esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results Analysis
### Bearing Contact under Combined External Loads  
#### 5.1.1 Contact Load Distribution before and after Optimization  
Figure 6a,b shows the bearing contact force distribution before and after optimization under the combined action of Table 2, where \(Q_{rigid}\) is the contact force before optimization and \(Q_{opt}\) is the optimized contact force. It can be seen from the figure that before optimization, the contact force \(Q_{1}\) is greater than 0 in the range of azimuth 96.2-327.9\({}^{\circ}\). The contact force \(Q_{2}\) is greater than 0 in the ranges of 0-110.5\({}^{\circ}\) and 317.2-356.4\({}^{\circ}\). The contact force \(Q_{1}\) and \(Q_{2}\) reach the maximum when the azimuth is 213.9\({}^{\circ}\) and 32.1\({}^{\circ}\) respectively (corresponding to 10,464 N and 10,492 N, respectively).  
After optimization, the maximum values of contact force \(Q_{1}\) and \(Q_{2}\) decreased compared with those before optimization, resulting in \(Q_{1}\) of 5410 N (decreased by 5054 N) and \(Q_{2}\) of 4922 N (decreased by 5569 N). The azimuth angle corresponding to the maximum contact force did not change, remaining 213.9\({}^{\circ}\) and 32.1\({}^{\circ}\), respectively. When the azimuth is 71.3-356.4\({}^{\circ}\), the contact force \(Q_{1}\) is greater than 0. When the azimuth angle is in the range of 0-131.9\({}^{\circ}\) and 303-356.4\({}^{\circ}\), the contact force \(Q_{2}\) is greater than 0. It can be deduced that compared with before optimization, the contact force inside the bearing is significantly reduced after optimization, and the bearing area of the rolling element is increased, which makes the rolling element load more uniform and effectively improves the bearing capacity.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Ball diameter \(D_{w}\) (mm) & 12.7 \\ Bearing pitch diameter \(D_{pw}\) (mm) & 545 \\ Ball number \(Z\) & 101 \\ Nominal contact angle \(a_{0}\) (\({}^{\circ}\)) & 30 \\ Inner raceway curvature coefficient \(f_{i}\) & 0.54 \\ Outer raceway curvature coefficient \(f_{o}\) & 0.525 \\ Clearance \(g_{0}\) (mm) & 0 \\ \hline \end{tabular}
\end{table}
Table 1: Structural parameters of single-row four-point contact slewing ball bearing.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(F_{x}(N)\) & 3000 \\ \(F_{y}(N)\) & 4000 \\ \(F_{z}(N)\) & 48,000 \\ \(M_{x}(N\cdot mm)\) & \(-\)25,000,000 \\ \(M_{y}(N\cdot mm)\) & 35,000,000 \\ \hline \end{tabular}
\end{table}
Table 2: Maximum external loads for single-row four-point contact ball bearing.
###### Abstract  
By enabling a satellite network with edge computing capabilities, satellite edge computing(SEC) provides users with a full range of computing service. In this paper, we construct a multi-objective optimization model for task offloading with data-dependent constraints in an SEC network and aim to achieve optimal tradeoffs among energy consumption, cost, and makespan. However, dependency constraints between tasks may lead to unexpected computational delays and even task failures in an SEC network. To solve this, we proposed a Petri-net-based constraint amending method with polynomial complexity and generated offloading results satisfying our constraints. For the multiple optimization objectives, a strengthened dominance relation sort was established to balance the convergence and diversity of nondominated solutions. Based on these, we designed a multi-objective wolf pack search (MOWPS) algorithm. A series of adaptive mechanisms was employed for avoiding additional computational overhead, and a Lamarckian-learning-based multi-neighborhood search prevents MOWPS from becoming trapped in the local optimum. Extensive computational experiments demonstrate the outperformance of MOWPS for solving task offloading with data-dependent constraints in an SEC network.  
