esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Correlation Between Shapley Values for Models with the Same Architecture  
In this experiment, we calculate the correlation between Shapley values when the partition from which the models are re-trained changes but the neural network architecture remains the same.  
In this case, the correlation was calculated between the results of each possible pair of partitions, per architecture. For each pair of partitions, a correlation value was obtained between two vectors of as many elements as test images and Shapley values per image. We therefore have \(\binom{5}{2}=10\) correlation values per architecture.  
The results obtained are shown in Figure 6, where each box-and-whiskers plot has been calculated from the correlation values between the results for each pair of partitions per network architecture.  
Figure 5: Boxplots of the correlation coefficients between the Shapley values of the random and re-trained models. There is one diagram per network architecture. Each diagram has been obtained from the 5 correlation values corresponding to each partition by architecture.  
Figure 6: Boxplots of the correlation coefficients between the different pairs of partitions by architecture for the Shapley values.  
All medians are above 0.65, which seems to indicate that, regardless of the architecture considered, the models are not very sensitive to the change of the training and validation partitions.
## 2 Numerical Method  
### Physical Model  
Figure 1 shows a 3D physical model of a water droplet impacting on a surface with micro-pillars. The whole calculation domain is rectangular with a size of \(130~{}\upmu\text{m}\times 110~{}\upmu\text{m}\times 65~{}\upmu\text{m}\). The bottom of the microstructure and the surface of the microstructure are considered as smooth-wall surfaces and they are set as wall boundary conditions. Wall adhesion is realized by the values of the surface tension and contact angle. The perimeter and top of the computational domain are pressure outlets. An array of micro-pillars is arranged above the bottom wall in 7 rows and 14 columns. The diameter \(d_{p}\) of each column is \(3.2~{}\upmu\text{m}\) and the height \(H\) is relatively high at \(30~{}\upmu\text{m}\) to capture the behavior of the droplets in the microcolumn. The spacing between two pillars is fixed at \(9~{}\upmu\text{m}\), which is denoted as \(d_{x}\). To save the computational time, a droplet is located tangentially on the top of the micro-pillar arrays at the initial state with a diameter \(D_{0}\) of \(43.7~{}\upmu\text{m}\). Furthermore, according to [PERSON] et al. [46], the surface tension \(\gamma\) and contact angle are set to \(0.0683\) N m\({}^{-1}\) and \(110^{\circ}\), respectively, with a droplet viscosity of \(1.003\times 10^{-3}\) Pa s and a density of \(998\) kg m\({}^{-3}\). At the microscopic level, the droplet interacts with the surface and makes it exhibit superhydrophobicity due to the presence of microstructures.  
Figure 2 illustrates the definitions of the parameters discussed in this study. For convenient comparison, a series of dimensionless quantities is introduced. The Weber number (_We_) is usually used to describe the relative importance of inertial forces to surface tension in fluid dynamics. In this study, the characteristic velocity in Weber number is replaced by the initial falling velocity \(v_{n}\) and denoted as \(We_{n}\) to describe the droplet, i.e., \(We_{n}=\rho v_{n}{}^{2}D_{0}/\sigma\), varying from \(0.04\) to \(9\), where \(\rho\) is the fluid density, \(D_{0}\) is the initial diameter of droplet, and \(\sigma\) is the droplet surface tension. Furthermore, another transverse Weber number \(We_{t}\) is defined for moving surface, i.e., \(We_{t}=\rho v_{t}{}^{2}D_{0}/\sigma\), varying from \(0\) to \(6.4\). Here, \(v_{t}\) is the horizontal velocity of the micro-pillar surface. Moreover, \(H_{d}\) represents the penetration depth of the droplet. Additionally, the droplet is divided into upstream  
and downstream sections for subsequent analysis, according to the movement direction of surfaces.
