esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 3 Proposed Methodology
### Change Metric  
Due to the continuous evolution of Linked Data, the local views become outdated. We proposed a change metric that quantifies the evolution of Linked Data. The main benefit of using the change metric is to alleviate the expensive job of copying the whole data instead of only updating local views with the changed items. We utilized the Dynamic Characteristic Set (DCS), which identifies the addition and deletion of the items in the Linked Data cloud. Typically, queries issued by the end-user are repetitive and follow similar patterns that only differ in a specific element. Therefore, we use a bottom-up matching approach to find similar queries. For the structurally similar queries, we first compute the distance between the patterns, then the results of these queries are placed in a cache for future access. To improve the efficiency of our system, we schedule the view maintenance as a low priority job that combines the multiple tasks into one larger job and executes it when the system is in idle state.  
We propose a change metric to quantify the evolution of the Linked Data Cloud. The change metric utilizes the Dynamic Characteristics Sets (DCS) [33], a scheme abstraction that classifies the Linked Data on the basis of the properties of _subjects_ and _objects_. Moreover, DCS captures the inherent structure and relationships of the Linked Data cloud. The DCS is a combination of properties and types that are used to describe the content in Linked Data. A change at any level of Linked Data implies a change in the mapping of the _properties_ and _type_.  
Let \(X_{t}\) represent the Linked data set captured at time \(t\), which consist of subject \(s\), predicate \(p\) and object \(o\), where \(P\) is the set of the properties in \(X_{t}\) and \(T\) are its type set. We define the DCS as a schema abstraction based on the \(s\) and \(p\). Any change in the DCS shows new content in the Linked data has been added or removed.  
**Example 1**.: _To explain the changes in the Linked Open Data (LOD) cloud, consider the snapshots of LOD captures at time \(t\) as shown in Table 1. The changes are illustrated by \(DCS_{1}\), \(DCS_{2}\), \(DCS_{2a}\) and \(DCS_{2b}\). \(DCS_{1}\) = {foaf:Person, fof: knows} captured at \(X_{t_{1}}\) and \(DCS_{1}\) = {foaf:Person, fof: knows} captured at \(X_{t_{2}}\) remain unchanged across snapshots. From the later version of the snapshot \(DCS_{2}\) was deleted and new combinations of \(DCS_{2a}\) and \(DCS_{2b}\) were observed. Thus, \(DCS_{2}\) is no longer used in the LOD._  
Figure 2: Overall architecture of the proposed method.
## 3 Results and Analysis
### Model Verification  
The model focus on expressing the weakening effect of the downstream deviation when the boundary layer of the incoming flow is thick. Therefore, the following verification was carried out for the positive incidence range when the deviations were on the suction surface. On this condition, the boundary layer is thick in the deviation positions. The coupled effect model was verified in the case discussed above firstly. Figure 7 shows the comparison between the performance calculated by the coupled effect model and the performance of the coexisting condition, where the value of k is 0.8074 determined by the least square method. It can be seen that the coincidence of the two results is very high, which means the coupled effect model accurately predicts the positive incidence range of the coexisting condition.  
To verify the accuracy of the coupled effect model further, the aerodynamic performances of the blade with the geometric deviations in the \(-0.3\) position and the \(-0.5\) position were analyzed. The cost of massive high-precision numerical simulations is huge. To reduce the computational cost, the Gaussian Process Regression (GPR) was used to make a surrogate model. In addition, the results obtained by the coupled effect model were compared with the results obtained by the high-precision surrogate model to verify its accuracy. The research of [PERSON] et al. [17] shows that the GPR model can provide enough flexibility for modelling the nonlinear functions. Figure 8a shows the sample points obtained by one-factor-at-a-time method of DOE. Figure 8b shows the accuracy of the surrogate model by using the leave-one-out method. The regression coefficient for a linear model fit to the leave-one-out results is R\({}^{2}\) > 0.99. Therefore, it can be considered that the surrogate model obtained by the GPR method is accurate, thereby the accuracy of the benchmark to verify the coupled effect model is ensured.  
Figure 7: Comparison between the positive incidence range calculated by the coupled effect model and the positive incidence range of the coexisting condition.  
