esa-sceva/satcom-chunk-collection huggingface dataset:
# A Narrative Review of Metabolomic Insights into Olive Oil's Nutritional Value
## 4 Case Studies and Applications
Furthermore, NMR spectroscopy has contributed to the study of the implications of the phenolic compound S-(-)-oleocanthal in the gut microbiota in a novel ex vivo mouse fecal anaerobic fermentation model that mimics the human gut environment, potentially aiding the prediction of bacterial distribution in the human body. Solid-phase extraction was performed, and H NMR and HPLC analyses allowed the achievement of 99% purity for the metabolite. These results demonstrate that polyphenols can affect the levels of several bacteria of known importance in human health [90]. Another study aimed to evaluate the effect of MD on urine metabolites in the context of the PREDIMED trial. H NMR allowed a comparison between the three dietary pattern groups, and the non-diabetic individuals were divided into MD + EVOO, MD + nuts, and low-fat diet groups. The metabolites in the urine were analyzed at baseline, a one-year follow-up, and a three-year follow-up, and evidence demonstrated clear differences between the groups [91]. As previously mentioned, olive oil has a protective effect against tumor cells, mainly owing to the antioxidant effects of phenolic compounds. In fact, in a study where HPLC allowed isolation of the phenolic compounds of EVOO, and the identification of the polyphenols was confirmed via CE-MS, polyphenols were found to promote proteasomal degradation of the protein HER2, coded by the homonymous oncogene [92]. Nanoliquid chromatography coupled with ESI time-of-flight mass spectrometry (nanoLC-ESI-TOF-MS) was the analytical technique used to isolate the phenolic fraction of olive oil in a study that provided valuable insights into the antiproliferative and pro-apoptotic effects of these olive oil components in colon cancer cells [93]. These analytical techniques are fundamental for understanding the beneficial health effects of isolated polyphenols. For instance, in a survey comparison of EVOO polyphenols where HPLC with diode array detection (DAD) was used to analyze and quantify HT, tyrosol, oleocanthal, and oleacein, the authors found, in a fly model, healthy aging-promoting effects from both oleocanthal and oleacein, along with their fundamental antioxidant properties [94]. In conclusion, the ability of advanced analytical techniques to precisely identify and quantify the key components of olive oil has contributed to ongoing efforts to harness its full potential as a valuable component of MD with several health benefits.
## 6 Factors Affecting Wax Inhibition Performance
### Chemical Inhibitor's Molecular Structure  
Most chemical inhibitors are polymers that have a backbone and a hydrocarbon pendant chain which interacts with the wax molecule in the crude oil and inhibits the agglomeration of large wax crystal structures. This contributes to the list of factors that influence the performance of the chemical inhibitors: the polymer backbone, the length of the hydrocarbon pendant chain, and the molecular weight. Despite earlier findings that the polymer backbone factor was not significant in affecting the pour point reduction [117], the polymer backbone of ethylene vinyl acetate co-polymer was reported to affect the polymer's inhibition performance. The length of the hydrocarbon pendant chain length is expected to match the length of wax molecules in the crude oil to obtain the inhibitor optimum performance [118]. A short, low molecular weight polymer may not have the molecular volume to interrupt the co-crystallization of the wax crystals, while a long, high molecular weight polymer may be insoluble or could interact with itself and not with the wax molecule in the crude oil. Also, the solubility of the polymer in the crude oil could be limited, thus inducing wax crystallization and increasing the pour point of the crude oil [118].
## 2 Motivation and Background Analysis
### Reason for Coexistence of ICN and Edge Computing  
We already described the features of ICN and edge computing. The individual characteristics of both ICN and EC are not fully suitable for respective scenarios. For example, EC provides the caching mechanism in the edge of the end-users but ICN provides caching mechanism at each node in the network. Therefore, the availability of the cached content will be increased automatically. To handle the millions and billions of IoT devices, EC does not provide any unique solution but the ICN naming technique can solve the IoT naming issues. Similarly, the coexistence of ICN and EC will be a greatenvironment for the future service-oriented network architecture that may solve both upstream and downstream content management issues.
# Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving
## 2 Data Mining
### Regression Algorithms  
#### 2.2.1 Multiple Linear Regression  
Multiple linear regression (MLR) is a classical regression algorithm for the prediction of a numerical attribute based on multiple attributes. The predicted attribute \(\breve{Y}_{n}\) of an instance \(n\) is calculated as the weighted sum of the other \(K\) attributes \(X_{k,n}\) of the same instance (1).  
\[\breve{Y}_{n}=a_{0}+a_{1}X_{1,n}+a_{2}X_{2,n}+\ldots+a_{K}X_{K,n}=\sum_{k=0}^{ K}a_{k}X_{k,n} \tag{1}\]  
The weights are iteratively determined in a way to minimize the root mean square error (\(RMSE\)) of the prediction \(\breve{Y}_{n}\) against the observed attribute \(Y_{n}\) of \(N\) instances (2).  
\[RMSE=\sqrt{\frac{\sum_{n=1}^{N}\left(\breve{Y}_{n}-Y_{n}\right)^{2}}{N}} \tag{2}\]
## 4 Experiments
### Evaluation Indexes  
In our study, we evaluated the above 3 datasets in both RR and FR tests. In the RR evaluation, we employed several performance metrics, including the Spectral Angle Mapper (SAM) [24], the Erreur Relative _Globale Adimensionnelle de Synthese_ (ERGAS) [28], the Correlation Coefficient (CC) [29], and the Extended Universal Image Quality Index (Q2n, with Q4 and Q8 for four-band and eight-band images, respectively) [30].  
We computed three additional metrics: the Spatial Correlation Coefficient (SCC), the Spatial Distortion Index \(D_{S}\), and the Spectral Distortion Index \(D_{\lambda}^{K}\). Furthermore, the High-Quality No Reference (HQNR) metric was also utilized. According to [PERSON] et al. [31], the value of HQNR can be obtained using \(D_{S}\) and \(D_{\lambda}^{K}\). In other words, HQNR helps us evaluate the quality of images by measuring spatial and spectral distortions. The spatial distortion is related to the spatial component (\(D_{S}\)) of the image, and the spatial distortion represents the degree of distortion of features such as pixel change, texture, and edge in the image. Related to the spectral component of the image (\(D_{\lambda}^{K}\)), spectral distortion represents the distortion of the image in the frequency domain, usually affecting the high-frequency portion of the image. The mean absolute difference of the \(D_{AVE}\) values across all channels is used to obtain the value of \(D_{S}\) (the \(D_{AVE}\) calculation method and practical significance are also described in the paper). The spectral distortion index \(D_{\lambda}^{K}\) is found according to [PERSON]'s protocol [32]. The lower \(D_{\lambda}\), \(D_{S}\), and the higher QNR correspond to the better image quality [33].
## 3 Results
### Development of a Detailed Systematic Approach for the Function of Heat Storage
#### 3.2.2 Degradation Effects and Underlying Mechanisms  
Knowing the basics of the \"property\" enthalpy, which is used to describe the \"function\" heat storage, the next thing to do is to generally analyze the \"degradation effects\" and their underlying \"mechanisms\". For a specific case, e.g., for a specific PCM, this will allow looking for possible degradation mechanisms, and answer the question if degradation could happen at all. Further on, it allows drawing conclusions on the selection of test conditions, e.g., the T(t) profile during cycling. Currently, knowledge of degradation effects and mechanisms is from experience for specific material classes, like phase separation for salt hydrates, oxidation of organic PCMs, compatibility of salt hydrates and metals or organic PCMs and plastics, etc. However, if a new material class is investigated in search for new PCM, then there is no previous experience. And even if a material class is already investigated for a long time, are the known degradation effects and underlying degradation mechanisms complete? Is it possible that when selecting specific effects, mechanisms, or conditions for selective testing we miss something? A systematic approach is needed. It is now developed, using the approach described in Section 3.1.4, Figure 3. The results are summarized in Table 2.  
Basically, a PCM is a single chemical compound or a mixture of several chemical compounds. For heat storage, the PCM undergoes a change in phase, meaning of the chemical composition and/or physical composition, the latter referring specifically to its crystal structure, crystallinity, and other related things. This is also the origin of the phase change enthalpy h(T), being the basis for the function of heat storage. The first degradation effect is a change in the chemical composition of the PCM to other chemical compounds, thus at the atomic/molecular level, and the related degradation mechanism is then a chemical reaction. The simplest example is a decomposition of just the PCM. For example, normal paraffins are known to undergo thermal decomposition to methane and the corresponding olefine [23]. At ambient atmosphere, normal paraffins react with oxygen, forming fatty acids, starting at about 150 \({}^{\circ}\)C [24]. Erythrittol was also investigated with regard to this effect [25]. The exposure to another substance, like oxygen, is an issue of compatibility, and thus protection of the PCM.
