esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions and Future Studies  
In this study we proposed an interferometric measurement methodology of the morphological transformation of epithelial cell lines during the epithelial-mesenchymal transition process. The problem is a well suited application of the digital holographic microscopy method due to the importance of precise measurement of the observed sample's depth map. It is also an interesting problem from the perspective of cancer research which may help better understand the metastasis process. The results obtained in this study gives insights into the critical parameters that effect the EMT process. The results indicate that cells undergo a spherical morphology transition during this process which is attributed to the decrease in cells adhesive properties. The future studies on this application could lead to the development of a model for the EMT process and its effects on cell to cell interactions.  
Conceptualization, R.V., G.B.E. and H.U.; methodology, R.V. and G.B.E.; software, R.V.; validation, R.V. and G.B.E.; formal analysis, R.V. and G.B.E.; investigation, R.V.; resources, H.U.; data curation, R.V.; writing--original draft preparation, R.V.; writing--review and editing, G.B.E. and H.U.; visualization, R.V.; supervision, G.B.E. and H.U.; project administration, G.B.E. and H.U.; funding acquisition, H.U. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the Scientific and Technological Research Council of Turkey (TUBITAK) under project number 116E867.  
The authors declare no conflict of interest.  
Figure 3: Eccentricity, surface area, cell height, and cell volume parameters are calculated and summarized in these graphs. Samples treated with TGF-\(\beta\) are given at the top graphs and the control groups are given at the bottom graph. The labels A, B, C, and D labels respectively represent HCT-116, HUVEC, ONCO-DG1, and MDA-MB-231 cells before EMT and E, F, G, and H labels respectively represent HCT-116, HUVEC, ONCO-DG1, and MDA-MB-231 cells after EMT. For each cell line, the most notable morphological change was the decrease in the eccentricity and increase in cell height parameters. Also notable is the decrease in the variance in eccentricity and surface area values.
## 4 Results and Discussion
### Limitations  
Although the study was conducted successfully, some limitations must be considered. Firstly, although it is concluded with the mixed type of results from the experiment as they are both statistically significant and insignificant, fewer participants may hinder the generalizability of the study due to human heterogeneity. i.e., how someone performs when the given task uses a new technology, such as virtual reality, can be contingent on participants' acceptance of technology and how well they performed before. Although participants were given comprehensive pre-training sessions, varying learning abilities still exist. A participant's cognitive ability to find the design errors in the building model is different, leading to misinterpretation of the result. The questionnaire provided to participants to assess themselves may skew the results. The participants might interpret the questions differently and answer them according to their understanding which may also mislead the result. Future studies must ensure that there should be minimum human variation issues in the experiment, which will support the results from our analysis. Electroencephalography EEG measures the mental stress-inducing task and identifies optimal task allocation, workplace efficiency, and workspace safety to measure skin response during cognitively demanding activity. We subjectively measured the participants' CL, SA, and TP due to limited resources, as these can all be measured by the new advanced techniques such as electrodermal activity EDA. This research is also limited by the fact that it only uses Oculus Quest 2 VR handset. Other handsets with different resolution and refresh rate may produce different results. A future study investigating the effect of user performance for using different VR handsets technology with different resolution and refresh rate would be interesting. Despite the limitation mentioned earlier in this study, this research extends knowledge and understanding of the impact of VR and cognitive issues for design review tasks in the construction industry. Further research might explore more dimensions by considering a more realistic environment (increase more dimensions to a simulated environment such as hearing, feeling, smell, interaction with, and affect the surroundings) to measure SA.
## 3 Proposed GIS-Based Approach
### GIS-Based Input Data Preparation
#### 3.1.1 Power System Data  
The power system data considered are related to PS and SSs. Figure 7 shows the location of the substations: red circles are the PSs owned by Unareti; yellow circles are the PSs owned by a competitor DSO; in purple are, instead, MV substations supplied by a PS, thanks to dedicated power cables. The last type of substation has to be intended as an extension of the supplying PS in another closer location. The developing plan of Unareti [29] indicates the need for building new PSs to face the coming increase in load demand. Moreover, the developing plan already reports the expected number and location of several coming PSs. Therefore, in the optimization approach, the PSs owned by Unareti are taken as-is, MV substations are potentially convertible into PSs, and the PSs of the competitor DSOs are neglected.  
