameters of CPU scheduling as background for performance isolation.  
To allocate CPU resources between processes fairly, CFS first calculates the _period_ depending on the number of processes in the run queue. Then, CFS determines a _time slice_ for each process depending on its _load weight_. When a specific process is scheduled, it runs the _time slice_ of the process. The process may fully use the _time slice_ or it may not use all of the _time slice_ by preemption. As the process is preempted, CFS measures the execution time of the process. When the process is dequeued from the run queue, the virtual runtime (_runttime_) is updated based on the measured execution time. Depending on its _vruntime_, the process is sorted and stored in the red-black tree, which is a self-balancing binary search tree [28]. A process with the least _vruntime_ will be the leftmost node in the tree. When CFS schedules the next process to run, it picks the leftmost node. Details of the main parameters, _load weight_ and _vruntime_, are as follows.  
First, the _load weight_ is given to all processes, and CFS provides the CPU time (_time slice_) for processes in proportion to their _load weight_[29]. _time slice_ is calculated as in Equation (1). Note that the default _load weight_ is 1024 in Linux kernel. The _period_ is 24 ms by default if the number of processes in the run queue is less than eight. Otherwise, the _period_ is calculated by multiplying the number of processes in the run queue by 3 ms. The value of 3 ms is set to avoid preempting processes too frequently in Linux kernel. _se \(\to\) load.weight_ indicatesthe _load weight_ of a process, and _cfs_rq_\(\rightarrow\)_load.weight_ indicates the sum of _load weights_ of all processes in the run queue.  
\[time\;slice=period\times\frac{se\to load.weight}{cfs\_rq\to load.weight} \tag{1}\]  
Second, the _cruntime_ is inversely proportional to the amount of CPU time that a process has already used. The process with the lowest _cruntime_ has the highest scheduling priority. Equation (2) shows how CFS calculates _cruntime_, where _delta_exec_ is CPU time actually used. If a process has a high _load weight_, it receives small _cruntime_ compared to other processes consuming similar amounts of CPU time. This enables the process with the high _load weight_ to be scheduled frequently.  
\[\centering\;runtime=delta\_exec\times\frac{default\;load.weight}{se\to load.weight} \tag{2}\]  
In order to control the _load weight_, CFS offers an adjustable parameter called _CPU share_. Since Linux cgroups feature calls _load weight_ as _CPU share_, we use the term _CPU share_ for the rest of this paper [30].
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 2 Two Plausible Calibration Concepts: The White- or the Black-Box
### Black Box  
The 'black box' calibration is a direct comparison of the reconstructed wind characteristics with the corresponding reference quantity. Using this approach, the lidar is seen as a black box, a system where the knowledge of the transfer function between inputs and outputs is not relevant or not necessary (Figure 2).  
The wind speed calibration of ground-based lidars is an example of a black box calibration: lidar-estimated horizontal wind speeds (HWS) are compared to reference anemometers placed at multiple heights on a met. mast [13]. For profiling nacelle lidars, the measurement setup of a black box calibration would require:  
* **for horizontal wind speed and wind direction:*
* lidar placed on a platform high enough to allow the beam(s) to not be blocked by the ground in order for the reconstruction algorithm to be available. High stiffness of the tower is required to avoid significant deflections causing the lidar beams to constantly move and sense winds in locations unacceptably far from the reference instruments. This is the reason why we do not recommend calibrating lidars mounted on the nacelle of on an operating wind turbine. The beam perturbations would depend on both the turbine and the actual wind distribution during the testing period. Consequently the repeatability of the calibration would be seriously impaired and the uncertainties increased. For modern wind turbines, the rotor diameter \(D_{rot}\) is \(\sim\)100 m. Power performance standards (IEC 61400-12-1, [14]) require to measure the free wind at an upstream distance of \(2.5D_{rot}\). With a cone or half-opening angle of \(\alpha=15^{\circ}\), the height of the platform should therefore be \(>\)2.5\(D_{rot}\tan\alpha\approx 67\) m. In addition, a minimum height should be considered to account for the lidar probe volume and avoid sensing highly inhomogeneous and turbulent winds too close to the ground;
* a mast with reference instruments (e.g., cup or sonic anemometers, wind vanes) mounted at the location where the lidar-reconstructed wind characteristics are estimated. For a two-beam lidar system, such a location may be at the point directly in between the two beam positions, or formally anywhere between the two beams (see Section 6.2);
* accurate detection of the lidar beam or centreline, in order to position the reference instruments appropriately. This may be extremely difficult to achieve, particularly if no beam is physically present at the centre of the scanning pattern.
