esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 3 Data and Methods
### Method Used for the Simulation Study
#### 3.2.1 Modeled Solar Zenith Angle Variation  
The simulation started by deriving the evolution of the Solar Zenith Angle (SZA) as a function of LTDN temporal changes, using the following formula [26]:  
\[\cos\vartheta=\sin\delta\sin\phi+\cos\delta\cos\phi\ \cos\omega, \tag{1}\]  
in which \(\vartheta\) is the SZA, \(\delta\) is the declination of the sun, \(\phi\) is the latitude (defined positive at the northern hemisphere) and \(\omega\) is the hour angle, which is derived from the LTDN (in hours) using the following expression:  
\[\omega=|LTDN-12|\cdot\left(\frac{360^{\circ}}{24}\right)\cdot\frac{\pi}{180^{ \circ}} \tag{2}\]  
The hour angle is a measure of the local time; it is defined as the angle through which the Earth must turn to bring the meridian of the location of observation directly under the sun. The following empirical expression for the solar declination \(\delta\) was used, which is a function of the day of the year [26]:  
\[\begin{array}{rl}\delta=&(0.006918-0.399912\cos\gamma+0.070257\sin\gamma-0. 008758\cos 2\gamma+\\ &0.000907\sin 2\gamma-0.002697\cos 3\gamma+0.00148\sin 3\gamma\gamma)\left( \frac{180^{\circ}}{\pi}\right)\end{array}, \tag{3}\]  
where \(\gamma=2\pi(i-1)/365\) and \(i\) is the day of the year. The evolution of SZA as a function of LTDN was used as input to a BRDF semi-empirical model in order to forecast the variation of surface reflectances in the four Proba-V spectral bands and for the three cameras.
## 4 Proposed Approach
### Feature Analysis of MQTT Dataset  
Evaluation Data Analysis (EDA) is a method of evaluating data that employs graphs or charts. It is used to detect trends, patterns, and assumptions using statistical summaries and graphical representations.  
Using Pearson correlation, the author [69] demonstrated a real-time supervised learning approach. Pearson correlation [70] using deep convolutional neural networks (DCNN) for document categorization with the goal of reducing the influence of negative document image problems such as signatures, markings, logos, and handwritten notes.  
Correlation is a statistical technique used to determine how one variable moves or changes in relation to another. It denotes the strength of the connection between the variables. It is a bivariate analytical measure that depicts the connection between many variables.  
Pearson coefficients are correlation coefficients that represent the relationship between two variables measured on the same ratio or interval scale. The Pearson coefficient reflects the degree of similarity between two continuous variables. The Pearson correlation coefficient measures the strength of a two-variable, linear relationship. It has a value between \(-1\) and \(1\), with \(-1\) representing total negative linear correlation, \(0\) representing no link, and \(+1\) representing total positive linear correlation [71].  
Figure 5: The Pseudo code of the operation process flow of the research for anomaly detection using ML techniques to identify the best-performing algorithm based on the evaluation metrics.  
Below is a formula for calculating the Pearson correlation coefficient (r) [72] represented in Equation (1)  
\[r=\frac{\sum_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}-\overline{y})}}{\sqrt{\sum_{i=1 }^{n}{(x_{i}-\overline{x})^{2}\sqrt{\sum_{i=1}^{n}{(y_{i}-\overline{y})^{2}}}}}} \tag{1}\]  
where, \(n\) is sample size, \(x_{i}\), \(y_{i}\) are the individual sample points indexed with \(i\).  
Rearranging gives us the following formula which is represented in Equation (2)  
\[r=\frac{n\sum{x_{i}y_{i}-\sum{x_{i}\sum{y_{i}}}}}{\sqrt{n\sum{x_{i}^{2}-(\sum{ x_{i}})^{2}\sqrt{n\sum{y_{i}^{2}-(\sum{y_{i}})^{2}}}}}} \tag{2}\]  
The Pearson correlation is applied to MQTT dataset to determine the dependencies among the input variables and the target classes.
## 3 Models and Methods
### Computational Methods  
In a frequency range lower than the MHz range, it is feasible to utilize a quasi-static approximation to calculate the internal electric field within biological tissue [51; 52; 53]. In the regime in which the quasi-static approximation is valid, the conduction current in the body is dominant rather than the displacement current. This assumption causes the external electromagnetic fields to be decoupled into electric and magnetic fields. Additionally, the external magnetic field can be assumed to be unperturbed by the presence of the human body.  
The magnetic field leaked from the WPT system was computed using a commercial electromagnetic simulator (HFSS, Ver. 2023 (R1), ANSYS, Canonsburg, PA, USA) without considering the human body model. The magnetic field was extracted from HFSS in several rectangular volumes with a grid resolution of 50 mm \(\times\) 50 mm \(\times\) 50 mm. These data were used to construct a magnetic vector potential with a grid resolution of 2 mm \(\times\) 2 mm \(\times\) 2 mm.  