Regarding SSs, thanks to the information available in the Unareti database, the grid has been overlapped with a point layer containing the location and the yearly load profile of the current SSs. The resulting map, shown in the left image of Figure 8, pictures the distribution of the SSs in the urban area. To assign a power value to each of the squares, the QGIS function \"joint attributes by location\" has been used, intersecting the point layer of the SSs with the polygon layer of the grid. Considering the right picture in Figure 8, the yearly load profile of every single cluster results in summing up the load profiles of the points, the SSs, that fall in their area. It is assumed that, in the following years, the load related to the current SSs will be in the same location.  
Moreover, based on the analysis and assumption reported in the Unareti developing plan [29], the squares power demand has been updated to take into account the forecasted evolution of demand and generation in the next ten years, such as new residential areas, final consumption electrification, electric vehicles, and distributed generators deployment. It is worth noticing that recent projects for significant urban development could change the location of the new PSs, especially in the areas of Porta Romana and Milano Innovation District. The estimates of energy needs were ongoing at the time of modeling development and simulations, and, therefore, its impact is not included in the simulations input. Finally, the squares are colored based on their yearly peak power, resulting in the load map shown in Figure 9. The classification is made using a dedicated feature of QGIS, in the range  
Figure 7: Location of the PSs and MV substations feeding Milano and Rozzano. Red points are PSs owned by Unareti; Yellow points are PSs belonging to the competitor DSO; MV substations are in purple.  
of 100 kW-8 MW. The picture shows a higher load density in the city center than in the suburban areas.
# Performance Comparison of Machine Learning Disruption Predictors at JET
A common database of diagnostic signals from the ITER-like Wall (ILW) JET campaigns is selected to build the training and validation sets with which three different machine learning (ML) DP models have been built: an MLP-NN, a GTM manifold learning model [9], and a convolutional deep learning neural network (CNN) model [33]. The performance of the three models has been compared using the same test experiments and the same performance metrics, namely the recall, the specificity, the precision, the confusion matrix, the receiver operating characteristics (ROC), and the area under the ROC curve (AUC), which are the most used metrics in binary classification problems. However, in the disruption prediction literature, the accuracy, the warning time distribution, and the assertion time provide a more immediate reading of the predictor's result, and they have been provided in the paper. An accurate analysis of the results of the comparison between predictors has allowed the pros and cons of DP models to be highlighted and to learn, from all the efforts of recent years, which of them are the most promising models for future devices. The paper is organized into the following. Material and Methods in Section 2 reports, in Section 2.1, the background of the disruption prediction task. Section 2.2 reports the database of the JET diagnostics used to train, validate, and test the ML models, the feature engineering performed to extract meaningful input information compatible with the architectures of such models, and the labelling of the discharge samples to associate these inputs to consistent outputs. In Section 2.3, the descriptions of the well-known machine learning methods used to develop the different comparison methods are briefly summarized whereas, in Section 2.4, the performance metrics used to perform the comparison are recalled. Section 3 details the implemented architectures of the DP models and their results, while Section 4 reports the results of the comparison and draws the conclusions, providing initial guidance on which diagnostics and models are most promising for the development of a cross-machine disruption prediction system and looking to the future for its integration into next-generation devices.
# IoT-Enabled Smart Sustainable Cities: Challenges and Approaches
## 2 Overview on a Smart Sustainable City  
In general, a city, which aims at becoming a smart sustainable city, has to become more attractive, more sustainable, more inclusive, and more balanced for the citizens who live in, work in, and visit the city itself. To this end, a local administration--such as that of the city of Parma, located in the northeastern side of Italy and participating to the H2020 Ruggedised EU project [6]--could summarize its intentions with the following keywords;  
* _share_ experiences, information, and projects, as well as responsibilities, among citizens, stakeholders and other institutions, for all the decisions that will affect the life of people in the upcoming years;
* strengthen the _identity_ of the city as a capital city for well-known aspects (e.g., good food, culture, and music), and make the city more attractive for its citizens, tourists, and businesses; and
* focus on _values_ in terms of ethics, social responsibility, and quality of services for people and companies.  
Therefore, the involvement of businesses, multi-utility companies, and public transport stakeholders can be considered as an essential _added value_ to achieve these goals. To this end, a viable and optimal solution corresponds to creating inter-sectorial working groups from different areas (e.g., energy, urban planning, mobility, and ICT), working on integrating urban policies with the aim of evolving towards a smart city.  
As an example, the following plans are associated with activities which a municipality may be interested in, in order to improve its sustainability level.  
* Strategic Environmental Assessment Plan (SEAP), targeting a reduction in CO2 emissions of a certain percentage, thus improving the global environmental conditions of the municipality.