satellite edge computing; mobile edge computing; task offloading; data-dependent constraint; multi-objective optimization +
Footnote †: journal:  
Article
## 3 Results
### Emission Rate of Organic Molecular Markers
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **LPG** & **Gasoline** & **Gasoline** & **Diesel** & **Diesel (DPFe)** \\  & & & & **EURO4** & **EURO6** & **EURO6** \\ \hline
**Compounds** & **L2 + L1** & **G1** & **G2** & **D4-1 + D4-2** & **D6-1 + D6-2** & **D6-1 + D6-2** \\ \hline Phenanthrene & 0.52 & 234.99 & 8.19 & 14,826.95 & 0.27 & 41.42 \\ Anthracene & 0.21 & 63.60 & 2.01 & 3343.98 & - & 0.45 \\ Fluoranthene & 1.64 & 420.13 & 24.07 & 15,904.90 & - & 2.18 \\ Acephenanthrylene & 0.26 & 63.56 & 2.72 & 3341.99 & - & - \\ Pyrene & 4.68 & 576.89 & 20.02 & 18,199.49 & 0.16 & 2.85 \\ Benzo(ghi)fluoranthene & 2.45 & 30.21 & 5.18 & 1,588.51 & - & 0.44 \\ Cyclopenta(cd)pyrene & - & 2.83 & - & 148.72 & - & - \\ Benz(a)anthracene & 3.09 & 14.24 & 2.00 & 748.48 & - & 0.36 \\ Chrysene & 2.34 & 17.14 & 2.75 & 901.46 & - & 0.35 \\ Retene & 0.28 & 1.20 & 0.35 & 63.11 & - & - \\ Benzo(b)fluoranthene & 0.70 & 8.49 & 1.57 & 714.58 & - & - \\ Benzo(k)fluoranthene & 0.17 & 5.57 & 0.87 & 492.06 & - & - \\ Benzo(j)fluoranthene & 0.00 & 0.82 & 0.07 & 77.93 & - & - \\ Benzo(e)pyrene & 0.39 & 7.41 & 1.99 & 623.55 & - & - \\ Benzo(a)pyrene & 0.27 & 4.32 & 1.23 & 408.46 & - & - \\ Perylene & - & 0.88 & - & 46.29 & - & - \\ Indeno(123 cd)pyrene & - & 8.11 & 3.00 & 426.25 & - & - \\ Benzo(ghi)pyerjee & 0.28 & 13.21 & 8.30 & 694.45 & - & 1.25 \\ Coronene & 0.00 & 8.49 & 5.77 & 446.52 & - & - \\ Dibenzo(ae)pyrene & 0.00 & 0.00 & 0.34 & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 8: Emission Rate of Organic Molecular Markers (PAHs) (ng/km). The EURO4 vehicles exhibit the highest relative emissions of OC and EC. Notably, specific organic marker substances such as PAHs (Phenanthrene, Fluoranthene, Pyrene), 17A(H)-22,29,30-Trisonrophane, 17A(H)-21B(H)-30-Norhopane, and A(H)-21B(H)-Hopane were identified. Furthermore, the highest relative distribution of Alkanoic acid is observed in the G1 vehicles. These findings indicate that the emission contributions to the atmosphere from different fuel types can be estimated based on the distinctive distribution patterns of organic marker substances, Alkane, and Benzo carboxylic acid, particularly for EURO4 vehicles.
# An Insight into the Chemistry of Cement--A Review
## 5 Pore Structure in OPC  
### Type of Porosities  
In hardened cementitious materials, there are four types of pores, namely: gel pores, capillary pores, hallow-shell pores, and entrapped/entrained air voids [85].  
Gel pores (size of approx. a few nm) form within the hydrated phases at the interface between the cement grains with the liquid. Due to their small size and the great affinity between gel surfaces with water molecules, the movement of the liquid water within gel pores does not contribute to the cement paste permeability.  