The next step involves computing the internal electric field by substituting the vector potential distribution into an electromagnetic solver developed at the Nagoya Institute of Technology [54]. The solver used to compute the internal electric field is based on the scalar-potential finite-difference (SPFD) method [55].  
The SPFD method discretizes the human model using cubical voxels and generates simultaneous linear equations for all contacts, with the electric scalar potential as an unknown variable. The internal electric field is then computed through matrix calculation. To accelerate computation, the geometric multigrid method is employed as a preconditioner [54]. In the SPFD method, tissue conductivities in the human body model are taken from a technical report in [56], which is consistent with [57; 58]. In addition, the leaked magnetic field from WPT coil has been validated for the simplified vehicle model with  
Figure 3: Cross-section of the vehicle model (unit: mm): (**a**) side view and (**b**) top view. \(y_{\text{s}}\) represents the distance in the front-back direction starting from the seat position of the vehicle.  
same computational approach [33]. Our computational approach here has been validated by intercomparison [40].
# Spirulina Supplements as a Source of Mineral Nutrients in the Daily Diet
## 3 Results  
Overall, in terms of mean values, the most abundant mineral in spirulina supplements was phosphorus (P) (15,149 \(\pm\) 13,024 mg/kg) and the least abundant was selenium (Se) (0.31 \(\pm\) 0.91 mg/kg). The content of calcium (Ca) was 8554 \(\pm\) 17,869 mg/kg, iron (Fe)--664 \(\pm\) 436 mg/kg, magnesium (Mg)--3726 \(\pm\) 1283 mg/kg, potassium (K)--14,274 \(\pm\) 3628 mg/kg, sodium (Na)--13,439 \(\pm\) 6793 mg/kg, and strontium (Sr) 42 \(\pm\) 45 mg/kg. The high standard deviations were due to the large differences in mineral contents determined in individual samples (Table 3).  
Table 4 shows the content of the studied minerals depending on the form of the supplement. Supplements in powder form had statistically significantly higher content of Fe, Mg and K (\(p\) = 0.042945, \(p\) = 0.010622, \(p\) = 0.001389, respectively). Tablets, on the other hand, contained significantly more Na (p = 0.008072). With respect to the content of the other elements (Ca, Sr, Zn, P, Se), the differences in their content between tablets and powder were not statistically significant (\(p\) > 0.05).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Elements** & **Mean** & **Standard Deviation** & **Min.** & **Max.** \\ \hline Ca & 8554.46 & 17,869.90 & 761.191 & 76,781.53 \\ Fe & 664.80 & 436.81 & 212.228 & 2785.82 \\ Mg & 3726.32 & 1283.81 & 2134.017 & 11,067.04 \\ K & 14,274.26 & 3628.57 & 9019.566 & 38,335.28 \\ Na & 13,439.40 & 6793.01 & 3102.046 & 36,144.06 \\ Sr & 42.83 & 45.59 & 6.784 & 258.42 \\ Zn & 45.96 & 71.57 & 7.068 & 322.14 \\ P & 15,149.59 & 13,024.69 & 7196.181 & 70,759.49 \\ Se & 0.31 & 0.91 & 0.031 & 5.69 \\ \hline \end{tabular}
\end{table}
Table 3: Element content of tested samples (\(n\) = 33) of spirulina supplements—results of statistical analysis.  
Statistical analysis of the results revealed significant differences in the content of Mg, K and Fe (\(p\) = 0.007932, \(p\) = 0.000419 and \(p\) = 0.001533, respectively), with organic spirulina tablets being the richer source.  
Detailed results, including minimums, maximums, medians and quartiles are presented as a Supplement (Tables S1-S4).
## 5 Multifunctional mPCMs
### Multifunctional mPCMs
#### 5.1.2 Photothermal Conversion Activity  
The choice of inorganic materials for shell materials for photothermal conversion property is a suitable alternative to polymeric materials due to their better durability, flame retardancy, and thermal and chemical stability. These materials of various species can be used alone or coupled with other additives to improve the thermal properties. [PERSON] et al. used calcium carbonate (CaCO\({}_{3}\)) to improve the solar thermal conversion of paraffin [183]. They established that paraffin/CaCO\({}_{3}\) microcapsules have an excellent light to heat energy conversion property. PCMs double-layered microcapsules were used as photothermal materials that convert solar light to heat in heat transfer fluids for direct adsorption in solar collectors. [PERSON] et al. encapsulated paraffin with SnO\({}_{2}\)/CNTs composite shell for the same dual application through in situ precipitation [184]. Graphene oxide (GO) was recognized as an excellent material to increase light absorption in the visible region of mPCMs. [PERSON] et al. [185] developed paraffin/SiO\({}_{2}\)/graphene oxide (GO) bifunctional microcapsules. The developed microcapsules showed suitable thermal characteristics with melting point and melting enthalpy of 57.5 \({}^{\circ}\)C and \(\sim\)80 J\(\cdot\)g\({}^{-1}\), respectively.  