Figure 9 compares the positive incidence range obtained by different method. It can be seen that the dotted line in Figure 9b is more consistent with the solid line than the dotted line in Figure 9a. So the coupled effect model can better predicts the positive incidence range than the sum of the separated condition. Therefore, the model has sufficient accuracy to be used in the performance evaluation of the blade with two-position deviation.
## 2 Cardiovascular Disease
### Coronary Artery Disease
#### 2.1.2 Atherosclerosis  
The term \"atherosclerosis\" was first introduced by [PERSON] in 1904 [29]. In 1908, [PERSON] illustrated the relationship between a cholesterol-rich diet and experimental atherosclerosis [30]. The chemist [PERSON]'s work demonstrated that atherosclerotic lesions contain significantly more cholesterol compared to healthy arteries [31; 32]. [PERSON] demonstrated that cholesterol could directly develop atherosclerosis in rabbits, thereby creating the first in vivo model [33]. In 1999, [PERSON] (1929-1999) introduced the response-to-injury hypothesis, linking endothelial damage to the onset of atherosclerosis, providing a deeper understanding of vascular disease [34; 35].  
Atherosclerosis is described as the hardening of coronary arteries caused by the build-up of cholesterol and fat particles in the inner wall, called atherosclerotic lesion. The mechanism by which atherosclerosis plaque starts to form, as shown in Figure 2, occurs within the intimal layer. Low-density lipoprotein (LDL) particles, termed bad cholesterol, accumulate at and then enter the injured intima [36]. Endothelial cells express adhesion molecules (chemoattractant cytokines, or chemokines) to recruit monocytes from the bloodstream. Chemokines enable monocytes to migrate into intima where they become mature and differentiate into macrophages. Subsequently, macrophages express scavenger receptors that allow them to engulf oxidised LDL particles and transform into foam cells [37]. T lymphocytes also migrate into the intima, where they regulate the activities of immune cells along with endothelial and smooth muscle cells. To respond to the injury, the secretion of growth factors and cytokines by platelets and other cells (e.g., platelet-derived growth factor, termed PDGF) switches VSMCs contractile phenotype to the proliferative and migratory phenotype [38; 39]. Subsequently, VSMCs proliferate and migrate from the tunica media into the intima [40]. VSMCs aggregate at the intima forming a fibrous cap around the injury to keep the potency of the lumen. This fibrous cap is vulnerable to rupture and thereby to inducing acute cardiovascular events [41].
## 3 Results and Discussion
### Mechanical Behaviour
In fact, the SWF+SWP+FBA mortar presented a large increment of flexural strength when compared to the REF mortar. This large increment was mainly due to the SWP waste [6]. Nevertheless, comparing the SWP+FBA and SWF+SWP+FBA mortars, it was concluded that when SWF was added in the ternary mix of wastes the strengths improved when compared with SWP+FBA. So, the SWF waste was also responsible for the increment of strength. It is concluded that a good symbiosis between the wastes was achieved, improving the mechanical strength of the mortar. In the compressive strength test (results presented in Table 3 and Figure 4), it was found that the SWF+SWP+FBA mortar presented a strength that varied between 7.9 and 9.2 MPa, at 28 days. These values are considerably higher than the ones of the REF mortar (between 3.6 and 5.9 MPa). In this test, it is also clear that the waste that was responsible for this increment of strength was SWP. Contrary to flexural strength, the addition of SWF to the SWP+FBA mortar was not favourable for compressive strength, since it presented slightly higher values for SWF+FBA than the SWF+SWP+FBA mortar in most curing ages. The angular particles of SW were more favourable in flexural strength than in compressive strength. In the compressive strength test results, a slight decrease over time was noticed. This had already been noticed in the SWP mortar and micro-cracking had been identified as the reason. Susceptibility to micro-cracking seems to also affect the SWF+SWP+FBA mortar. Micro-cracking is also responsible for the results obtained in the modulus of elasticity test, water absorption and water drying: a lower modulus of elasticity, a higher absorption and a faster drying behaviour over time were noticed. However, the reduction of strength was slight and is not comparable with the one  
Figure 3: Flexural strength test results. presented by the SWP mortar, meaning that the SWF and the FBA wastes decreased this susceptibility; although they were not able to completely eliminate it, they were responsible for a minimization of this disadvantage of the mortar.