Capillary pores (sized between a few nm to a few microns) form from the spaces in the fresh paste initially filled with water. During hardening, such spaces evolve into either interconnected channels or gel-pore interconnected cavities. Capillary pores are highly  
Figure 6: Image showing the mechanism of flocculation: (**a**) unhydrated section of polymineralic grain (scale of interstitial phase is slightly exaggerated), (**b**) after 10 min, (**c**) after 10 h, (**d**) after 18 h, (**e**) after 1–3 days, (**f**) after 14 days [83].  
irregular, and their presence is due to the volume of water used during the hydration process. Since a high \(w/c\) ratio causes high porosity in the hardened paste, capillary pores are typically formed in formulation with \(w/c\) ratios larger than 0.42 [86].  
Hollow-shell pores (size of several microns) are closed, ink-bottle pores that form within the void spaces at the borders of cement grains as they move back during hydration, whose shape is a relict of the cement particles.  
Entrapped air voids form during mixing due to the high viscosity of the paste. These voids are irregular and typically isolated from each other. Entrained air voids, instead, are intentionally formed during the cement mixing. These voids are uniformly distributed, spherical in shape, and not interconnected with each other [86].
# A Novel GPU-Based Acceleration Algorithm for a Longwave Radiative Transfer Model
## 5 Algorithm Implementation
### trrnmc_d
(2015)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Efficient parallel GPU design on WRF five-layer thermal diffusion scheme. _IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens._**2015**, \(8\), 2249-2259. [CrossRef]
* _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Parallel GPU architecture framework for the WRF Single Moment 6-class microphysics scheme. _Comput. Geosci._**2015**, _83_, 17-26. [CrossRef]
* NVIDIA (2019) NVIDIA, CUDA Fortran Programming Guide and Reference. Technical Document (2019). Available online: [[https://www.pgroup.com/resources/docs/19.1/pdf/pgi19](https://www.pgroup.com/resources/docs/19.1/pdf/pgi19) cudaforug.pdf]([https://www.pgroup.com/resources/docs/19.1/pdf/pgi19](https://www.pgroup.com/resources/docs/19.1/pdf/pgi19) cudaforug.pdf) (accessed on 1 October 2019). * _[PERSON] et al. (2010)_ [PERSON]; [PERSON]; [PERSON] GPU acceleration of the long-wave rapid radiative transfer model in WRF using CUDA Fortran. In Proceedings of the Many-Core and Reconfigurable Supercomputing Conference, Rotay, Italy, 22-24 March 2010. Available online: [[https://pdfs.semanticscholar.org/6844/d70506d1f79](https://pdfs.semanticscholar.org/6844/d70506d1f79) ce7a70 fa50 sa4625 febd2 dec2.pdf]([https://pdfs.semanticscholar.org/6844/d70506d1f79](https://pdfs.semanticscholar.org/6844/d70506d1f79) ce7a70 fa50 sa4625 febd2 dec2.pdf) (accessed on 1 October 2019). * [PERSON] et al. (2014) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] GPU-accelerated longwave radiation scheme of the Rapid Radiative Transfer Model for General Circulation Models (RRTMG). _IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens._**2014**, \(7\), 3660-3667. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] GPU compute unified device architecture (CUDA)-based parallelization of the RRTMG shortwave rapid radiative transfer model. _IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens._**2016**, \(9\), 921-931. [CrossRef]
* [PERSON] et al. (2011) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] GPU acceleration for GRAPES meteorological model. In Proceedings of the IEEE International Conference on High Performance Computing and Communications, Banff, AB, Canada, 2-4 September 2011; pp. 365-372. * [PERSON] et al. (2016) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Contributions of the ARM program to radiative transfer modeling for climate and weather applications. _Ams Meteorol. Monogr._**2016**, _57_, 15.1-15.19. [CrossRef]
* [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia. _IEEE Trans. Inf. Technol. Biomed._**2010**, _14_, 1417-1427. [CrossRef]
* [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON]; [PERSON] CPU/GPU computing for long-wave radiation physics on large GPU clusters. _Comput. Geosci._**2012**, _41_, 47-55. [CrossRef]
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 2 Related Work
