esa-sceva/satcom-chunk-collection huggingface dataset:
# QB4 MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web
## 5 Conclusions and Future Works  
This paper studied and provided a proof-of-concept of mobility modeling for the enhancement of multidimensional data cubes using RDF Semantic Web. This research presents the representation of Mobility DW with an extended mobility data type, specified user-defined mobility functions, and implemented the concept and functions in the new QB4 MobOLAP vocabulary and SPARQL, respectively. QB4 MobOLAP is an extension of QB4 OLAP--the de facto standard Vocabulary for RDF Cube, which allows users to publish Mobility DW in the RDF format with mobility facts and measures that can be analyzed while taking into account contextual dimensions that are not restricted in pre-aggregate measures, as is the case for traditional DW representation. Integrating these mobility measures in DW realizes the notion of spatiotemporal queries defined in [55].  
Users can publish Mobility multidimensional RDF data using the QB4 MobOLAP Vocabulary. Thus, users can query from RDF endpoints using SPARQL's Mobility OLAP operators. The essential Mobility OLAP operations were defined to support users in mobility analysis, and these include Mobility Row Aggregation, Mobility Spatial Aggregation, Lifting Function Aggregation, Spatial Operation, and Temporal Operation. The evaluation of the vocabulary and operations was provided by implementing a real-world mobility query for FFB transportation analysis that illustrated the comprehensive set of queries and the execution performance.  
Future works will be conducted to extend and optimize the implementation of the query operators in SPARQL, which is still limited in this research. For non-technical users, the SPARQL query language is complex. So, the challenge is also to provide high-level Mobility OLAP operators to help non-technical users.  
**Author Contributions:**: [PERSON]. wrote the paper and code, [PERSON], [PERSON] and D.B.S. gave in-depth reviews and revised the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:**: This research did not receive any external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[[http://lod.if.tiuajy.ac.id:7200/repository](http://lod.if.tiuajy.ac.id:7200/repository)]([http://lod.if.tiuajy.ac.id:7200/repository](http://lod.if.tiuajy.ac.id:7200/repository))].  
**Acknowledgments:** We would like to acknowledge the support received from Computational Intelligence and Technologies (CIT) group in Universiti Teknikal Malaysia Melaka.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Literature Review
Relying solely on public satellite imagery, the epitome approach did not require any alterations to existing GPS infrastructures, making it a feasible solution for daily schemes. [PERSON] et al. [32] explored various machine learning algorithms, including linear regression, Naive Bayes, DTs, SVMs with different kernels, and Random Forests (RFs), grouping the signals into two sections: spoofed signals and authentic signals. Some machines, such as linear regression and an SVM (with a sigmoid kernel), performed poorly on the suggested dataset. Therefore, a combination of KNN and SVM algorithms was implemented to enhance the model's validity. The KNN algorithm served in detecting the closest neighbors of the test data, while the SVM algorithm grouped the test data into spoofed or authentic signals. The recommended model achieved a certainty of 99.5% on the test dataset, making it notably fitter than existing models. In addition to this, [PERSON] et al. [33] emphasized the importance of continuous authentication in drone systems, demonstrating that LSTM models could effectively detect unauthorized access with a 73% accuracy. [PERSON] et al. [34] introduced a hybrid approach that combines self-supervised representation learning with transfer learning, achieving a validation accuracy of 79%. Additionally, [PERSON] et al. [35] enhanced intrusion detection systems by integrating artificial neural networks with Genetic Algorithms, resulting in a 6% improvement in prediction accuracy. Notably, recent studies have underscored the effectiveness of LSTM networks in navigating GNSS-denied environments, with various researchers demonstrating significant reductions in positioning errors. Collectively, the reviewed literature shown in Table 1 highlights the potential of advanced machine learning techniques, particularly LSTM networks and their derivatives, to enhance UAV security against GPS spoofing attacks. These models have demonstrated high accuracy and reliability under controlled conditions, showcasing their capability to detect spoofing attempts effectively. However, a significant gap exists in the integration of optimization methods, such as Genetic Algorithms (GAs), to enhance adaptability and robustness in dynamic environments. Current studies employing GAs have primarily focused on optimizing typical hyperparameters or achieving incremental improvements, leaving the potential of exploring atypical configurations underutilized.