## 3 Principals of Routing Protocols and Their Breakdowns
### Data-Based Routing Protocols
#### 3.4.9 iDFR [73]  
For UWSNs, a directional flooding-based routing technique (DFR) has been suggested. The DFR employs a link-quality-based packet flooding method. However, DFR fails to deal with dynamic changes throughout the interactions due to fixed system characteristics. Intelligent directional flooding routing (iDFR) protocols are suggested to help DFR with a variety of novel QoS measures, including the holding time method. iDFR has two protocols: QoSAware DFR with angle adaption (QA DFR AA) and QoSAware DFR with threshold adaption (QA DFR TA). By changing the base angle, the former adjusts the flooding area for the entire path--the latter changes the flooding region by updating a threshold value.  
* Comparison  
Here, all the protocols have a significant flow; none have a void avoidance mechanism, which is a major drawback. From Table 18, we can see that most protocols have a high end-to-end delay, but only UHRP produces a high delivery rate. The network cost of the protocols is high except for DD, and the energy consumption of these protocols is also high.  
Table 19 illustrates the key idea, advantages and disadvantages of the protocols described.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & **Void** & **End-to-End** & **Simulation** & \multirow{2}{*}{**No. of Sink**} & **Delivery** & **Network** & **Energy Consumption** \\  & **Avoidance** & **Delay** & **Tool** & & **Rate** & **Cost** & **sumption** \\ \hline UHRP & No & High & NS2 & Single & High & Medium & - \\ RACAA & No & High & Matlab & Single & - & Medium & High \\ L2-ABF & No & Low & NS2 & Multiple & Medium & Medium & High \\ iDFR & No & High & NS2 & Multiple & Medium & Medium & High \\ DD & No & Low & NS2 & Single & Medium & Low & Very high \\ \hline \hline \end{tabular}
\end{table}
Table 18: Comparison of direction aware routing protocol.
## 4 Methodological Approach
### Study Site 1
#### 4.1.3 Hyperspectral Burned Scar Detection Assessment  
Hyperspectral sensors proved to be efficient in burned area analysis, mapping the diffusion of ashes, coal and soil oxidation [81]. The PRISMA sensor used represents novelty and innovation due to its high spectral resolution. Nevertheless, in this study, the advantages this characteristic would have provide were almost nullified because of the time elapsed between the fire and the acquisition. Indeed, PRISMA was deployed on March 2019 while the fire occurred in June 2017. This delay allowed a significant scene change in comparison to the immediately post fire condition, creating a strong phase mixing within the pixel. The first PRISMA image over the AoI was acquired two years after the event and coincides with the last Sentinel-2 image. The AoI image was processed via EnMap Box, a QGIS plugin developed by the German Aereospatial agency in collaboration with Humboldt Universitat of Berlin [78]. The tool is capable of opening PRISMA HDF5 files  
Figure 4: Area of interest (AoI) for biophysical parameter computation compared to Sentinel-2 granule dimension. In transparency is possible to observe the fire affected area. Image acquired on 4 July 2017.  
with the correlated ancillary files necessary for further processing. The PRISMA image was opened in the software and coregistered with Sentinel-2 burn severity RBR June-July 2017 product, used as a fire severity reference map.  
This allowed a PRISMA feature spectral library to be created based on the acquisition of crosshair pointed ground pixel spectral signatures, classified with the reference of the 2017 Sentinel-2 burn severity map. This process allowed a classified spectral library to be created, associating a burn severity value of the area from the 2017 RBR map to a spectral signature of a 2019 PRISMA image (Figure 5). One hundred pixel-spectral signatures were acquired. The obtained classified spectral library was used as reference for the classification of the whole PRISMA scene via the EnMap toolbox, based on a random tree forest model. The sensor 237 spectral bands constitute a wide range of possible pixel spectral signatures, therefore requiring a remarkable computational power to perform the classification and definition of each pixel inference class.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Update the Q-values  
By implementing the Bellman equation, the system updates the Q-value for the current (state, action) pair:  
\[Q(S,\ A)=Q\ (S,A)+\alpha\ \big{[}R+\gamma\ max_{A^{\prime}}Q\big{(}S^{\prime}, \ A^{\prime}\big{)}-Q(S,\ A)\big{]} \tag{3}\]  
where  
* \(Q(S,\ A)\) represents the expected reward for action \(A\) in state \(S\).