### Reliable and Topology Control Protocols in UwSNs
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Protocol Name** & **Weakness** & **Proposed TBRL** \\ \hline \multirow{4}{*}{GEDAR [22]} & The selection of neighboring sets has some trade-offs due to its greedy approach mechanism & It does not support greedy approach, it accepts nodes by calculating reliability in term of expected transmission count, residual energy and distance in between them \\ \hline \multirow{2}{*}{DCR [23]} & For every time the depth is calculated for flooding, so large end-to-end delays occurred & At initial stage, TBRL determines the depth of each node and create possible paths to avoid large end to end delay \\ \hline \multirow{4}{*}{SWARM-SEA [24]} & The scheme runs in hybrid mode (DCR+GEDAR), and complexities are increased to detect in underwater nodes movements & It proposed algorithm for detecting the nodes movement to reduce the complexity in term of computational cost \\ \hline \multirow{2}{*}{VARP [25]} & Longest propagation delay occurs, at every hop count & It proposed average time calculation process in its mathematical model to reduce propagation delay \\ \hline \multirow{4}{*}{L2-ABF [26]} & Before the flooding, each time angle zone is calculated whereas nodes depth not considered for selection of nodes, so the unexpected delays and energy consumption occur & All reliable links are established and average time calculation is taken, furthermore, specific depth threshold is defined for next node selection. Thus, it helps to measure expected delay and energy consumption. \\ \hline \multirow{2}{*}{AHH-VBF [27]} & Consumption of battery issues are highly affected & Energy consumption model is provided for the approximate energy utilization of network \\ \hline \multirow{4}{*}{NADEEM [28]} & Low network throughput, fails to find another routing path when the void nodes are present on a dedicated path & Expected transmission count is considered for the selection of reliable link to maximize network throughput \\ \hline \multirow{4}{*}{ASEDG [29]} & Link quality and AUVs time for data gathering process not considered, thus unexpected delay occur & Link quality is measured for data gathering process and expected delay is measured \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with state of the art routing protocols.
# Real-Time Kinematic Positioning Using Multi-Frequency Smartphone Measurements +
## 4 Kinematic Tests  
As described in Section 2, two automotive tests were conducted, first fixing the smartphone to the windshield of the car and then placing the smartphone on the roof of the car. In addition, a further kinematic test was carried out by fixing the smartphone on the windshield of a tractor. The full path travelled by the car during the automotive tests is shown in Figure 7a. During the automotive tests, the base was at a distance below 5 km. As mentioned in Section 4, the path was characterised by the presence of houses and trees with foliage, rendering signal reception quite difficult. In Figure 8a,b, sections of the path are also reported. The solution provided by the smartphone (for the case of the smartphone placed inside the car and fixed on the windshield) is reported in blue while the solution given by the reference is represented in green. The pictures show that the smartphone solution follows the reference one quite closely, even in the correspondence of the curves. The statistics of the tests are reported in Table 4, along with those obtained for the reference ones. As expected, when the smartphone is placed on the roof of the car, better performance can be achieved in terms of TTFF, thanks to better satellite visibility. However, in both cases, the percentage of fix solutions is above 75%. In Figure 9, the full path travelled with the tractor is shown. Also, in this case, the smartphone solution (in blue) closely follows the reference one (in green). However, in correspondence with the turns, some outliers are present. Indeed, the turns are more challenging due to the dynamic changes. The statistics for this test are also reported in Table 4. In this case, the performance is slightly better than the automotive test cases thanks to the most favourable scenarios since the field where the test was performed was under quasi-open sky conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Base**} & \multirow{2}{*}{**Samples**} & **TTFF** & \multirow{2}{*}{**Fix \%**} & **Horizontal** & **Vertical** \\  & & **[s]** & & **Precision [m]** & **Precision [m]** \\ \hline CN09 & 9560 & 28 & 97.6 & 0.020 & 0.037 \\ \hline CGU2 & 9560 & 29 & 95.9 & 0.046 & 0.048 \\ \hline GUAS & 9560 & 175 & 91.3 & 0.090 & 0.134 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Internal antenna (CN09: 1 km, CGU2: 5 km, GUAS: 25 km).  