## 2 Methodology
### Models for Extracting Code Biases
#### 2.2.1 Undifferenced-SCB Model  
When only SCBs are extracted, the RCBs are assimilated using other parameters. Thus,  
\[\left\{\begin{array}{l}P_{3}(i)=\rho(i)+cdt_{r,SCB}(i)-c\widehat{d}^{s}(i)+T (i)-d_{3}^{s}\left(i\right)\\ L_{3}(i)=\rho(i)+cdt_{r,SCB}(i)-c\widehat{d}^{s}(i)+T(i)+\hat{A}_{3,SCB}\end{array}\right. \tag{7}\]  
where  
\[\begin{split} dt_{r,SCB}(i)&=dt_{r}(i)+\frac{\Delta d_{r,3}+ \delta b_{r,3}(i)}{c}\\ A_{3,SCB}&=A_{3}+\Delta b_{r,3}-\Delta b_{3}^{s}-\Delta d_{r,3} \end{split} \tag{8}\]  
Obviously, there will be a rank deficiency between \(cdt_{r,SCB}(i)\), \(d_{3}^{s}\left(i\right)\) and \(A_{3,SCB}\) parameters. The SCBs at the first epoch are chosen as a datum to eliminate the rank deficiency [25; 29]. Then, a full rank model for extracting SCBs in clock estimation can be obtained as follows:  
\[\left\{\begin{array}{l}P_{3}(i)=\rho(i)+cdt_{r,SCB}(i)-cdt_{SCB}^{s}(i)+T(i)- \widetilde{d}_{3}^{s}\left(i\right)\\ L_{3}(i)=\rho(i)+cdt_{r,SCB}(i)-cdt_{SCB}^{s}(i)+T(i)+\widetilde{A}_{3,SCB} \end{array}\right. \tag{9}\]  
with  
\[\widetilde{d}_{3}^{s}(i) =d_{3}^{s}(i)-d_{3}^{s}(1) \tag{10}\] \[dt_{SCB}^{s}(i) =dt^{s}(i)+\frac{d_{3}^{s}(1)+\delta b_{3}^{s}(i)}{c}\] \[\widetilde{A}_{3,SCB} =A_{3}+\Delta b_{r,3}-\Delta b_{3}^{s}-\Delta d_{r,3}+d_{3}^{s}(1)\]  
It is worth mentioning that \(\widetilde{d}_{3}^{s}(i)\) is the variation of the SCB with respect to the first epoch.
# A Comparative Study of Real and Virtual Environment via Psychological and Physiological Responses
## 3 Results
### Phase 2: Analysis of the Similarity between the Physical Experiment and the IVE  
The statistical treatment for this analysis depends on the normality of data for each variable. The Kolmogorov-Smirnov (K-S) test was used to assess the normality of the data.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**ANALYSIS AND DATA USED**} & \multicolumn{1}{c}{**STATISTICAL TREATMENT**} & \multicolumn{1}{c}{**EXPECTED RESULT**} \\ \hline Phase 1. Analysis of level of sense of presence. & Descriptive analysis of means. & Sufficient level of presence. \\ _SUS-Total._ & & \\ \hline Phase 2. Analysis of the similarity of the physical experiments and the IVEs, based on attention and memory performance and physiological responses. & \\ \(\bullet\) _Task-Time._ & ANOVA or Mann–Whitney test, depending on data distribution. & Significant differences depending on the experimental condition \\ \(\bullet\) _Memory-Correct answers._ & & (Physical vs. IVE). \\ \(\bullet\) _EEG-C3-Beta._ & & \\ \(\bullet\) _EEG-F3-Highbeta._ & & \\ \(\bullet\) _HRV-HE._ & & \\ \(\bullet\) _EDA-Phasic._ & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical treatments.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Item** & **Mean (Standard Deviation)** \\ \hline
1. & I had a sense of “being there” in the classroom space. & 5.33 (1.365) \\ \hline
2. & There were times during the experience when the classroom space was the reality for me. & 5.00 (1.168) \\ \hline
3. & The classroom space seems to me to be like somewhere that I visited before. & 5.20 (1.618) \\ \hline
4. & During the experience you felt you were in the classroom space. & 4.73 (1.543) \\ \hline
5. & I think the classroom space as a place similar to other places that I’ve been today. & 4.07 (1.789) \\ \hline
6. & During the experience I often thought that I was really in the classroom space. & 5.27 (1.711) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Means and standard deviations provided by the SUS questionnaire for each item.  