* \(A\) represents the learning rate (adjusts the influence of new information), _given_\(0<\alpha\leq 1\).
* \(\gamma\) represents the discount factor (prioritizing immediate vs. future rewards), _given_\(0\leq\gamma<1\).
* \(A\) represents the immediate reward for the chosen action.
* \(max_{A^{\prime}}Q(S^{\prime},\ A^{\prime})\) represents the best future reward in the next state.  
This ensures that the system learns from previous experiences and continuously improves.
### Motivational Statement
#### 1.1.2 Edge Intelligence  
With the proliferation of IoT, data-driven AI solutions are applied for (1) handling the enormous data features and heterogeneous IoT taxonomies, (2) analyzing the underlying hidden patterns of structured, semi-structured, quasi-structured, and unstructured data, and (3) making reliable decision outcome in terms of classifications, predictions, and recommendations [17; 18]. To make this solution sufficient in real-world applications, the development process has to consider computational resource placement, leading to the confluence of AI with adequate cloud computing and EC capacities, known as cloud intelligence (CI) and EI, respectively [19]. Figure 1 illustrates the overview between CI and EI, each of them consisting of three primary tiers: end devices (ED), edge, and cloud. In CI, model training and inference are performed in central cloud servers, which raised various challenging drawbacks while gathering the local data, including backhaul congestion, high latency, privacy leakage, and insufficient bandwidth consumption.  
EC brings solutions for traditional cloud computing by allowing IoT nodes to offload the tasks wholly/partially toward computing entities in close proximity, effectively alleviating the backbone congestion, lowering the computing delay, and saving local IoT power utilization [20]. Based on different service deployments, EC servers are attached to particular access points (APs) such as base stations, wireless APs, routers, etc. A micro-data center (MDC) at the edge assists the applicability of EI, predominantly for smart cities. The model can be trained and evaluated for clustered ED and services with specific local context awareness by leveraging sufficient computing resources. With EC-enabled collaboration, the advancement of communication, computation, control, and caching (4C) is jointly optimized in edge networks [21; 22]. EI tackles these issues by integrating DL and EC to enable efficient edge learning in distributed networks [23; 24; 25].  
From real-time communication perspectives, integrated DL requires a processing time of critical upper-bound tolerable delay in the execution procedure between input, hidden, and output layers. Within DL modeling, deep neural networks (DNN) are a prominent class of several popular DL algorithms such as convolutional neural networks, artificial neural networks, multi-layer perceptron, and recurrent neural networks, which necessitates minimizing the abundance of execution time. With adequate support from EC, partial DL model training and inference are executable in edge networks to support practical collaborative learning systems [26].
## 2 Preliminaries
### Fruit Fly Optimization Algorithm  
The fruit fly optimization algorithm is inspired by the food-searching mechanism of a fruit fly swarm due to the fact that the fruit fly is superior to other species in vision and ophresis. The fruit flies in a swarm send and receive information from neighbors, compare the current location and fitness, and fly toward the food with better fitness. The global optimization can be obtained via an appropriate iteration, as shown in Figure 1, and the standard foraging process of FOA can be summarized as follows.  
Step 1.1: Key initial parameters, population size _PS_, maximum generation number (maximum iterations) \(iter_{\max}\), and location range parameter \(LR\) are generated. The initial location of the fruit fly swarm is represented as follows:  
\[\left\{\begin{array}{l}X\_axis=rand(LR)\\ Y\_axis=rand(LR)\end{array}\right. \tag{7}\]  
Figure 1: Food searching process of a fruit fly swarm. Dist \(i\) indicates distance to the original coordinates, and smell \(i\) indicates the smell concentration.  
Step 1.2: Individual fruit fly uses osphesis to search food, and the searching direction is randomly generated as follows:  
\[\left\{\begin{array}{l}X(i)=X\_axis+rand(FR)\\ Y(i)=Y\_axis+rand(FR)\end{array}\right. \tag{8}\]  
where \(FR\) is the fruit fly distance range.  