Figure 7: (**a**) Kinematic test path; (**b**) section of the path of the kinematic tests with the smartphone placed inside the car (green: reference solution, blue: smartphone solution).
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 2 Background and Related Work
### Software-Defined Data Center
Such technologies are designed to consolidate and deliver the security components needed to support a fully virtualized infrastructure [22; 23]. Software-defined technologies have been used for efficient data center management in several studies. For instance, [PERSON] et al. [11] introduced an SDDC architecture for QoS-aware serviceorchestration and developed an integrated system of software-defined technologies for fog computing, SK Telecom is working on implementing an SDDC with the aim of building a telco-centric data center [6]. [PERSON] and [PERSON] [24] implemented a system that provides a load-aware virtual networking service in SDDC. In [25], [PERSON] et al. introduced a strategy of power saving for large-scale SDDC networks. They formulated a mathematical model of a power saving problem based on energy consumption characteristics with the constraint of satisfying energy optimization effectiveness. [PERSON] et al. [26] considered traffic engineering strategy for SDDC WANs. With the SDN functionality, they proposed a strategy of reducing data plane failure by rerouting the victim flows from failed tunnels to other healthy tunnels. In [27], [PERSON] et al. used an SDN controller to enable the central control of the data center network, and they proposed a joint optimization model to achieve high bandwidth utilization for provider and low delay for users. [PERSON] et al. [11] proposed software-defined computing based system that abstracts connected entities as services and allows applications to orchestrate with end-to-end QoS requirements. In [28], software-defined data service (SDDS) was introduced. SDDS is a network-aware data service approach that extends and leverages SDN technologies in a data center. It aims to minimize the network bandwidth overhead by reducing the data movements based on the network traffic statistics received from the SDN controller. By leveraging the global awareness of the network topology and network flow statistics, SDDS scales and distributes the data services in a network-aware manner. While SDDS focuses on the efficient execution of data-centric services using SDN functionalities, SDDC attempts to mitigate a scalability problem of virtualization and softwarization in an entire data center. Software-Defined technologies enable more convenient resource management of the data center, since monitoring and automation of the entire infrastructure can be abstracted from hardware and implemented in software. In this regard, functionalities of the SDDC are efficiently used in existing works for various purposes such as improving service performance, saving energy, and reducing operational cost of the data center.
# Investigations on Interface Shear Fatigue of Semi-Precast Slabs with Lattice Girders
## 4 Fatigue Tests on Semi-Precast Slabs with Lattice Girders
### Test Results
#### 4.5.3 Influence of Interface Reinforcement Ratio  
Figure 18 shows the influence of the interface reinforcement ratio realized by two, three, and four rows of lattice girders by crack patterns of the failure crack in the first sub-test. The compared specimens of Series 1 had a concrete strength of C25/30 and an interface roughness of about \(R_{\text{t}}\) = 0.9 mm.  
Test Specimen EG03 with small interface reinforcement ratio shows a clear interface failure with considerable interface slip of 1.5 cm. Specimen EG04 with a medium interface reinforcement ratio failed by a shear crack without horizontal interface displacement and only a small interface opening. Specimen EG08 with a high interface reinforcement ratio also failed by a shear crack. A delamination of the interface could not be identified.  
Figure 18: Comparison of interface reinforcement ratio: Crack patterns of Specimens EG03, EG04, and EG08.