* **for vertical wind shear and veer**: reference wind speed and direction instruments located at several heights ranging between the minimum and maximum measurement heights of the lidar, e.g., from 10 m to 150 m.  
In practice, the described example setup makes the black box calibration an unrealistic approach for nacelle-based lidars. Such a setup would be extremely expensive, hard to find or develop, and lidar-specific.  
Figure 2: Black box lidar concept.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 3 The Components of HY-2A Capf  
In order to address the problems stated previously, it is necessary to propose a new interactive data service framework to complement the existing ground application system. This section discusses the high-level components of the framework. By reviewing a large number of users' requirements, the user community can be categorized into two groups: the professional staff from State Oceanic Administration (SOA) and researchers. The professional staff are able to access the archived data in an intranet environment, and they use the framework to complete their daily work, so the framework should allow users to start the pre-defined batch processing workflows and schedule these workflows according to their requirements. For researchers, they can access the data from the internet according to authorization rules, and they always want to adapt the framework to test their own algorithms. The framework should provide tools for these users to design on-demand processing workflows and generate proper products based on the pre-defined processing algorithms and their own algorithms. Hence, as shown in Figure 1, the framework can be divided into three parts: the routine customized processing subsystem, the on-demand customized processing subsystem, and the integrated algorithmic environment.
## 3 Analysis of Ground Deformation Characteristics in Water-Rich Soft Stratum
In general, the ground loss ratio \(V_{L1}\) = 0.54-1.20% for the first tunnel construction, with an average of 0.78%; the additional ground loss ratio \(V_{L2}\) = 0.38-1.67% for the second tunnel construction, with an average of 0.97%. It can be concluded that the additional ground loss ratio of the second tunnel construction is greater than that of the first tunnel construction. This is because of the stratum disturbance induced by the first tunnel building. On the other hand, the secondary disturbance caused by the second tunnel construction makes it more difficult to control the ground deformation. (4)  Analysis of surface settlement pattern  
The midpoint of the double-line tunnel axes with the first tunnel for the negative direction, and the second tunnel for the positive direction was taken as the coordinate origin. The surface monitoring data and the corresponding fitting curves are shown in Figure 4, and the settlement duration curves are shown in Figure 5. The first tunnel's surface settlement was in the \"single-groove\" mode, with the maximum settlement right above the metro line's center and symmetrically distributed settlement troughs. In the shield portion, the clear distance of a double-line tunnel at each monitoring section was less than twice the tunnel diameter. The surface settlement of a double-line tunnel was in the \"single-groove\" mode, with the highest settlement on the side of the metro line with the most ground loss and asymmetrically distributed settlement troughs. Compared with the surface settlement of the first tunnel, the second tunnel changed the surface settlement trough model and increased the maximum settlement and the settlement trough width. The analysis of measured data showed that the maximum surface settlement \(S_{\text{max}}=-14.20\)\(-\)\(-\)\(-\)\(28.00\) mm after the double-line tunnel excavation, with an average of \(-20.73\) mm. The influence of shield tunneling on surface settling in Tianjin's water-rich soft stratum could be inferred to be acceptable. The surface settlement of the water-rich soft stratum could be controlled within 20-30 mm by setting reasonable shield tunneling parameters. Figure 4: Settlement fitting curves of monitoring sections. (**a**) Monitoring section HY-54. (**b**) Monitoring section HY-67. (**c**) Monitoring section HY-120. (**d**) Monitoring section HY-200. Figure 5: Settlement duration curves of monitoring sections. (**a**) Monitoring section HY-54. (**b**) Monitoring section HY-67.