Step 1.3: Since the food location cannot be known, the distance to the origin of coordinates \(Dist_{i}\) and the smell concentration judgment value \(S_{i}\) are calculated as follows:  
\[\begin{split} Dist_{i}=\sqrt{X_{i}^{2}+Y_{i}^{2}}\\ S_{i}=\
icefrac{{1}}{{Dist_{i}}}\end{split} \tag{9}\]  
Step 1.4: The best fruit fly with maximum smell concentration searches for food according to the smell concentration judgment function (also known as fitness function), which is represented as follows:  
\[\begin{split} Small(i)=function(S_{i})\\ [bestSmell,bestIndex]=\max(Smell)\end{split} \tag{10}\]  
where \(bestSmell\) denotes the maximal smell concentration, \(bestIndex\) is the corresponding fruit fly number, and \(Smell\) is the smell concentration set of the group.  
Step 1.5: Compare the smell concentration with the previous best smell concentration; if it is better than the last generation, update the current best location as follows:  
\[\begin{split} SmallBest=bestSmell\\ \left\{\begin{array}{l}X\_axis=X(bestIndex)\\ Y\_axis=Y(bestIndex)\end{array}\right.\end{split} \tag{11}\]  
Otherwise, go back to Step 1.2 to find other better smell concentrations.  
Step 1.6: If the searching process reaches the maximum generation number, the procedure will be terminated, otherwise, repeat Step 1.2 to Step 1.5. This optimization algorithm is very efficient to reach the global optimization, but it is also easy to fall into a local optimization.
## 4 Results and Analysis
### Compare of Heat Fluxes Calculated Using Different LST Datasets
#### 4.3.1 Validation of the DisTrad-LST and GWR-LST Heat Fluxes  
Table 5 provides the in situ validation results of H and LE calculated using the DisTrad downscaled LSTs and the GWR downscaled LSTs. Compared to the validation results of the MODIS-LST heat fluxes in Table 4 and Figure 8, the DisTrad-LST heat fluxes display better retrieval accuracy than the GWR-LST heat flux, and the GWR-LST heat fluxes are generally better than the MODIS-LST heat fluxes. Also, the GWR-LST heat fluxes and the DisTrad-LST heat fluxes are both better than the MODIS-LST heat fluxes on all of the days examined; moreover, the MBE and RMSE values decreased and the \(R^{2}\) values increased on most days. Using the data collected on 19 June as an example, the spatial distributions of H and LE calculated using the GWR-LST and DisTrad-LST are shown in Figure 9. Based on Figure 9, Figure 7c,d, the results obtained using the GWR-LSTs and DisTrad-LSTs are synoptically smoother than the MODIS-LST results; no \"boxy\" anomalies like those shown in Figure 7c,d are present. Because the GWR-LST heat fluxes and DisTrad-LST heat fluxes consider the heterogeneity of surface temperatures that could be a significant source of error in the LE models, the differences among the three heat fluxes result mainly from the differences between the sharpened LSTs and resampled LSTs of the subpixels at a resolution of 30 m. For example, the boundary between the oasis and uncultivated land becomes a belt of intermediate H and LE values because these mixed pixels include uncultivated land and vegetation. However, the temperatures of the mixed pixels in the coarse-resolution image (which shows the MODIS LSTs) are replaced by a single value. This result neglects the significant differences in the subpixel temperatures. Figure 8: Scatterplots showing the MODIS-LST heat fluxes. Inhomogeneity is an inherent property of the Earth's surface parameters. Because of its complexity, this heterogeneity can seriously affect the accurate acquisition of land surface heat fluxes, and it is one of the main factors that cause various surface parameters to change with scale [65,66]. Landscape inhomogeneity can be classified using two conditions: nonlinear vegetation density variations between subpixels (e.g., different types of vegetation mixed with each other) and coarse pixels containing different landscapes (e.g., vegetation or bare soil mixed with areas covered by buildings or water). To evaluate the effects of LST, stations EC04, EC15, and EC11, which display a typical severely heterogeneous surface, a uniform surface, and a weakly heterogeneous surface, respectively, were selected to analyze the heat fluxes calculated using the results of different LST downscaling methods. EC04 is used as an example because its land cover displays a complex pattern. Table 6 compares the