## 3 Results and Discussion  
### Effect of Soil Salinity on Germination Percentage (GP) and Mean Germination Time (MGT)  
Increased salinity negatively affected the germination percentage (GP) and mean germination time (MGT) of three _Chloris gayana_ genotypes. The GP showed a decreasing trend with the increasing salinity and ranged between 68.3-71.7% in control (0 dS m\({}^{-1}\)), 63.2-67.0% at 5 dS m\({}^{-1}\), 53.3-56.7% at 10 dS m\({}^{-1}\), 50.0-51.7% at 15 dS m\({}^{-1}\), and 45.0-48.3% at 20 dS m\({}^{-1}\) (Figure 2a). The highest GP at each salinity level was obtained for ILRI-6633, followed by ILRI-7384 except for higher salinity level of 20 dS m\({}^{-1}\) where CV-massaba performed better than ILRI-7384.  
The germination of all three genotypes was delayed with the increasing salinity, i.e., from eight days in control (0 dS m\({}^{-1}\)) to more than 12 days for higher salinity (20 dS m\({}^{-1}\)) (Figure 2b). The local genotype CV-massaba germinated faster than the other two genotypes under all salinity levels. This  
Figure 2: Effect of different salinity levels on **(a)** Germination Percentage (LSD = 6.69; CV = 12.32) and **(b)** Mean Germination Time (LSD = 0.86; CV = 9.68) of three selected Chloris gayana genotypes.  
shows that CV-massaba is more tolerant to salinity as far as germination is concerned. On average, the delay in MGT was 4, 13.3, 21.7, and 39.1% at 5, 10, 15, and 20 dS m\({}^{-1}\), respectively. The average MGT for all genotypes was 7.2 days in control, with ILRI-6633 being the maximum (7.9 days) and CV-massaba the minimum (6.8 days). At the highest salinity (20 dS m\({}^{-1}\)), the average MGT for ILRI-7384 was 12.2 days, followed by 12.1 days for ILRI-6633 and 11.2 days for CV-massaba. This shows that although CV-massaba germinated faster, its germination percentage was lower than the other two genotypes. In conclusion, the GP and MGT of all genotypes was negatively influenced by increasing salt stress in the soil. These results agree with the findings of [PERSON] et al. (1989) ([PERSON] et al., 1989) for perennial ryegrass and [PERSON] et al. (2006) ([PERSON] et al., 2006) for the teff grass.  
Crop genotypes that can effectively germinate under salt stress conditions do not necessarily qualify for a good seedling growth ([PERSON] et al., 2006). However, in our case, ILRI-6633 and CV-massaba genotypes showed promising results not only in terms of germination percentage (GP) and mean germination time (MGT) but also seedling growth. Studies have shown that the crops with higher germination percentage have the capacity to establish themselves effectively on moderate to high saline soils (ECe = 5-12 dS m\({}^{-1}\)) ([PERSON] et al., 2006). Although it is difficult to generalize pot results for field conditions, ILRI-6633 and CV-massaba genotypes can potentially germinate and establish in moderate to high saline areas. This will help in producing high biomass because yield losses due to poor germination and inadequate crop stand establishment will be minimum ([PERSON] et al., 2006; [PERSON] et al., 2006).
# Model Experimental Study on De-Icing Method of Bridge Pylon Beam Based on Electric Heating
## 4 Results and Analysis of De-Icing Tests
### Anti-icing Tests
#### 4.1.3 Suggested Environment Range of Application

Table 4: The test conditions and the ice coverage ratio of icing tests under windy conditions. In the five groups of anti-icing tests under windy conditions, three groups were iced. When the wind velocity was 5 m/s, the surface of the heating system froze in both sets of tests. When the wind velocity was 3 m/s, the surface of the heating system only froze in an environment of \(-15\)\({}^{\circ}\)C. Therefore, the applicable environment range of the heating system should be adjusted according to the wind velocity.