esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Gridded satellite precipitation products (GPPs), each with individual strengths and differences, were compared on daily, monthly, seasonal and interannual scales. The main conclusions of the study are listed as follows.  
The GPPs captured a consistent climatological precipitation distribution over the diverse topography of Pakistan. The multiyear monthly mean precipitation had the same pattern as in-situ precipitation although differences are obvious at regional scale. With in-situ precipitation as a reference, we see that GPPs are able to capture extreme precipitation events (95 th percentile) quite consistently, where TMPA and MSWEP were found to be most similar to in-situ, followed by CHIRPS. The performance of GPPs improves with increased (>5 mm/day) thresholds. The Correlation/RMSD values were higher during peak precipitation seasons.  
Based on the performance of the GPPS, they can be used (with proper adjustments) to fill in the gaps between gauges for application to flood forecasting, droughs, land atmosphere interactions studies, extreme events, agricultural planning and many others [34; 35; 37]. The source of the deviations need to be further explored, with detailed studies of the possible reasons for these differences in both GPPs and in-situ data.  
Conceptualization, W.U. and G.W.; methodology, W.U. and G.W.; software, W.U. and D.F.T.H.; validation, W.U.; formal analysis, W.U. and G.U.; investigation, W.U. and G.W.; resources, G.W.; data curation, G.A. and A.S.B.; writing--W.U. and G.W.; writing--review and editing, W.U., G.W.; visualization, D.F.T.H. and D.L.; supervision, G.W.; project administration, G.W.; funding acquisition, G.W.  
This research was funded by National Key Research and Development Program of China (2017 YFA0603701) and National Natural Science Foundation of China (41875094, 41561124014 and 41375099).  
We used TRMM-TMPA, MSWEP, CHIRPS and PERSIANN-CDR precipitation product along with In-situ data from Pakistan Meteorological Department. The authors cordially acknowledge and appreciate the efforts of the agencies and scientist whose efforts made it possible to provide these datasets, without which the current study would not be possible. At last, we thanks the College of International Students (CIS-NUIST) for their continuous guidance and administrative support.  
The authors declare no conflict of interest.
## 6 Conclusions  
We presented the analysis of UORA channel access and the performance enhancement of the proposed V2X-UORA channel access procedure to support IEEE 802.11p-based NG-V2X systems. Unlike conventional IEEE 802.11p channel access, the proposed V2X-UORA channel access does not reduce the average channel access delay when more channel bandwidth is used. This means that using conventional IEEE 802.11p channel access is still a good option if a V2X application requires a much lower delay in a sparse STAs scenario. However, the proposed V2X-UORA exhibits better user capacity performance in terms of meeting the QoS delay requirement in wideband transmission and dense STAs scenarios. This means that the proposed V2X UORA is a more suitable channel access method for dense V2X STAs scenarios. Furthermore, we examined how dedicated RU-based V2X UORA channel access considerably enhances V2X user capacity performance. If the IEEE 802.11p-based NG-V2X system considers sensor STAs gathering traffic information, the proposed V2X UORA with dedicated RUs need to be considered. As we observed in the analysis of the UORA procedure, the UORA contention window can affect UORA channel access performance. Fortunately, we could observe that controlling OCW can be performed with a long-term decision-making algorithm because the performance does not change dramatically if the UORA CW value lies in the range of the UORA CW sweet spot. Because all proposed methods in this paper are designed under the consideration of backward compatibility and real implementation, they can be expected to be considered as an option in the NG-V2X channel access procedure.  
Conceptualization, J.A.; Data curation, J.A.; Formal analysis, J.A.; Funding acquisition, R.Y.K.; Investigation, J.A. and R.Y.K.; Methodology, J.A. and R.Y.K.; Project administration, R.Y.K.; Resources, R.Y.K.; Software, J.A.; Supervision, Y.Y.K. and R.Y.K.; Validation, Y.Y.K. and R.Y.K.; Writing--original draft, J.A.; Writing--review & editing, Y.Y.K. and R.Y.K.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning, grant number NRF-2017R1A2B4003987.  
The authors declare no conflict of interest.
