esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 OpenFlow Flow Table Memory Management
### Timeout and Eviction Mechanisms  
To overcome the limitation of the switch flow table storage (TCAM), OpenFlow allowed the use of a timeout mechanism to determine the life span of forwarding entry in the switch flow table [52]. When no packet matches an entry within its timeout period, it will cause an entry to be evicted from the flow table to free space for new incoming packets. Currently, there are two major methods for the OpenFlow controller to install the timeout mechanism, idle and hard timeout. OpenFlow controller usually configures flow entry with fixed idle timeout value in the scale of seconds across flows [56]. However, such timeout value could give better performance when it is close to packet inter-arrival time and the flow table has sufficient space to accommodate all flows. Moreover, this method could be straightforwardly implemented without much computational overhead, especially in a fixed network environment. However, setting fixed value across all flow may adapt to dynamic traffic flows especially in a large-scale network setting because of the variability exhibited by flow as illustrated in Figure 8. This has recently gained research interest to devise a scheme that will improve the flow lifespan, as shown in Table 2.  
In Figure 8, suppose a number of packets that have been transmitted until time t1, t2 are vt1 and vt2, respectively. F1 represents short flow with a small number of packets at both t1 and t2. While F2 shows long flow with a small number of packets and F3 illustrates short flow with a large number of packets. Finally, F4 has a long flow with large packets. Therefore, it is required to investigate the feasibility of adopting a flow timeout strategy that can incorporate fixed and dynamic timeout values based on observed natures of intra-flow packet inter-arrival time in Figure 8[46]. Applying fixed timeout value across the flow, which used to be the conventional method, makes it difficult to cope with the current traffic pattern, given that flows extreme exhibit variabilities in statistics such as duration  
Figure 7: Taxonomy of flow entries management. and volume. Moreover, [47] reports the impact of timeout value on flow statistics and found there is a performance trade-off. While large timeout value may unnecessarily preserve a large number of flows with no packet expected. As for small timeout may lead to premature eviction of flows, which increase flow set up request and teardown long live flow into a smaller flow. Consequence flows may be evicted more often, which causes the generation of a more packet-in message to the controller, such a situation increases communication overhead between switch to controller.
# The Vehicle Routing Problem: State-of-the-Art Classification and Review
## 3 VRP and Its Variants
### VRP Variants
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Multi-Object** & **Single-Object** & **Dataset** & **Max Nodes** & **Other Settings** \\ \hline \multirow{4}{*}{13} & minimize the total traveling & & [[http://www.coin-or.org/](http://www.coin-or.org/)]([http://www.coin-or.org/](http://www.coin-or.org/)) & \multirow{4}{*}{32} & \multirow{4}{*}{13} \\  & cost, maximize the prizes & & & \\  & collected by all vehicles & & VRP/data/index.htm.old & & \\  & & & accessed on 14 June 2018 & & \\ \hline \multirow{4}{*}{14} & minimize the operational cost & & & & \\  & when visiting customers & self-generation & 200 & 14 \\ \hline \multirow{4}{*}{15} & & minimize the makespan & test instances adopted from [105] & 50 & 15 \\ \hline \multirow{4}{*}{16} & minimize transportation and & & & & \\  & time cost & & & & \\  & & & & & \\ \hline \multirow{4}{*}{17} & minimize total driving and & & [PERSON] et al.’s [139] testbed & \multirow{4}{*}{20} & \multirow{4}{*}{17} \\  & charging time & & [[http://vrp-rep.org](http://vrp-rep.org)]([http://vrp-rep.org](http://vrp-rep.org)), accessed on & & \\  & & & 18 June 2018). & & \\ \hline \multirow{4}{*}{18} & & minimize carbon emission & test instances adopted from [107] & \multirow{4}{*}{100} & \multirow{4}{*}{18} \\ \hline \multirow{4}{*}{19} & & & test instances adopted from [107], & \multirow{4}{*}{19} \\  & minimize the number of & & includes 56 instances divided into & & \\  & vehicles & six sets with 100 nodes; includes & & \\  & & 300 instances of different sizes & & \\  & & & from [108] & & \\ \hline \multirow{4}{*}{20} & minimize the total distance & & & & \\  & travelled by vehicles & self-generation & 80 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._current single-objective problem research tend to be compound costs representing actual delivery costs.