## 4 Conclusions  
(1) The proposed unsupervised learning approach combining t-SNE and k-means clustering demonstrated superior performance in classifying coal spontaneous combustion warning levels compared to traditional PCA and SNE methods. The t-SNE method achieved optimal clustering with six distinct warning levels and a peak silhouette coefficient of 0.95, indicating excellent cluster separation and cohesion. This classification framework provides a more objective and reliable basis for risk assessment compared to conventional supervised learning methods.  
(2) The analysis of temperature distributions and gas concentration patterns revealed strong nonlinear relationships between indicator gases and warning levels, with maximum MIC values of 0.95 and 0.81 for CO and O\({}_{2}\) concentrations, respectively. The t-SNE method effectively captured these nonlinear relationships while preserving both local and global  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sequence** & **CO\({}_{2}\)\%** & **C\({}_{2}\)H\({}_{4}\)/ppm** & **C\({}_{2}\)H\({}_{6}\)/ppm** & **CH\({}_{4}\)/ppm** & **CO/ppm** & **C\({}_{2}\)H\({}_{2}\)/ppm** & **O\({}_{2}\)\%** & **N\({}_{2}\)\%** & **t-SNE** \\ \hline
1 & 0.06 & 0 & 0 & 0 & 0 & 0 & 20.56 & 79.38 & Safe \\
2 & 0.06 & 0 & 0 & 0 & 0 & 0 & 20.37 & 79.57 & Safe \\
3 & 0.53 & 0 & 0 & 5 & 9 & 0 & 19.39 & 80.08 & Low Risk \\
4 & 0.68 & 0 & 0 & 6 & 8 & 0 & 19.22 & 80.10 & Low Risk \\
5 & 0.75 & 0 & 0 & 3 & 5 & 0 & 19.63 & 79.57 & Low Risk \\
6 & 0.95 & 0 & 6.20 & 79 & 107 & 0 & 17.95 & 81.05 & High Risk \\
7 & 0.91 & 0 & 8.55 & 86 & 132 & 0 & 16.97 & 81.10 & High Risk \\
8 & 1.45 & 0 & 13.53 & 132 & 192 & 0 & 15.21 & 83.30 & Severe Risk \\
9 & 1.64 & 0 & 13.31 & 112 & 199 & 0 & 15.18 & 83.16 & Severe Risk \\
10 & 1.59 & 0 & 15.64 & 136 & 226 & 0 & 13.94 & 84.44 & Severe Risk \\ \hline \hline \end{tabular}
\end{table}
Table 2: Gas concentration monitoring data and warning level classification results from 3302 fully mechanized caving face of Longgu Coal Mine.  
data structures, enabling more accurate risk level classification based on multiple indicator gases rather than relying on single-parameter thresholds.  
(3) The field application at the Longgui Coal Mine validated the practical effectiveness of the integrated t-SNE and SVC approach. The system successfully detected and classified different risk levels based on gas concentration patterns, even in the absence of high-temperature indicators like C\({}_{2}\)H\({}_{4}\). This demonstrated the model's robustness in real-world conditions and its capability to provide an early warning for spontaneous combustion risks in goaf areas, making it a valuable tool for mine safety management.  
Conceptualization, P.Z. and X.C.; Methodology, P.Z.; Data curation, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52174206.  
Not applicable.  
The data used to support the findings of this study are included within the article.  
The authors declare no conflicts of interest.