As the Task-Time variable follows a normal distribution (K-S test, \(p\) > 0.05), an ANOVA was applied. Due to the non-normality of data for the other variables (K-S test, \(p\) < 0.05), the Mann-Whitney test was used. The effect sizes were reported with partial eta squared (\(\eta^{2}_{\text{P}}\)). The results show that there were no significant differences in cognitive performance and physiological response. Tables 4 and 5 present the results of the statistical analysis, showing the test statistic value and the obtained \(p\)-value.
# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 2 Literature Review
### NN Techniques in Stock-Price Prediction
Finally, they compared the proposed model with general methods to demonstrate its performance. Recently, the substantial impact of cryptocurrencies on the global financial markets has led to an increasing number of price prediction studies in academic research. [PERSON] and [PERSON] (2018) proposed an LSTM algorithm to forecast the values of four types of cryptocurrencies: AMP, Ethereum, Electro-Optical System, and XRP. To overcome the problem of price-fluctuation prediction, they proposed an LSTM that captures the time dependency aspects of the prices of cryptocurrencies and proposed an embedding network to capture the hidden representations from linked cryptocurrencies. They then employed these two networks in conjunction with each other to predict price. In addition, [PERSON] et al. (2019) introduced a methodology aimed at optimizing cryptocurrency trading decisions to enhance profit margins. Their approach integrates various statistical, text analytics, and deep-learning methodologies to support a recommendation trading algorithm. Notably, the study leverages supplementary data points, such as the correlation between social media activity and price movements, causal relationships within price trends, and the sentiment analysis of cryptocurrency-related social media, to generate both buy and sell signals. Finally, the researchers conducted numerous experiments utilizing historical data to evaluate the efficacy of the trading algorithm, achieving an average gain of 194% without factoring in transaction fees and 117% when accounting for fees. Lastly, [PERSON] et al. (2019) employed AI algorithms, including the gated recurrent unit (GRU) and MLP, for forecasting Bitcoin prices. They evaluated their models using various metrics, such as mean square error (MSE), root mean square error (RMSE), Pearson correlation (R), and R-squared (R2), to assess performance. Their findings indicated that the MLP method outperformed the GRU approach. Given these studies, the primary contributions of the current study to the extant literature are as follows. * We are the first to use Bloomberg Twitter and news publication count variables as critical inputs for stock-price prediction within the North American context;* We use a novel approach in employing Twitter and news publication count variables as inputs into multi-layer perceptron (MLP) and long short-term memory (LSTM) NNs. This novel approach seeks to assess the influence of these variables on various NN architectures, allowing us to concurrently evaluate and contrast the stock-price prediction performance of both models;
* We focus on examining the existence of a potential notable decline in model performance during periods rife with market panic (e.g., the COVID-19 pandemic). Therefore, we seek to provide insights into the robustness of the proposed models under stressful conditions in financial markets.
## 3 Results and Discussion
### Drug Release
Due to the complexity of the synthesis for functionalized systems, the release experiments of DOX@MSN-PLH and DOX@MSN-PLH-TAM (synthesized by post-surface grafting approach, Figure 2b) were only conducted once for each sample. However, the pilot study still confirmed a decrease in DOX release from DOX@MSN-PLH and DOX@MSN-PLH-TAM system, compared to that of native DOX@MSNs. This implied a potential pore blocking effect due to the addition of surface functionalities. Additionally, this may be due to the (-OH) groups of encapsulated DOX molecules forming some additional hydrogen bonds with amine groups (from the lysine moiety) of the PLH molecules and/or with the oxygen atoms of TAM molecules. We observed a pH sensitive release behaviour of DOX in our novel nanostructure MSN-PLH-TAM, with more DOX released at acidic pH compared to that of biological pH. However, the release of encapsulated DOX from MSN-PLH-TAM decreased at both pHs, compared to that of MSN-PLH. This may suggest that obtained delayed release was a result of the pore-blocking effect caused by the increased surface moieties, following the conjugation of TAM-TEG building blocks onto MSN-PLH. In addition to this, it is possible that some hydrophobic stacking between TAM and PLH molecules may have occurred on the surface of MSN-PLH-TAM, contributing to its overall lower amount of DOX released over time.