# Biomechanical Performance of the Cemented Hip Stem with Different Surface Finish
## 2 Experimental Work  
### Specim Preparation and Mechanical Tests  
To assess the bonding strength of the interface between bone cement and the metal stem, a number of specimens were prepared from acrylic bone cement and metal samples. The configuration and dimension of the cement-metal specimens are depicted in Figure 1. Preparation procedures for these interfacial specimens are briefly described as follows. First, metal samples of medium carbon steel were machined into rectangular shapes (\(30\times 10\times 5\) mm). To investigate the influence of surface roughness on the interfacial bonding strength, these samples were then divided into two groups--those with a smooth surface and those with a rough surface (Figure 2). The bonding surface of each metal sample was then fine-machined to be smooth (Ra = 0.14 \(\mu\)m), while samples in the rough surface group were sand-blasted to a roughness of 5.56 \(\mu\)m approximately. The bone cement was then prepared by mixing PMMA (Polymethyl methacrylate) polymer powder with a liquid monomer in a container according to the manufacturer's guidelines. The metal samples were individually placed in mold cavities, after which mixed bone cement was added. Manually exerted pressure was maintained during curing to ensure firm bonding with the metal samples. After the bone cement polymerized, the specimens were carefully removed from the mold cavities and verified by visual examination to ensure their integrity for testing.  
The specimens were separately fixed in custom grips for tests using a computerized universal testing machine (Figure 1). During testing, the upper crosshead imposed a tensile force on the specimen at a rate of 2.0 mm/min until the interface failed completely. The critical load \(F_{cr}\) at the fracture instant was obtained from the load-displacement diagram recorded by the computerized data acquisition system. This information was then used to calculate the interfacial strength according to the formula \(F_{cr}/A\), where \(A\) is the bonding area between the metal and bone cement. Additionally, the load-displacement diagram was converted into a stress-displacement curve by dividing the applied force by the bonding area, as illustrated in Figure 3. The slope of the stress-displacement curve represented the relative stiffness \(K_{int}\) of the interface between the bone cement and the metal. This curve was determined by applying the least squares fitting procedure to the stress-displacement curve for applied stress levels of less than 50% of the failure strength [20].  
Figure 1: Geometry dimensions of metal/cement specimens and testing configuration for tensile and shear tests, respectively. (W = 30 mm, H = 10 mm, B = 5 mm, D = 10 mm).  
Figure 2: Surface topography of the metal specimens (a) polished; (b) sand-blasted.
## 3 Results and Discussion
### Turbulent Flow Models
Of course a caveat must be made that in this example, a selected profile of velocity at the location 10 mm away from the joint above the cold plate is considered. The velocity magnitudes tabulated in Table 1 can also be used to represent the gas displacement, \(\delta=u_{m}/2\pi f\); hence, the drive ratios of 0.30%, 0.45%, 0.65% and 0.83% correspond to the gas displacements of 16, 23, 36 and 47 mm, respectively. Clearly, the gas parcel in the flow with a drive ratio higher than 0.65% moves by a distance longer than half of the total length of the heat exchanger channel (70 mm). The forward and backward movements of the gas parcel convey the energy of the vortex structures at the end of plates into the channel. Vortex structures of the selected phase, \(\phi 8\), obtained from the numerical calculations for drive ratios of 0.3% and 0.83% are shown in Figure 5:5 Evidently, the strong vortices appearing in the plate wake for the drive ratio of 0.83% can create strong flow disturbances when pushed back into the channel. This could be a possible explanation for the appearance of turbulence (and the need for transition/turbulence model) at this drive ratio as a means of dissipating the flow energy. The growth of any flow disturbances that occur between the plates of the heat exchanger can also be complicated by the effects of the thermal expansion that causes the gas particles to move at a different displacement amplitude for the two halves of one cycle. The short length of the plates investigated in this study prohibits the flow of high drive ratios to reach a fully-developed state. Most heat exchangers are short, and a practical system works with a high drive ratio. This study suggests that turbulence is likely to occur within heat exchangers working in the oscillatory flow condition for Stokes Reynolds numbers well below the critical value of 400 given by [PERSON] and [PERSON] (2007). Figure 5:6 Plot of metric \(\sigma_{met}\) for laminar, transition-SST and SST k-\(\omega\) models vs. the drive ratio.