## 2 Overview of Cyanphage
_Synchococcus_ phage SynMITS9220M01, **100**. _Synechococcus_ phage S-H25, **101**. _Synechococcus_ phage S-P4, **102**. _Synechococcus_ phage S-E7, **103**. _Synechococcus_ phage S-B64, **104**. _Synechococcus_ phage BELamy, **105**. _Synchococcus_ phage S-H35, **106**. _Synchococcus_ phage S-CAM9, **107**. _Synechococcus_ phage S-CAM7, **108**. _Synchococcus_ phage S-CAM4, **109**. _Synchococcus_ phage S-CAM3, **110**. _Synechococcus_ phage S-CAM2, **111**. _Synchococcus_ cyanophage S-WAM2, **112**. _Synechococcus_ cyanophage S-WAM1, **113**. _Synchococcus_ phage ACG-2014j, **114**. _Synchococcus_ phage ACG-2014g, **115**. _Synchococcus_ phage ACG-2014e, **118**. _Synchococcus_ phage ACG-2014d, **119**. _Synechococcus_ phage ACG-2014b, **120**. _Synechococcus_ phage ACG-2014a, **121**. _Synechococcus_ phage S-MbCM100, **122**. _Synechococcus_ phage S-MbCM2, **123**. _Synchococcus_ phage S-MbCM7, **124**. _Synchococcus_ phage S-RIM8 A-HR5, **125**. _Synchococcus_ phage S-RIM8 A.HR3, **126**. _Synechococcus_ phage S-RIM2 R1_999, **129**. _Synechococcus_ phage S-RM2 R21_2007, **130**. _Synechococcus_ phage Syn30, **131**. _Synchococcus_ phage ACG-2014c, **132**. _Synechococcus_ phage S-T4, **133**. _Synchococcus_ phage S-CBWM1, **134**. _Synchococcus_ phage ACG-2014h, **135**. _Synchococcus_ phage Synn19, **136**. _Synchococcus_ phage Synn19, **137**. Cyanophage KBS-S2, **138**. Cyanophage MED4-117, **139**. Cyanophage PSS2, **140**. _Microcystis_ phage MACPNOA1, **141**. _Prochlorococcus_ phage MED4-184, **142**. Cyanophage S-L2, **143**. _Synechococcus_ phage S-H1, **144**. _Synchococcus_ phage S-LBS1, **145**. _Synchococcus_ phage S-CBS1, **146**. _Synechococcus_ phage S-CBS3, **148**. _Synchococcus_ phage S-CBS2, **149**. _Synechococcus_ virus S-ESS1, **150**. _Microcystis_ cyanophage vB_Mae-Syong1, **151**. _Microcystis_ cyanophage Mwe-Yong1112-1. In analyzing the data, we sought first to explore genes linked to photosynthesis, with the question in mind as to what extent might viral infection enhance photosynthesis and thus carbon capture and oxygen production? Of the 153 viral DNA sequences analyzed, almost all could be seen to contain AMGs encoding proteins with a role in photosynthesis, with the potential to augment or control the infected host's photosynthetic activity. Only seven of the candidates did not clearly show the presence of such a gene. AMGs related directly to the enhancement of photosynthesis, including genes encoding the production of the photosystem II reaction center protein, the production of the high-light inducible protein, the antennae protein as well as the production of photosynthetic pigments, were clearly visible in many of the viruses.
## 4 Challenges and Future Directions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Challenge** & **Current Limitations** & **Emerging Solutions** & **Future Directions** \\ \hline \multirow{3}{*}{Signal Overlap} & Overlapping signals in complex mixtures & Multidimensional NMR, computational modeling, isotopic labeling & Development of advanced spectral deconvolution algorithms \\  & Low sensitivity for dilute & DNP-enhanced NMR, isotopic labeling (\({}^{13}\)C, \({}^{15}\)N) & Integration of DNP with ultra-high-field NMR \\  & Difficulty in studying dynamic & Time-resolved NMR, in situ & Development of portable NMR systems for real-time monitoring \\  & polysaccharide-protein & solid-state NMR & real-time monitoring \\  & Interactions in real time & Integration with & Holistic approaches \\  & Heterogeneous samples & Complementary techniques & combining NMR with \\  & & complicate structural analysis & (e.g., mass spectrometry, X-ray) & other methods \\  & & Cost-effective NMR systems, & Democatization of \\  & & open-access NMR facilities & NMR technology \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Challenges and future directions in NMR-based polysaccharide research. shown that combining these methods with NMR spectroscopy works well [17; 77]. In the future, we should make efforts to improve this integration. Specifically, we should focus on in situ studies of the dynamic processes and interactions of polysaccharide proteins. The resulting boundary field involves solid-state NMR studies of polysaccharides in intact biological systems like plant tissues and microbial biofilms. These methods can provide a more comprehensive understanding of the composition and function of polysaccharides in their natural environment more comprehensively [5; 10]. Additionally, recent advances in portable NMR technology are expected to be used on-site in areas such as food quality control, environmental monitoring, and industrial process optimization. Even though its resolution is currently limited, this technology shows great potential [27]. Finally, improving the accessibility of high-resolution NMR instrumentation is important to expand its applications in polysaccharide research globally. Innovations in hardware design, including cost-effective ultra-high-field systems, could broaden access to this powerful technology, enabling wider use across academic and industrial field.
# Towards Sentiment Analysis for Romanian Twitter Content
## 3 Data Processing Methodology
### Classifier Selection  
Following suggestions in the literature [2; 31; 3], we selected the following methods for building our classifiers:  
1. Bernoulli Naive Bayes (Bernoulli NB), Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR) from the classical ML;
2. Deep Neural Network (DNN), Long Short-Term Memory (LSTM) and the Convolutional Neural Network (CNN) from the area of deep learning;
3. Multilingual BERT--to get a glimpse of the state-of-the-art results. As indicated in Figure 1, the classical ML methods and the DNN were applied on the TF-IDF encoding, with and without dimensionality reduction and on Doc2 Vec. On Word2 Vec, we constructed classifiers with the help of LSTM and CNN.  