## 4 Analysis of the Vibration Suppression Mechanism of the Deflector Plate
### Analysis of Vibration Suppression Mechanism of Deflector Plate  
The wind tunnel test found that the lower chord and web deflector plates were the best working conditions for vibration suppression in all deflector test conditions. In order to explore vibration suppression mechanisms, we conducted a numerical simulation on a double-deck truss girder section with lower chord and web deflector plates. We analyzed the influence mechanism through the change in the flow field. Figure 25 shows the relationship between the maximum value of the vertical amplitude and the change in wind speed after setting web and lower chord deflectors. It can be seen from Figure 25 that after the lower chord and web deflector plates were installed on the original section of the double-deck truss girder, the locking range of VIV wind speed was significantly shortened, and the maximum vertical amplitude was significantly reduced at the wind attack angles of +3\({}^{\circ}\) and +5\({}^{\circ}\).  
Figure 24: Vorticity evolution diagram of truss girder’s original section in one period.  
Figure 26 shows the vorticity change in a complete cycle of the original section of the double-deck truss girder after the lower chord and deflector plates were set.  
It can be seen from Figure 26 that at +3\({}^{\circ}\) and +5\({}^{\circ}\) wind attack angles, due to the effect of the sheer skirt of the lower chord deflector, the length of the separation zone at the lower chord deflector was significantly smaller than that of the original section. The velocity of the flow around the lower surface was reduced so that the vorticity at the exact position of the lower chord deflector was significantly lower than that of the original section, so the lower chord deflector plate can effectively suppress the VIV response. At the position of the web deflector, although the degree of flow separation was weakened compared with that of the original section, a weak Karman vortex street was still formed, which can slightly suppress the VIV response.  
Figure 26: Changes in the size and direction of a cycle vortex.  
Figure 25: VIV responses of lower chord and web deflector plates.
# A New Methodological Approach for the Assessment of the \({}^{238}\)U Content in Drinking Water
## 2 Materials and Methods
### Diagnostics Techniques and Samples Measurements
#### 2.2.2 Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)  
The concentration of \({}^{238}\)U was obtained through ICP-MS analysis with a Thermo Scientific iCAP Qc ICP-MS. The sample introduction system consisted of a Peltier cooled (3 \({}^{\circ}\)C), baffled cyclonic spray chamber, PFA nebulizer and quartz torch with a 2.5 mm  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**HPGe GMX Detector**} \\ \hline
**Parameter** & **Value** \\ \hline Full Width at Half Maximum & 1.94 keV \\ Peak to Compton ratio & 65:1 \\ Relative efficiency & 37.5\% (at the 1.33 MeV \({}^{60}\)Co \(\gamma\)-line) \\ Bias voltage & \(-\)4800 V \\ Energy range & 5 keV–2 MeV \\ \hline \hline \end{tabular}
\end{table}
Table 1: The HPGe GMX operating parameters.  
i.d. removable quartz injector. The instrument operated in a single collision cell mode, with kinetic energy discrimination (KED), using pure He as collision gas. All samples were presented for analysis using a Cetac ASX-520. The iCAP Qc ICP-MS operated in a single KED mode using the parameters reported in Table 2[36]. For the \"direct analysis\" of analytes in drinking water where sample turbidity is <1 NTU, the sample was prepared by the appropriate addition of nitric acid (1%).  
Sample material in solution was introduced by pneumatic nebulization into a radiofrequency plasma where energy transfer processes cause desolvation, atomization and ionization. Ions were extracted from the plasma through a differentially pumped vacuum interface and separated on the basis of their mass-to-charge ratio by a quadrupole mass spectrometer having a minimum resolution capability of 1 amu peak width at 5% peak height. Ions transmitted through the quadrupole were detected by an electron multiplier or Faraday detector and the ion information processed by a data handling system. Interferences relating to the technique had to be identified and corrected. Such corrections included compensation for isobaric elemental interferences and interferences from polyatomic ions derived from the plasma gas, reagents or sample matrix. Instrumental drift as well as suppressions or enhancements of instrument response caused by the sample matrix were corrected for using internal standards[36].  