## 5 Procedure  
Six private hospitals were included in the study. Four hospitals were in the Riyadh Region, Saudi Arabia. The fifth hospital was in Al Qassim, Saudi Arabia, and the sixth hospital was in Dubai, UAE. Researchers contacted all research and academic coordinators in the six hospitals to demonstrate the surveys to the staff; all coordinators were aware of the objectives of the study and trained in data collection by their own departments. For this research, data were collected between 2017 and 2018 from hospitals in six regions in the Gulf Cooperation Council Region. This research conformed to all ethical guidelines necessary for conducting human research, including the legal requirements of Saudi Arabia. Furthermore, this research project was granted approval from the Institutional Review Board chosen in this study (RC18.11.21). It is important to state that this study did not require ethical approval, as there was no treatment involved, i.e., medical treatment or invasive procedures that could lead to participants' discomfort, and no patients were included in the data collection. Following ethical  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**CFA**} & \multicolumn{2}{c}{**EFA**} & \\ \hline
**Demographic** & **N** & **\%** & **N** & **\%** & **N** & **\%** & \(\mathbf{X^{2}}\) & \(\mathbf{p\) Value *} \\ \hline
**Gender** & & & & & & & & \\ Male & 231 & 26.2\% & 153 & 26.2\% & 78 & 26.2\% & \textless{}0.001 & 0.99 \\ Female & 651 & 73.8\% & 431 & 73.8\% & 220 & 73.8\% & & \\ \hline
**Profession** & & & & & & & \\ Physician & 115 & 13.1\% & 80 & 13.7\% & 35 & 11.8\% & 5.38 & 0.14 \\ Nurse & 612 & 69.6\% & 392 & 67.2\% & 220 & 74.3\% & & \\ Respiratory & 18 & 2.0\% & 12 & 2.1\% & 6 & 2.0\% & & \\ Therapist & Others & 134 & 15.2\% & 99 & 17.0\% & 35 & 11.8\% & & \\ \hline
**Nationality** & & & & & & & & \\ Saudi & 84 & 9.9\% & 55 & 9.7\% & 29 & 10.3\% & 0.08 & 0.78 \\ Non-Saudi & 766 & 90.1\% & 513 & 90.3\% & 253 & 89.7\% & & \\ \hline
**Experience** & & & & & & & \\
1 to 5 years & 405 & 46.6\% & 264 & 45.9\% & 141 & 47.8\% & 0.70 & 0.70 \\
6 to 10 years & 338 & 38.9\% & 229 & 39.8\% & 109 & 36.9\% & & \\ \textgreater{}10 years & 127 & 14.6\% & 82 & 14.3\% & 45 & 15.3\% & & \\ \hline
**Marital Status** & & & & & & & \\ Single & 476 & 54.8\% & 306 & 53.5\% & 170 & 57.4\% & 1.30 & 0.52 \\ Married & 378 & 43.5\% & 256 & 44.8\% & 122 & 41.2\% & & \\ Divorced & 14 & 1.6\% & 10 & 1.7\% & 4 & 1.4\% & & \\ \hline \hline \end{tabular} * Pearson chi square test.  
\end{table}
Table 1: Demographic profile of participants.  
approval, chiefs of departments and nurse coordinators from the hospitals were then asked to assist in administering the questionnaire to the healthcare workers in the hospital. Participants who volunteered in this research did not receive any form of reward. Furthermore, the cover page of the questionnaire clearly stated the aim of the research, the voluntary nature of participation, the anonymity of the data collected, and the elaboration of the research findings.
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Magnesium Oxide Nanoparticles  
Magnesium is an important biogenic element in the human body. It is a cofactor for more than 300 enzymatic reactions and is essential for DNA, RNA, and protein synthesis. It is also involved in the metabolism of vitamin D and calcium [138]. Therefore, scaffolds developed from various materials in combination with magnesium regulate not only cellular behavior but also stimulate bone formation and healing. Such materials release magnesium and thus promote the cell adhesion, proliferation, and osteogenic differentiation of MSCs [139].  