We implemented the classical ML algorithms with the help of Scikit-Learn library [62], while for the deep learning we used Keras [63].  
For BERT, we used the model available on the Hugging Face transformers ([[https://huggingface.co/docs/transformers/model_doc/bert](https://huggingface.co/docs/transformers/model_doc/bert)]([https://huggingface.co/docs/transformers/model_doc/bert](https://huggingface.co/docs/transformers/model_doc/bert)), accessed on 5 September 2022), called with the base multilingual uncased variant. On top of BERT, we added a hidden dense layer with 75 nodes and ReLU activation function, followed by the standard classification layer with 3 nodes which produce the sentiment. [PERSON] was the selected optimizer, with a learning rate of \(2\times 10^{-5}\) and \(\epsilon=10^{-8}\). The loss function was set to Categorical CrossEntropy.
## 9 Conclusions  
The edge based IoT environment suffers from serious security and privacy issues. It was observed that the majority of the existing schemes for authentication and key management have limitations as they were vulnerable to various attacks. Some of them were not even efficient from the computation and communication cost point of view. Consequently, a new authentication and key management scheme with lightweight cryptographic operations was designed for the edge based IoT environment (LDAKM-EIoT). The rigorous security analysis of LDAKM-EIoT using formal security verification using AVISPA tool and also other security analysis evidenced that LDAKM-EIoT could prevent different possible well known attacks. LDAKM-EIoT also supported new smart IoT device deployment in the network after the initial deployment. Moreover, LDAKM-EIoT preserved the anonymity and untraceability properties. LDAKM-EIoT was also compared with the closely related existing schemes in the IoT environment. The conducted comparative performance analysis and NS2 based simulation study on the network performance parameters evidenced that LDAKM-EIoT was much better in security and functionality features, communication, and computation as compared to other existing schemes.  
In the future, we would like to implement the proposed LDAKM-EIoT in a testbed environment. Moreover, we would also like to include more functionality features in the proposed LDAKM-EIoT while maintaining less communication and computational overheads without degrading the security.  
Conceptualization, M.W., A.K.D., S.S., J.J.P.C.R. and Y.P.; Methodology, M.W. and A.K.D.; Security analysis, M.W. and A.K.D.; Investigation, M.W., A.K.D., S.S. and J.J.P.C.R.; Formal security verification, A.K.D.; Resources, A.K.D., S.S., J.J.P.C.R. and Y.P.; Writing-original draft preparation, M.W.; Writing-review and editing, A.K.D., S.S., J.J.P.C.R. and Y.P.; Supervision, A.K.D. and J.J.P.C.R.; Project administration, A.K.D., S.S. and J.J.P.C.R.; Funding acquisition, J.J.P.C.R., Y.P. and S.S.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2017R1A2B1002147); in part by Fundacao para a Ciencia e a Tecnologia through the UID/EEA/50008/2019 Project; and in part by Brazilian National Council for Scientific and Technological Development (CNPq) via Grant No. 309335/2017-5.  
Figure 5: Comparison of network parameters: (**a**) network throughput; (**b**) end-to-end delay.  
This work was also supported by the Office of the Assistant Secretary of Defense for Research and Engineering (OASD (R&E)) agreement FA8750-15-2-0120.  
**Acknowledgments:** We thank the anonymous reviewers and the Editor for their valuable comments, which helped us to improve the quality and presentation of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion
### Uncertainties in LAI Time Series Images  
We produced the MODIS-based LAI seasonal variation curves for different forest types with spatial resolution of 500 m. The different spatial resolutions of MODIS (500 m) and Landsat TM/ETM+ (30 m) images might result in different LAI values in the same location due to the mixed pixels in MODIS images, even after applying the smooth interpolation procedures. Moreover, clumping index is a key parameter to retrieve LAI based on the MODIS data, which is closely related with forest types. The different forest types might result in different MODIS-based LAI estimates. Finally, the noisy signals of MODIS time series images, such as cloud, would affect the LAI seasonal variation curves for a specific forest type. In addition, gaps on raw Landsat images were filled using the GNSPI algorithm [32]. The uncertainties in the filled spectral values would be definitely propagated into the retrieved LAI.