# A Presentation of Ionic Liquids as Lubricants: Some Critical Comments
## 2 Why Should ILs Be Used as Lubricants?
It must, however, be pointed out that this is just one of the \"green\" uses of IL in addition to their use in organic and inorganic reactions as green solvents, where their facile recovery from the reaction media allows their recycling for successive reactions [22; 23; 24; 25; 26; 27]. All these, are applications which are triggered by ever-increasing attention towards environmental concerns. Moreover, the thermal stability of ILs up to 300-400 \({}^{\circ}\)C prevents problems concerning the decomposition of synthetic lubricants. As expected, the combination of anions, cations, alkyl chains, and the length of side chains dramatically influences the lubricant performance of ILs. Anions as [BF\({}_{4}\)]\({}^{-}\) and [PF\({}_{6}\)]\({}^{-}\) are the most employed, generally for their affordable costs and easily availability [28]. However, as will be discussed in the next sections, the anion structure can be changed, for example, with organic anions such as dialkylphosphate [DAP], dicyanamide [DCN], and tricyanomethane [TCC] so that the presence of fluorine-based anions (promoting undesired corrosion reactions in the presence of water) can be avoided. In any case, lubricating noncorrosion-resistant materials can be made by ILs with other organic anions containing fluorine (i.e., trifluormethanesulfonate [TfO], bis(trifluoromethylsulfonyl)imide [NTF2], and perfluoroalkylphosphate [FAP] anions) [29]. Pyrrolidinium, pyridinium, and imidazolium cations affect the viscosity of ionic liquids, under the same anions and lateral chains, which is a property crucial for the thickness of lubricants [30]. For instance, the combination of imidazolium and phosphate groups may increase the lubricating performance of ILs, significantly reducing friction and wear challenges. Moreover, the length of the side alkyl chain is very determinant for the tribological properties of ILs due to the different alignment of ionic liquids with short or long alkyl chains under an electrical field [31]. As it can be deduced from the above introduction, the interest in ILs as lubricants must be addressed, in our opinion, to their peculiar chemical nature which provides a complex behaviour. This causes, in addition to the bare increased viscosity usually exploited in lubrication, further emerging properties to arise which are of added value (see Figure 2) and which cannot be found in conventional lubricants.
# Social-Aware-Based Resource Allocation for NOMA-Enhanced D2D Communications
## 3 Social-Aware-Based Resource Allocation
### Optimization Model
#### 3.2.2 Power Control
In each iteration, the relevant parameters of each D2D content provider are calculated. Therefore, the complexity of SCA is \(O(T_{1}|D|)\). The complexity of GA mainly depends on the complexity of the fitness function \(O(f)\). Assume that population size is \(Q\), the maximum number of iterations is \(T_{2}\), and the complexity of GA is \(O(T_{2}QO(f))\). Therefore, the total computational complexity is \(O(T_{1}|D|+T_{2}QO(f))\).
## 3 Methodology
### Knowledge Extraction
#### 3.2.2 Triplet Preprocessing  
The next stage is to apply more preprocessing to all the components of a triplet as shown in Figure 1. Our preprocessing consists of three steps: (1) abstraction of triplet action, (2) reducing subjects and objects to their dictionary base, and (3) determining the noun form of subjects and objects. To represent the information in each document, we create a knowledge map in which nouns are nodes and verbs are edges. However, it is necessary to simplify the map by abstracting the relationship edges formed from verbs. The abstraction of a triplet action is performed using the WordNet lexical classes [24]. WordNet organizes concepts into forty-five lexicographer files based on syntactic category and logical groupings of which 15 apply to verbs. For example, the action in the triplet \"City establish initiative\" describes a creation activity and hence action type has a \"creation\" class. On the other hand, the action in the \"city understand solution\" triplet describes a cognition relationship. \begin{table}
\begin{tabular}{c c c c} \hline
**Cnt** & **Subject** & **Action** & **Object** \\ \hline
1.1 & city & establish & initiative \\
1.2 & initiative & relate to & year \\
2.1 & city & understand & solution \\
2.2 & solutions & relate to & technology \\
2.3 & city & implement & solution \\
3.2 & understand & has property & better \\
3.3 & solution & has property & smart \\
3.2 & implements & has property & better \\
4 & city & establish & initiative \\
7 & solution & improve & delivery \\
8 & delivery & relate to & city \\
9 & delivery & relate to & service \\ \hline \end{tabular}
\end{table} 
Table 2: Example triplets generated after processing the dependency tree using the Stanford CoreNLP package. Duplicates were merged. Figure 2: Dependency tree for terms in the triplet “City better understand smart technology solutions”. Next, we transform each subject and object to its dictionary noun form. For this we use the Aspell Automatically Generated Inflection Database (AGID) [37]. AGID is part of the Spell Checker Oriented Word Lists (SCOWL) project that is intended to be suitable for use in spell checkers [37]. Using this database, we can remove all the inflections from subjects and objects. For example, by removing the inflection from the subject \"understands\" will result in \"understand\". We further notice that this subject is a verb and not a noun so we will further convert it to its noun form \"understanding\". The result of this preprocessing is shown in Table 3.
## 6 Conclusions  
This work has presented the production and analysis of a real-life environmental audio database in two urban and suburban scenarios corresponding to the pilot areas of the DYNAMAP project: the Milan district 9 and the Rome A90 highway, respectively. The collected 9 h 8 min of audio data have been categorized as road traffic noise, background city noise, and anomalous noise events. Due to the relevance of the ANE for the generation of reliable traffic noise maps through WASN, that category has been subsequently annotated using 19 subcategories and automatically labelled in terms of contextual acoustic salience as signal-to-noise ratio.  