* Sustainable Urban Mobility Plan (SUMP), aiming at
* reducing private cars' usage and allowing people to move in a more efficient, sustainable, and safe way;
* re-designing the public transport network to cope with actual user expectations in terms of efficiency, quality and fast access to information;
* developing inter-modality and interconnection with different urban transfer systems (e.g., bike and car sharing, electric mobility, car pooling);
* improving environmental quality, by reducing noise and air pollution, and recovering urban spaces; and
* reducing transport costs, energy consumption, and waste of resources.
* Sustainable Energy and Climate Action Plan (SECAP), aiming at (i) mitigating the environmental problems through decarbonization process, (ii) adapting to climate changes, and (iii) increasing energy efficiency for a secure and sustainable energy management.
## 5 Implementation Details
### The Key Interfaces  
Applications of TinyOS and also SOMM are written via a component based programming language called nesC [29]. Each component of a nesC program provides some interfaces. The key interfaces of SOMM are shown in Figures 7 and 8 while does not consists of much details for the purpose of readability.  
Tuple space interface is shown in Figure 7(a) which in addition to provide standard tuple space operations, provides _tuple Ready_ and _new Tuple_ events. The _tuple Ready_ event fires when an _in_ or _rd_ instruction is completed successfully and also _new Tuple_ event fires when a new tuple is added to tuple space. Agent manager must be informed of addition of a new tuple to tuple space in order to resume the execution of a blocked agent that waits for that tuple and this is done using _new Tuple_ event. The agent manager is also responsible for marshaling, un-marshaling, allocating memory and executing the agents. Therefore, it uses the _agent Arriving_ event of agent transmitter component [that is shown in Figure 7(b)] to be aware when an agent wants to migrate to its host. Before an agent migrate to another node, the _agent Arriving_ event of transmitter component in destination node fires and informs the agent manager. Agent manager then checks the available resources such as memory and decides whether to allow the migration or not. If migration is allowed, agent transmitter downloads the agent and informs the agent manager that the migration is completed using _agent Arrived_ event. After that, agent manager un-marshals arrived agent and prepares it for execution.  
Service manager interface which is shown in Figure 8(a) provides a command named _detDwnServices_. At node startup and after determination of closest code repository and service registry addresses, agent manager calls the _detDwnServices_ command. As a result of executing this command, service manager detects the connected sensors to the node and downloads the proper proxy agent for each sensor.
## 3 Results and Discussion
### Comparison of Oxidation Potential in PM\({}_{2.5}\)  
Our DTT analysis results can be used to measure PM biotoxicity, and the DTT loss rate was employed to indicate the OP of PM. In this study, the QDTT-OP for PM\({}_{2.5}\) measured during the study period was analyzed and found to have an average value of 5.3 nM m\({}^{-3}\). The diurnal concentration trend gradually increased during the early morning hours, reaching the highest levels during commuting hours and decreasing in the afternoon. The assessment of OP using the DTT method has been extensively studied. Ref. [48] analyzed the characteristics of oxidative potential in Milan, Italy, and reported that BB, which occurs in winter, influenced oxidative potential. Ref. [49] reported that the OP of PM\({}_{2.5}\) measured in downtown Atlanta, USA, during winter was associated with BB. Figure 6 shows the correlation between the QDTT-OP results and significant components. Overall, OC (r\({}^{2}\) = 0.62) and EC (r\({}^{2}\) = 0.59) showed higher correlations compared to K (r\({}^{2}\) = 0.35) and K\({}^{+}\) (r\({}^{2}\) = 0.35). These results suggest the influence of mobile sources, the primary emission sources of carbonaceous components. QDTT-OP showed correlations of 0.53 and 0.69 with levoglucosan and BrC\({}_{370}\), respectively, with levoglucosan exhibiting the highest correlation. On February 18 th at 18:00, when levoglucosan levels were also highest, QDTT-OP reached 12.1 nM m\({}^{-3}\), the highest value observed. It was approximately 2.3 times higher than the overall average OP during the study period. Considering the correlations above, it can be inferred that the influence of biomass combustion enhanced oxidative potential to a greater extent.  
Figure 6: Correlation of DTT-OP with (**a**) organic carbon (OC), (**b**) levoglucosan, (**c**) black carbon at 370 nm (BC\({}_{370}\)), and (**d**) brown carbon at 370 nm (BrC\({}_{370}\)) during the measurement period.