Magnesium oxide nanoparticles (MgO-NPs) and magnesium-based scaffolds are very easily biodegradable. A high level of degradability, however, can cause scaffold failure in vivo. The rate of degradation and bactericidal activity of these alloys is reduced up to 5 times when graphene nanoparticles are used in the Mg-Al-Cu matrix [140]. A disadvantage of synthetic polymer scaffolds is the release of acidic products during degradation, which may promote the conditions for the beginning of inflammation. The polymer containing magnesium hydroxide nanoparticles exhibits pH neutralizing properties, which significantly reduces the release of proinflammatory cytokines [141]. The polymeric scaffolds can be reinforced with an Mg-doped bioactive glass to enhance mechanical properties and biological reactivity [142].  
The challenge remains to create a scaffold with the effective release of magnesium cations. For example, a nanocomposite hydrogel based on hyaluronic acid (HA) and self-assembled bisphosphonate-magnesium nanoparticles exhibit improved mineralization capabilities and controlled release of magnesium cations [139].  
MgO-NPs have been found to show antibacterial activity with comparable results to standard 5.25% sodium hypochlorite, which is used for root canal disinfection in endodontics. They disrupt the cell membrane and induce oxidative stress, leading to cell death [143].  
The optimal concentration of MgO-NPs is important for cell growth on the scaffold. It was found that BM-MSCs density was increased significantly when cultured in 200 \(\mathrm{\SIUnitSymbolMicro m}\)/mL of MgO-NPs compared to the control. The additional benefit of MgO-NPs at a concentration of 200 \(\mathrm{\SIUnitSymbolMicro m}\)/mL is the reduction of bacterial adhesion. Therefore, at this concentration, MgO-NPs could be used as a coating material or as a part of a composite for potential medical implant applications. However, concentrations above 300 \(\mathrm{\SIUnitSymbolMicro m}\)/mL decreased the cell density, and concentrations above 1000 \(\mathrm{\SIUnitSymbolMicro m}\)/mL resulted in complete cell death [144].
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
## 4 Establishment and Calibration of Numerical Model
### Calibration of Numerical Model  
The difference between the Strain-Softening model and the Mohr-Coulomb model is that after the material reaches the stress peak, its cohesion, friction angle, and other properties decrease with plastic strain. In FLAC\({}^{\text{3D}}\), the material's mechanical parameters are assigned in the form of a table, reflecting the strain softening characteristics [51].  
A uniaxial compression test was conducted on standard samples of CPB materials in the laboratory to obtain the full stress-strain curve. The curve indicated that when the strain of the CPB material reached 0.1, the specimen still maintained a relatively high load-bearing capacity, with significant plastic deformation and apparent strain softening behavior. In FLAC\({}^{\text{3D}}\) numerical calculations, the Strain-Softening model can be used to describe the mechanical behavior of CPB materials [52].  
To obtain accurate parameters for the Strain-Softening model, a submodel method was employed for parameter calibration. A cylindrical model with a radius of 1 m and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Lithology** & **Thickness** & **Density** & **Bulk Modulus** & **Shear** & **Cohesion** & **Friction Angle** & **Tensile Strength** \\  & **/m** & **/(kg/m\({}^{3}\))** & **/GPa** & **/GPa** & **/MPa** & **/(\({}^{5}\))** & **/MPa** \\ \hline Carbonaceous mudstone & 4.5 & 2380 & 5.37 & 3.54 & 2.35 & 32 & 1.08 \\ Sandy mudstone & 6.1 & 2460 & 7.83 & 5.97 & 5.13 & 36 & 1.73 \\ Sandstone & 4.7 & 2620 & 11.73 & 9.12 & 6.04 & 39 & 3.73 \\ Sandy mudstone & 12.8 & 2460 & 7.83 & 5.97 & 5.13 & 36 & 1.73 \\ Sandstone & 12.0 & 2620 & 11.73 & 9.12 & 6.04 & 39 & 3.73 \\ Sandy mudstone & 3.0 & 2460 & 7.83 & 5.97 & 5.13 & 36 & 1.73 \\
3-1 coal & 4.3 & 1500 & 1.97 & 1.26 & 1.75 & 30 & 0.45 \\ Carbonaceous mudstone & 2.0 & 2380 & 5.37 & 3.54 & 2.35 & 32 & 1.08 \\ Sandy mudstone & 6.0 & 2460 & 7.83 & 5.97 & 5.13 & 36 & 1.73 \\ Sandstone & 8.0 & 2620 & 11.73 & 9.12 & 6.04 & 39 & 3.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of the model.  