## 3 Results  
### Effect of O\({}_{2}\) Concentration on DRE of NO and NO\({}_{x}\)  
Figure 3 showed the DRE of NO and NO\({}_{\rm x}\) under different O\({}_{2}\) concentration in the TM-DBD and CC-DBD reactors. O\({}_{2}\) not only competitively shares the input power, but contributes to the oxidation of NO. Therefore, the DRE of NO\({}_{\rm x}\) decreases from 75% to 35% in CC-DBD reactor and from 87% to 20% in the TM-DBD reactor with an increase of O\({}_{2}\) from 0 to 15.9%. However, DRE of NO first decreases and then sharply increases to 99% in CC-DBD reactor, while the DRE of NO first decreases and then keeps little change at about 69% in the TM-DBD reactor. In addition, the results also indicated that the DRE of NO and NO\({}_{\rm x}\) in the TM-DBD reactor was better than in the CC-DBD reactor when O\({}_{2}\) concentration was low, and the opposite result was seen under higher O\({}_{2}\) concentration. Thus, it can be seen that the electrode structure has important impact on the DRE of NO and NO\({}_{\rm x}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{4}{c}{**Model Parameters of DBD in VSim**} \\ \hline Grid numbers & \(\text{X}*\text{Y}*\text{Z}=100*100*200\) \\ \hline \multicolumn{4}{c}{Numbers of gyrating circles} & 50 \\ Numbers of microparticle & 50,000 \\ \hline \multicolumn{4}{c}{Geometry Parameters of CC-DBD and TM-DBD in VSim} \\ \hline \multirow{2}{*}{TM-DBD} & \(\text{R}_{\rm T}/\text{mm}\) & \(\text{r}_{\rm T}/\text{mm}\) & \(\text{D}_{\rm T}/\text{mm}\) \\  & 3 & 1.5 & 4 \\ \hline \multirow{2}{*}{CC-DBD} & \(\text{R}_{\rm C}/\text{mm}\) & \(\text{r}_{\rm C}/\text{mm}\) & \(\text{D}_{\rm C}/\text{mm}\) & \(\text{R}_{\rm oc}/\text{mm}\) \\  & 3 & 1.5 & 4 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Model parameters of DBD in VSim and Geometry parameters of CC-DBD and TM-DBD in VSim.
# Lifetime Measurement of Cesium Atoms Using a Cold Rydberg Gas
## 3 Experimental Method and Results
### Blackbody Radiation Induced Decay  
In this section, we consider the effect of the BBR on the decay rate of Rydberg state. As we know that the room temperature BBR has a negligible effect for the ground atom, but has a strong effect for electronically high-lying Rydberg states and enhance the decay rate of Rydberg states [22; 23; 24; 25]. Taking into account the BBR effect, the effective radiation decay rate of Rydberg atoms \(\Gamma_{eff}(T)\) can be written as  
\[\Gamma_{eff}(T)=\Gamma_{sp}+\Gamma_{BBR}(T) \tag{3}\]  
with \(\Gamma_{BBR}(T)\) the decay rate due to blackbody radiation at \(T\). Therefore, the corresponding lifetime can be written as  
\[\frac{1}{\tau_{eff}}=\frac{1}{\tau_{sp}}+\frac{1}{\tau_{BBR}} \tag{4}\]  
where \(\tau_{BBR}\) is blackbody radiation lifetime of Rydberg atom. The spontaneous radiation rate \(\Gamma_{sp}\) and the related lifetime \(\tau_{sp}\) for the give Rydberg state can be calculated with Equations (1) and (2). Using the Coulomb approximation method, [PERSON] et al. [5] calculates the BBR induced decay rate \(\Gamma_{BBR}\) at \(T\) for \(10\leq n\leq 80\) range. Decay rate due to BBR, \(\Gamma_{BBR}\), can be written as [5]  
\[\Gamma_{BBR}=\frac{A}{n_{eff}^{D}}\frac{2.14\times 10^{10}}{\exp(\frac{315 780B}{n_{eff}^{C}T})-1}(\text{s}^{-1}) \tag{5}\]  
where \(A,B,C\) and \(D\) are fitting parameters obtained from the fit of the numerical results based on a quasi-classical calculation [5] and corresponding values for Cs are given in Table 1, \(n_{eff}=n-\delta_{L}\) is the effective quantum number (\(\delta_{L}\) is quantum defect). At room temperature of 300 K, the calculated decay rate due to BBR is \(\Gamma_{BBR}=4.35\) kHz and the related lifetime is \(\tau_{BBR}=229.9\)\(\upmu\)s for \(|65D_{5/2}\rangle\) Rydberg state. The corresponding effective lifetime, \(\tau_{eff}=94.1\)\(\upmu\)s, is consistent with the experimental result of \(\tau_{exp}=94.1\pm 2.5\)\(\upmu\)s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**State** & \(\mathbf{A}\) & \(\mathbf{B}\) & \(\mathbf{C}\) & \(\mathbf{D}\) \\ \hline \(S_{1/2}\) & 0.123 & 0.231 & 2.517 & 4.375 \\ \hline \(D_{5/2}\) & 0.036 & 0.073 & 1.770 & 3.636 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Value of parameters \(A,B,C\), and \(D\) of Cs in Equation (5).  