The quality of the ICP-MS results was certified by the Italian Accreditation Body (ACCREDIA)[35].  
The massic elemental concentrations in \(\upmu\)g/L for uranium was converted in \({}^{238}\)U activity concentration, according to the following formula[37]  
\[C=\frac{\lambda N_{A}f}{MK}F \tag{2}\]  
where \(C\) is the measured specific activity (Bq/L) of the radionuclide under consideration, \(\lambda\) the decay constant of the measured isotope of element (s\({}^{-1}\)), \(N_{A}\) the Avogadro's number, \(f\) the fractional atomic abundance in nature, \(M\) the atomic mass (kg/mol), \(K\) a constant with value of \(10^{6}\) for U and \(F\) the fraction of element in the sample.
## 3 Materials and Methods
### Determination of OTA Concentration  
#### 3.6.1 QuEChERS Sample Preparation  
Sample preparation was performed following the QuEChERS method. Briefly, grape berries were manually destemmed and desseeded and then homogenized to obtain the grape must. The must (10 g) was transferred to a 50 mL polypropylene tube. Then, 10 mL of acetonitrile with 1% formic acid was added, and the tube was vortexed for 5 min. Subsequently, 4.0 g of anhydrous magnesium sulfate and 1.0 g of sodium chloride were added, and the tube was vigorously shaken by hand for 3 min, followed by centrifugation at 8228\(\times\)\(g\) for 5 min at 4 \({}^{\circ}\)C (model 5810R, Eppendorf, Hamburg, Germany). For the cleanup step, 1.0 mL of the supernatant was transferred to a 2.0 mL microtube containing 150 mg of anhydrous magnesium sulfate and 80 mg of graphite powder. Then, the mixture was vigorously shaken for 1 min, followed by centrifugation at 13,523\(\times\)\(g\) for 30 s at 4 \({}^{\circ}\)C  
Figure 7: Effects of different extraction solvents and sorbents on the peak area of ochratoxin A (OTA). A, 30 mg of primary secondary amine (PSA) and 150 mg of MgSO\({}_{4}\); B, 80 mg of graphite powder and 150 mg of MgSO\({}_{4}\); C, 30 mg of graphitized carbon black (GCB) and 150 mg of MgSO\({}_{4}\); D, 30 mg of octadecyl (C18) and 150 mg of MgSO\({}_{4}\); 1, ACN; 2, ACN and 0.1% formic acid; 3, ACN and 1% formic acid; 4, ACN and 5% formic acid.  
(model 5424R, Eppendorf, Hamburg, Germany). The obtained supernatant was filtered through a 0.22 \(\upmu\)m PTFE membrane and then injected for HPLC-FLD analysis.
## 4 Design, Implementation and Analysis Results of the Image Encryption Algorithm
### Image Encryption Application and Security Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fan Image** & **City Image** & **Baboon Image** \\ \hline
**Correlation Analysis (\(r_{xy}\))** & 0.0054 & 0.0039 & 0.0061 \\
**NPCR** & 99.6167 & 99.5232 & 99.5845 \\
**UACI** & 31.7543 & 35.2089 & 32.0312 \\
**Information Entropy** & 7.9563 & 7.9514 & 7.9572 \\
**Encryption Quality** & 35.8046 & 45.3828 & 61.4676 \\
**Total Time (encryption+decryption) (sec)** & 1.0540 & 1.0520 & 1.0485 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The security and performance analysis results. Figure 8: The result of Histogram and Correlation Analysis. (**a**) histogram of fan image, (**b**) histogram of city image, (**c**) histogram of baboon image, (**d**) correlation analysis of fan image, (**e**) correlation analysis of city image, (**f**) correlation analysis of baboon image. the proposed algorithm and some studies in the literature is given. When Table 6 is examined, it is seen that the proposed algorithm completes the encryption processes in a shorter time than studies in the literature.