Figure 6: Numerical model.  
a height of 4 m was established in FLAC\({}^{\text{3D}}\). A uniaxial compression test was performed using a strain-loading method with a loading rate of 2.5 \(\times\) 10\({}^{-5}\) m/step. Through a trial-and-error process, the internal friction angle and cohesion were adjusted until the stress-strain curve obtained from the numerical simulation closely matched the laboratory test results. The inverse calculation yielded the mechanical parameters for CPB material in the FLAC\({}^{\text{3D}}\) model. When the stress-strain curve from the numerical simulation showed good agreement with the experimental data, it confirmed that the calibrated parameters could be used to simulate the Strain-Softening model. The final Strain-Softening model mechanical parameters are shown in Tables 2 and 3, with the calibration results presented in Figure 7.
# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 2 Materials and Methods
### Input Data Sources  
The input data required to run the evapotranspiration models came from three main and two ancillary sources. The main sources were: Sentinel-2 shortwave observations, Sentinel-3 thermal observations and European Center for Medium-range Weather Forecasts (ECMWF) ERA-5 meteorological reanalysis data. The ancillary sources were: a digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) satellite, and land cover map created as part of the ESA Climate Change Initiative (CCI).
## 3 Proposal  
The design of any system architecture involving the usage of a WSN implies a set of challenges that must be considered. [PERSON] [11] identified in the past various factors that have an influence in the design of the network, including fault tolerance, scalability, hardware constraints, topology and communications among others. In the case of using also an event-driven model there are two other factors, regarding whether the event notification must be guaranteed, and if affirmative, which the accepted delivery time is.  
The proposal described in this section includes several innovative points. The most significant one is the way events in a WSN are modeled, as it leverages the virtualization of new node capabilities in the WSN and eases the integration of WSN in IoT infrastructures. Two different points of view are considered: the inner-WSN point of view, or how the nodes in the same WSN manage an event, and the outer-WSN point of view, or how the applications external to the WSN are notified of the triggered events and event sources.  
From the inner-WSN point of view, the nodes operate in a SOA-wise manner. Traditionally, the capabilities of the nodes in a WSN have not been exposed using well known approaches, broadly accepted in the business information systems, but proprietary solutions. In this proposal, the nodestriggering events must register in a service registry in the WSN and implement a service to register subscribers. The nodes that wish to be notified of specific events must look up the service registry for the proper event publisher and subscribe to the publisher event notifications by accessing the corresponding service. Thus, new virtual event sources can be made readily available by means of service composition or orchestration techniques.  
From the outer-WSN point of view, events are modeled complying with the ROA approach, similarly as proposed in [10, 12, 13, 14]. In this proposal, the list of available event sources in the WSN can be retrieved as any other RESTful resource. It is automatically updated due to the discovery protocol implemented by the components in the nSOM middleware. Each event source is described using ontologies, enhancing interoperability among different stakeholders' solutions. A hierarchical naming convention, as suggested in [10], is also proposed in this paper.  
The proposed model considers specially the response time, as it is a requirement of the scenario in which it will be tested. Additionally, the constrained nature of the resources is considered, in the form of reducing the amount of required messages between the entities participating in the notification of an event when it happens in the domain of the WSN.
# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 3 Methods  
### Data Preprocessing  
At first, invalid gauge observations with a quality control code of 1, 2, 5, 6, 7, 8 and 9 were removed. Then, 1381 stations (Figure 1b) with invalid records of less than 5% of the length of study period (438 h) were chosen for the evaluation. In total, more than 11.6 million reliable hourly gauge observations were finally used in the study. The time system of valid gauge measurements was converted to the Coordinated Universal Time to match with the SPPs records. Constrained by the time interval of gauge observations, the evaluation could not be conducted at finer temporal scales than hourly. The half-hourly IMERG estimates were therefore summed by hour to obtain hourly data which matched the gauge measurements. Similarly, hourly IMERG and valid gauge records were processed daily to investigate the daily-scale performance. Notably, a daily gauge record was considered invalid if the day contained invalid hourly measurements.  
Considering the limited spatial representativeness of a single gauge and the high density of the gauge observation network, the evaluation took the grid as a baseline by matching the grid estimate to the arithmetic average of the gauge measurements within the grid. Thus, only the IMERG grids containing more than one station were evaluated. There were 834 evaluated grids in the study area, accounting for 68.3% of the total grid number. Grids with no stations internally were mainly located at sea. This spatial matching method can, to some extent, alleviate the underestimation of the SPPs and yield more reliable assessment results [61; 62].  