The in-depth analysis of the distribution of the ANEs collected in the database has shown the high diversity of ANE in terms of occurrence, duration and salience, besides confirming their local and occasional nature. Although, as expected beforehand, specific differences have been found between both soundscapes due to their specific acoustic nature, both environments show high ANE diversity, besides presenting a dramatically large range and variation of SNR values when compared to previous artificially generated ANE audio databases. The obtained results have been also comprehensively discussed to guide the development of automatic ANE classifiers using real-life environmental data in future works, by accounting for the implications of this kind of peculiar acoustic data.  
Finally, we do not discard extending the conducted analyses in the considered urban and suburban soundscapes along with the DYNAMAP's WASN deployment in order to include a larger diversity of weather and traffic conditions, compare the obtained results with the final location of the low-cost sensors in the urban environment, etc., besides studying in more detail those audio clips tagged as 'complex audio'. Moreover, we also plan to analyze the statistical differences between ANE and road traffic or background noise at the spectral level for the future development of anomalous noise event detection systems in both scenarios.  
This research has been partially funded by the European Commission under project LIFE DYNAMAP LIFE13 ENV/IT/001254 and the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement (Generalitat de Catalunya) under grant ref. 2014-SGR-0590. We would like to thank our colleagues in ANAS S.p.A., Bluewave and Universita di Milano-Bicocca for their support during the recording campaign.  
The authors contributed equally to this work. [PERSON] led the initiative of preparing and writing the manuscript, also defining the focus of the analysis process, while [PERSON] led the production and analysis of the environmental audio database, besides to also contributing to the writing of the paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANE & Anomalous Noise Event \\ ANED & Anomalous Noise Event Detection \\ BCK & Background noise \\ DYNAMAP & Dynamic Noise Mapping project \\ GTCC & Gammatone Cepstral Coefficients \\ RTN & Road Traffic Noise \\ SNR & Signal-to-Noise ratio \\ \end{tabular}
## 3 BIM and DTs--Latest Development Trends and Challenges
### Implementation of IoT Paradigm and Data-Based Solutions
Furthermore, AI can be employed in the analysis of complex BIM data, suggesting changes to improve performance such as reducing energy consumption. In addition, it generates photorealistic images and 3D videos, thereby facilitating the visualization of a project [91; 92; 93]. One of the advantages of AI in BIM is that it costs less to detect errors early and optimize projects at each stage, generating savings. Such technologies facilitate creativity by giving more time to people. And at the same time, they help to create better buildings and aesthetic designs, supporting sustainable development. The AI trend in BIM is still in its nascent stages of development, yet its potential is considerable. It is advisable to monitor the development of this technology in order to gain a competitive advantage and to undertake projects at a completely new level [57; 94; 95]. BIM and DTs as Tools to Support BACS Design and Management Processes--Opportunities, Challenges, and Research Directions  
Considering the original contributions defined in Section 1 and the trends and challenges discussed in Section 3, the authors of this review propose expanding the concept of utilizing BIM and DT methods and tools in the domain of the design, construction, and operation of buildings. In particular, they should consider two main development trends in building innovations. The first is the increasing smartness and comfort of use, and the second is the improvement of energy efficiency, the utilization of RES, and the readiness for smart grid solutions. In light of the information presented in [26; 27; 69], it is evident that an important element of this concept is the required integration of the digital and physical layers. Therefore, the authors of this paper propose cloud services as an integration platform also for the implementation of BIM and DT services, as shown in Figure 4.  
In the context of designing BACS and BMS functions based on fieldbus and IoT network modules, it is essential to refer in BIM to functional blocks and network variables related to specific modules of sensors, actors, and available cloud services within the BIM framework. This enables the implementation of more advanced functions and data analytics. Consequently, at the stage of construction and operation of these systems within the building, hardware and software integration is necessary, allowing the acquisition and use of data from these modules in DT models and mechanisms. This enables the development of optimal operating parameters for building infrastructure devices and the effective implementation of predictive models. At the same time, such DT modelscan facilitate mechanisms for enhancing the energy efficiency of a building thanks to the selection of parameters of the BACS and TBM functions, not only during the design phase but also during the period of dynamic operational changes.
# Realistic Simulation for Body Area and Body-To-Body Networks
## 3 Proposed Simulator
### Semi-Deterministic (Stochastic) Radio Link and Mobility Modeling  
In this subsection, we briefly describe the implementation of the BAN mobility, the BAN environment and the channel models used to realistically reproduce on-body intra-BAN communications and body-to-body channels to model the interference generated by one BAN on another. Channel models are based on an extensive measurement campaign carried out in a classical office environment at a frequency of 2.48 GHz and recorded with a four-port vector network analyzer VNA. The measurements included the presence of up to four people, three antenna on-body positions (head, belt and wrist) and multiple mobility scenarios. It has to be noted that channel realizations for each BAN are generated independently from the others.  