Figure 2: (Color online) Normalized population of the \(|65D_{5/2}\rangle\) state as a function of the delay time \(t_{D}\). Samples are the averaged results of 100 measurements and the red line displays the fitting of the exponential function. The extracted lifetime is 94.1 \(\pm\) 2.5 \(\upmu\)s. The inset is the measured spectrum when we scan the second excitation laser frequency for the delay time \(t_{D}\) = 5 \(\upmu\)s, the linewidth of 7.35 MHz is obtained by Lorentz fitting shown with the red line.
# Ras Isoforms from Lab Benches to Lives--What Are We Missing and How Far Are We?
## 5 Missing Pieces in the Ras Puzzle
### One Ras and Many Locations
#### 5.6.4 Ras and Mitochondria  
Mitochondria and the nucleus have also been identified to contain Ras isoforms. H-Ras splice variant which lacks the HVR region is present in the cytosol as well as the nucleus. The presence of N and K-Ras in mitochondria has been characterized. Cellular N and K-Ras were both found to be constitutively associated with purified mitochondrial fractions. N-Ras was detected in mitochondria purified from cultured cells and is associated with both the outer and inner mitochondrial membrane. While K-Ras is associated with the outer mitochondrial membrane [150]. N-Ras knockout cells showed abnormal mitochondrial morphology with concentric cristae. Normal morphology is reinstated in these cells by targeting N-Ras to the inner and outer mitochondrial membrane. K-Ras knockouts also exhibited similar mitochondrial structures that were reversed on ectopic expression of K-Ras in these cells [151]. A study by [PERSON] et al. in 1999 showed the dependence of the expression of Ras isoform in mitochondria on IL-2 supplementation. K-Ras is present in mitochondria of IL-2 supplemented cells and its expression was not detected 12 h after deprivation of IL-2. Contrary to this, H-Ras was present only in mitochondria of IL-2 depleted cells. N-Ras was detected in mitochondria of both IL-2 depleted and enriched cells. Co-immunoprecipitation showed the association of K and N-Ras with Bcl2 in IL-2 supplemented cells and with H-Ras in IL-2 deprived cells [152]. This study also showed that the association of Ras isoforms is independent of their posttranslational modifications.  
[PERSON] et al. reported the presence of H-Ras in both cytoplasmic and nuclear extracts, and treatment with FTI caused the loss of H-Ras from the nucleus. This presence of H-Ras in the nucleus synchronized with different phases of the cell cycle in both transformed as well as Ras-transformed and non-transformed cells. The peak of the H-Ras signal corresponded to the S-phase of the cell cycle implying a role of this isoform in replication [153]. Figure 6 shows the various subcellular locations of H, K, and N-Ras.
## 3 FLARoute: Fair Locality Aware Routing
### Overview of FLARoute
To address this, we propose ReBurD, a mechanism that keeps account of message transmissions to determine the incurred burden. With ReBurD, a node increments counters each time it receives or sends a message on behalf of others. The relative burden for the node is determined at the end of a fixed time interval, after which the counters are reset. This allows a non-scenario-specific representation of node burden without utilizing buffer information. Phase 3. The third phase is concerned with locally estimating the maximum burden on neighbouring nodes in order to make fair routing decisions. Burden estimates are embedded in summary vectors and exchanged when two nodes encounter each other. Through this, a node is able to  
Figure 6: Functional block diagram of Fair Locality Aware Routing (FLARoute). update its maximum burden estimate when an encountered node presents a higher estimate. The main challenge in this phase is how to maintain a local estimate that is fairly close to the global value at all times. To achieve this, we propose MaxBE, a mechanism that keeps account of the maximum relative burden on encountered nodes with which it periodically estimates the maximum burden on neighbouring nodes. Nodes are able to make fair forwarding decisions with this estimate. Phase 4. The fourth phase is tasked with making forwarding decisions with the knowledge accumulated from the first three phases. In order to ensure fairness and delivery guarantees, messages are sent through two types of routes: one that presents a higher change of delivery, regardless the level of burden on relay nodes; and one that can make progress through less burdened nodes. To reduce the risk of overburdening popular nodes, the number of times a message is forwarded through the former route is kept minimal, while multiple copies of the message may be forwarded through the latter route. Since more transmissions are likely to increase the burden on nodes, a burden balancing mechanism is also proposed in this phase, to relieve over-burdened nodes of messages traversing the latter route. The following assumptions are made in the design of FLARoute: (i) each node is a smart mobile device and is equipped with a Global Positioning System (GPS); (ii) nodes are collaborative and willing to participate in routing; and (iii) source nodes have the necessary information for destinations, which are node ID and location-based information in this case.