## 6 Conclusions  
To detect hydrocarbons, generally, AVO analysis and attributes are used. However, there are still several pitfalls for this method because it requires a great deal of time and experience and subjectiveness as the analysis is completed manually. In this study, an unsupervised learning method was used named self-organizing maps (SOM) to delineate hydrocarbons from given AVO attributes that have been applied to detect hydrocarbons in previous research. From this study, the best selected AVO attributes to detect hydrocarbons using the proposed methods are gradient, product, sign of product, scaled Poisson ratio change, fluid factor, SQp, and SQs. The selected attributes were used as inputs for the anomaly detection model using SOM. In the well log scale analysis, the model worked well to detect hydrocarbons, where the model yielded an average accuracy of 83.5% on fluid replacement modelling well log simulation data. The model was also applied in A-1 and A-2 well log data, where it yielded an average accuracy of 92.5%. After being tested in well log data, the model was applied to real seismic data, where the model can predict the presence of hydrocarbons effectively. The proposed unsupervised learning model can be utilized as an alternative for early detection tools to identify the hydrocarbon presence at the preliminary stage of exploration when there is still no well available.  
This research can be elaborated for further development on hydrocarbon delineation by using the semi-supervised machine learning method. A small amount of labeled data to train a machine learning model is a common problem in the application of machine learning in hydrocarbon exploration because it often requires a great deal of experience and is cost ineffective. The current research workflow can be utilized to help labelling more unlabeled data to be used to train the machine learning model combined with the available data. These can potentially increase learning accuracy, creating a more robust model to predict hydrocarbons.  
Conceptualization, L.A.S. and M.H.; methodology, L.A.S. and M.H.; software, L.A.S.; validation, L.A.S. and M.H.; formal analysis, L.A.S. and M.H.; investigation, L.A.S. and M.H.; resources, L.A.S. and M.H.; data curation, L.A.S. and M.H.; writing--original draft preparation, L.A.S.; writing--review and editing, L.A.S., M.H. and I.S.; visualization, L.A.S.; supervision, M.H. and I.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by UTP fundamental research grant with grant number 015 MD0-059.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Univariate and Bivariate Analyses  
As a common practice, univariate imaging can be performed by selecting single-band image of the target chemical recorded using its most intense Raman peaks. The most intense Raman peak for Sudan-I was observed at 1594 cm\({}^{-1}\), but exhibited an overlap with a peak of Congo Red (1588 cm\({}^{-1}\)). On the other hand, the most intense peak of Congo Red observed at 1152 cm\({}^{-1}\) overlapped with that of paprika powder (1156 cm\({}^{-1}\)). Therefore, univariate imaging was herein performed by selecting the band images for Sudan-I and Congo Red at 1227 and 1351 cm\({}^{-1}\), respectively, since these bands were the second most intense and exhibited the least interference with the Raman peaks of other components. A chemical map of each adulterant was created by plotting these unique wavebands as a function of spatial position and intensity (Figure 3). The inconsistent background in the original images was corrected to a more consistent one, allowing pixels representing adulterant particles to be clearly seen after preprocessing.  
Since hypercube (image) correction is usually performed in the spectral domain (unfolded 2D data), the existence of noise in the spatial domain is obviously band-independent. Therefore, univariate (single-band) imaging may result in high-intensity noise pixels that can be classified as adulterant pixels after intensity-based image thresholding. A simple way of mitigating these effects is the application of the bivariate method, which involves the use of two data points. Importantly, noise is randomly distributed and is not related to the band position, whereas adulterant pixels in two band  
Figure 3: Raman band images of 0.75% adulterated sample at selected wavebands for adulterants.  
Figure 2: Preprocessed Raman spectra of paprika powder and adulterants.  
images of same adulterant have the same spatial position. Therefore, the multiplication of two bands representing the same adulterant obviously enhances only the pixels pertaining to the adulterant. Hence, we herein used the multiplication of two bands (i.e., 1227 and 1493 cm\({}^{-1}\) for Sudan-I and 1351 and 1451 cm\({}^{-1}\) for Congo Red) to enhance and segregate the adulterant particles from the paprika powder background and thus develop a quantitative model. The selected bands 1227 and 1493 cm\({}^{-1}\) for Sudan-I are related to the \(\delta\)(CH) and \(\
u\)(CC) vibration modes, respectively [22] and the Raman bands 1351 and 1451 cm\({}^{-1}\) selected for Congo Red represents CR bands and are related to azo-mode frequencies [23,24]. The result of the bivariate imaging (Figure 4) confirmed the enhancement of adulterants pixels and proved that the background noise could be significantly reduced, as is obvious from the comparison of the multiplication image with the single-band image for Congo Red.