As shown in Figure 2, six main steps are considered to model the radio link between a transmitter and a receiver.  
The first step consists of extracting some important deterministic information (_i.e._, positions, direction, speeds) from the mobility model, at every simulation iteration. These information are treated by the BAN environment in the second step to define link conditions between the transmitter and the receiver thanks to the BAN and physical simulation module. The channel is modeled through three modules in the WSNet simulator: path loss (Step 3), shadowing (Step 4) and fading (Step 5).  
Figure 2: Radio link modeling approach.  
Link conditions are the input parameters of body-to-body and on-body channel models to select the configuration of each model and generate realistic traces. Depending on the considered link condition, the parameters of the propagation model are selected in a dynamic way during the simulation. Once the received signal strength is computed according to the considered propagation models and parameters, the propagation modules return the evaluated RSS to the WSNet simulation core, which, according to the current concurrent transmissions, will evaluate the corresponding SINR, BER and PER.  
An essential feature of our simulation is our semi-deterministic approach instead of the statistical approach. The mobility model provides for each realization some important deterministic information (_i.e._, positions, relative orientations, speeds, obstructions, _etc._), which are treated as input parameters by body-to-body and on-body channel models to generate realistic traces (_i.e._, with consistent mobility patterns) as a function of time. For example, on-body channels can adapt their models based on human walking according to the BAN speed. Body-to-body channels can dynamically take into account BAN and physical environment information (e.g., the mutual orientations, the (non-)line of sight conditions and the room to which the other BAN belongs) for interfering links.
## 3 Results and Discussion
### Production of Metabolites under Various Growth Conditions  
Proteins, carbohydrates, lipids, and pigments synthesized by microalgae are biomolecules with high added value. The use of these products makes the cultivation of algal strains commercially viable [59]. It is known that both qualitative and quantitative characteristics of light affect the metabolic composition of cells; therefore, an optimal light regimen not only increases the productivity of biomass but also increases its value [60]. In turn, the light regimen affects the efficiency of assimilation of inorganic carbon, which is converted into various compounds during photosynthesis [61; 62]. Microalgae synthesize various types of pigments, which are widely used in the food industry, pharmacology, and cosmetology, competing with synthetic ones [63]. In this study, the concentrations of pigments (chlorophyll \(a\), chlorophyll \(b\), and total carotenoids) were estimated during the cultivation of _Scenedesmacae_ strain EZ-B1 in the photobioreactor to estimate the photosynthetic potential of the culture (Table 2; Figure 4). Under different photoautotrophic growth conditions, the total chlorophyll and total carotenoid content of the cultures varied from 83.2 \(\pm\) 3 mg L\({}^{-1}\) to 102.3 \(\pm\) 4 mg L\({}^{-1}\) and from 16.0 \(\pm\) 0.6 mg L\({}^{-1}\) to 18.2 \(\pm\) 0.8 mg L\({}^{-1}\), respectively (at the end of cultivation). The maximum total pigment concentrations were achieved at 56.16 mol m\({}^{-2}\) day\({}^{-1}\) with a 16 h photoperiod. [PERSON] et al. [64] noted that with increasing PPFD (from 100 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) to 500 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\)), the concentration of total pigments in the cells of _Parachloerella kessleri_ decreased, but for two other algal species, an inverse relationship was observed. Thus, when _Chlamydomonas reinhardtii_ and _Desmodesumo quadicauda_ were cultivated at the maximum tested PPFD value, the concentrations of chlorophyll \(a\), chlorophyll \(b\), and total carotenoids were significantly higher. Increasing light intensity has been shown to be important for the accumulation of total carotenoids by _Dunaliella tertiolecta_ under all conditions tested and to have an uncertain effect on chlorophyll synthesis [34]. The maximum chlorophyll content of _Scenedesmus acatus_ in the study performed by [PERSON] and [PERSON] [31] was recorded for a 24 h photoperiod, but the chlorophyll content was reached more quickly under 18 h and 12 h photoperiods. [PERSON] et al. [65] studied the effect of CO\({}_{2}\) concentration (0.05-100%) on the synthesis of pigments and found that increasing the CO\({}_{2}\) concentration to a certain level promoted the accumulation of pigments in _T. obliquus_ culture. Also, the authors demonstrated that more than 30% CO\({}_{2}\) had an inhibitory effect on the photosynthesis of microalgae and significantly reduced the concentration of all pigments.