For the performance comparison in urban and non-urban areas, we determined the land cover type for each IMERG grid based on GlobeLand30. Specifically, the type with the largest area in a grid was regarded as the type for the grid. Notably, grids with the type of artificial surfaces were considered as urban grids. Evaluations were conducted for three main land cover types: cultivated land, forest and urban, which accounted for 14.0%, 65.4% and 12.8% of the total evaluated grids, respectively. The other types covered very small areas with a fragmented distribution, and were therefore not considered. In addition, the artificial surface area proportion for each urban grid was calculated and used to analyze the performance differences within the urban areas.
# Transitional SAX Representation for Knowledge Discovery for Time Series
## 4 Experiments
### Experimental Results  
The overall classification results for the testing datasets are listed in Table 2, where the lowest classification error is highlighted. Clearly, SAX-TM has the lowest error in most of the datasets (14/20), followed by the SAX-SD (6/20). On average, the classification error for SAX-TM is lower than half of that for the original SAX in 19 datasets among the 20 datasets. The number of sub value spaces, i.e., the dimensionality reduction ratio, \(\alpha\), for SAX-TM is smaller than those for the others except SAX-SD. Figure 2 shows comparisons of SAX-TM with ED, SAX, SAX-TD, and SAX-SD, respectively, in terms of error rates of 1 NN classification. Figure 3a depicts changes of \(n\) parameters  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Name** & **\# of Classes** & **Size of Training Set** & **Size of Test Set** &
\begin{tabular}{c} **Length of** \\ **Time Series** \\ \end{tabular} \\ \hline
1 & Synthetic\_control & 6 & 300 & 300 & 60 \\
2 & GunPoint & 2 & 50 & 150 & 150 \\
3 & CBF & 3 & 30 & 900 & 128 \\
4 & FaceAll & 14 & 560 & 1690 & 131 \\
5 & OSULeaf & 6 & 200 & 242 & 427 \\
6 & SwedishLeaf & 15 & 500 & 625 & 128 \\
7 & 50 Words & 50 & 450 & 455 & 270 \\
8 & Trace & 4 & 100 & 100 & 275 \\
9 & TwoPatterns & 4 & 1000 & 4000 & 128 \\
10 & Wafer & 2 & 1000 & 6164 & 152 \\
11 & FaceFour & 4 & 24 & 88 & 350 \\
12 & Lighting2 & 2 & 60 & 61 & 637 \\
13 & Lighting7 & 7 & 70 & 73 & 319 \\
14 & ECG & 2 & 100 & 100 & 96 \\
15 & Adiac & 37 & 390 & 391 & 176 \\
16 & Yoga & 2 & 300 & 3000 & 426 \\
17 & Fish & 7 & 175 & 175 & 463 \\
18 & Beef & 5 & 30 & 30 & 470 \\
19 & Coffee & 2 & 28 & 28 & 286 \\
20 & OliveOil & 4 & 30 & 30 & 570 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The description of twenty UCR datasets.  
among SAX, SAX-SD, SAX-TD, and SAX-TM, and Figure 2(b) illustrates changes of parameter \(\alpha\) among the comparative algorithms. In addition to the classification performance, we present the computation time of the proposed method in comparison with the original SAX. The comparison bears significance that SAX-TM requires a memory of size \(n(1+\alpha^{2})\) for symbols, whereas the original SAX requires that of size \(n\), as mentioned in Section 3.1. For this comparison, we used three datasets (Lighting2, SpaceShuttle, ECG2) of lengths 637, 5000, and 21, 600; see the results in Table 3. The environment for the comparison was Matlab R2020b and Intel(R) Xeon(R) Platinum 8259 CL CPU @ 2.50 GHz with \(\alpha=10\). We observed that SAX is quite a lot faster than SAX-TM, which is reasonable since SAX-TM requires additional point-wise testing and storage for sub value spaces. The computation times for both methods, increasing according to the dataset length, are reasonably fast. Noticeably, the speed of SAX-TM is relatively robust against \(n\); SAX becomes quite much slow when \(n\) changes from 128 to 256 and SAX-TM hardly changes in speed; the standard deviation of SAX-TM is smaller than that of SAX in SpaceShuttle and ECG2.