## 7 Conclusions  
The GM model and FDGM can be expressed adopting a pole-zero formulation. This formulation, that in logarithmic scale becomes a superposition of pole-zero couple behavior, allows to overcome the computational and convergence issues that occur in parameter identification of a rheological model in the time domain and frequency domain form. Thus, a constrained pole-zero identification procedure has been defined. The convenience of the pole-zero formulation in the identification of parameters of transfer functions lies in the determination of boundary conditions and initial condition of the optimization problem. Using this approach, an extensive study has been carried out to evaluate the capability of the GM and FDGM models in properly describing the viscoelastic behavior of three materials, the mechanical behavior of which was previously characterized by means of DMA tests. The analysis returns that both GM an FDGM models are able to fit the viscoelastic behavior of the compounds in the entire frequency range in which they are experimentally characterized, but the GM model needs a number of elements that are higher than the FDGM one. From a practical point of view, this means that FDGM is able to provide good matching with the simple configuration with only ten variables (three elements), while comparable results can be obtained with the GM model identifying more than 51 parameters (25 elements). Thus, comparing the normalized errors and the relative curves of the three compounds, it is observed that the FDGM is able to fit the real viscoelastic behavior with the minimum number of parameters.  
The minimum number of experimental data needed to estimate the material behavior in a wide frequency range has also been investigated. The aim was to evaluate the capability of the FDGM model in moduli estimation starting from a reduced set of experimental data on which to make the fitting. An excellent prediction of the entire storage modulus and loss tangent can be obtained adopting data coming from the lower and upper frequencies plateau of the storage modulus, the peak of the loss tangent curve and the curvature changing of both curves. Furthermore, it is worth stressing that satisfying results can be achieved having also only the data available of the upper and low frequency plateaus plus the ones at the loss tangent peak.  
Figure 6: FDGM estimation of storage modulus and loss tangent for compound A adopting limited zones of experimental data.  
**Author Contributions:** Conceptualization, A.G. and A.S.; methodology, A.G., A.S. and F.C.; software, F.C.; validation, A.M. and F.F.; formal analysis, F.T. and A.S.; investigation, F.C. and A.M.; resources, F.F. and F.T.; data curation, F.C. and A.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** Authors declare no conflict of interest.
## 4 Numerical Simulation

Table 1: The finite time \(T\) values corresponding to different system parameters \(p\) and \(q\). Figure 1: The communication topology. With the initial conditions and other system parameters unchanged, setting \(p=q=1\) transitioned the system from finite-time consensus to asymptotic consensus. Figure 5 shows the variation in the consensus error curve under asymptotic consensus, where the consensus error clearly converged to zero after \(T=10\), but the exact convergence time could not be predicted in advance. In contrast, Figure 4 illustrates the finite-time consensus case, where not only could we accurately calculate the consensus time as \(T=8.2652\) based on the initial conditions, but the consensus error also converged to zero faster than in the asymptotic consensus case. Figure 4: The consensus error curves of agents under the event-triggered control (5). Figure 3: The states trajectory of agents’ velocity under the event-triggered control (5). Figure 2: The states trajectory of agents’ position under the event-triggered control (5). Under the event-triggered mechanism (8), Figure 6 supplies the update instants of the control protocol of each agent, which were the event-triggered instants \(t_{k}^{i}\). Each agent only sent control signals at the trigger instants \(t_{k}^{i}\) shown in Figure 6, significantly reducing the frequency of information transmission within the system, thereby conserving communication resources and bandwidth. Agents 1, agent 2, agent 3, and agent 5 had relatively large intervals between their trigger instants, and the controller remained constant during each trigger interval. As a result, the controller did not need to perform calculations or update the control inputs during the event-triggering intervals. This not only reduced the calculation frequency but also decreased the computational burden on the processor, enhancing the overall efficiency of the system. As shown in the figures, the bipartite consensus of hybrid-order heterogeneous multi-agent systems (1) is achieved within finite time under control protocols (5) based on Definition 2, which means that Theorem 1 is valid.