Paraffin/graphene-melamine-formaldehyde mPCMs were synthesized via in situ polymerization [186]. Graphene was not only used to improve the thermal properties but also to stabilize the Pickering emulsion. Two of the main interests of the presence of graphene were relied on to the suppression of the leakage problem and the increased of the light-to-thermal conversion efficiency up to 90.7%.  
Copper (I) oxide was also used to convert sunlight into thermal energy. The self-assembly process synthesized paraffin/Cu\({}_{2}\)O-Cu microparticles. The thermal conductivity of the microcapsules was about 0.92 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\), while that of PCM was only 0.25 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\), which allows for a high photothermal conversion efficiency [187]. Adding CNTs in this PCM formulation allowed increasing the thermal conductivity by 1.6% and 1.8% at 30 and 80 \({}^{\circ}\)C, respectively, and has improved the photothermal conversion performance of the microcapsule slurry [188].
# An Approach for Detecting Feasible Paths Based on Minimal SSA Representation and Symbolic Execution
## 2 Background  
### Static Analysis  
Static code analysis is one of the most widely used tools of source code analysis because it can identify potential security issues or weaknesses without program execution [14]. During static analysis, the complete source code is analyzed, which makes this tool very powerful in the detection of security-specific issues [15]. Nevertheless, the technique is not completely free of errors; false positives may still occur because the program traversed an infeasible path or another that failed execution. For these reasons, the concern regarding false positives is significant in the static code analysis context and requires additional evaluation. The removal of infeasible paths from the set of paths on which static analyses are performed is another potential solution to address the false positive concern [16].  
Figure 1: Example of infeasible path and duplicated paths in PHP source code.  
and assigned only once, which can be achieved by renaming variables such that each variable name occurs only once on the left-hand side of an assignment statement. Figure 2a illustrates such variable renaming. However, this representation introduces a complication at join points of different flow branches. To solve this problem, a \(\phi\) function is used in places where we could not pinpoint the exact variable \"version\" [19; 20]. A code corresponding to the \(\phi\) function usage and a simplified graph excerpt are shown in Figure 2b.  
As each variable is assigned once in the SSA representation and a \(\phi\) function is added at each join point, it leads to an increase in the time required to analyze all instructions of the program. For instance, the LLVM code for the simple example comprises over 600 lines [9], which requires considerable time to analyze the source code for such examples. Therefore, removing some of the instructions that will not affect the program execution and decreasing the number of the \(\phi\) functions in the SSA form would be solutions that would produce a minimal SSA form that can decrease the time taken to detect the feasible paths.
## 3 Results
### Data Description  
The Universidad del Norte conducted the MetS study with a sample of 615 patients split into 348 women and 267 men between 20 and 96 years old. The study used blood tests to obtain the biochemical variables of triglycerides, fasting plasma glucose, HDL-C. The results are separated into three groups: MetS, Non-MetS, and total, as shown in Table 7.  
The biochemical variables of the MetS group and the Non-MetS group have different statistical significance. Together with the waist circumference and systolic and diastolic blood pressure,  
\begin{table}
\begin{tabular}{c c} \hline
**AROC** & **Discrimination Ability** \\ \hline AROC = 0.5 & No discrimination \\
0.5 \(<\) AROC \(<\) 0.7 & Regular \\
0.7 \(\leq\) AROC \(<\) 0.8 & Acceptable \\
0.8 \(\leq\) AROC \(<\) 0.9 & Excellent \\ AROC \(\geq\) 0.9 & Outstanding \\ \hline \end{tabular}
\end{table}
Table 6: Assessment rules of AROC.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Variables * & **MetS m(SD)** & **No MetS m(SD)** & **Total m(SD)** & \(p\) \\ \hline TG & 216.94 (112.8) & 121.84 (63.05) & 160.81 (98.67) & \(<\)0.001 \\ GL & 97.33 (38.82) & 84 (19.56) & 89.47 (29.74) & \(<\)0.001 \\ HDL-C[W] & 38 (8.22) & 46.97 (13.54) & 43.39 (12.49) & \(<\)0.001 \\ HDL-C[M] & 36.11 (11.1) & 43.32 (11.63) & 40.27 (11.93) & \(<\)0.001 \\ \hline \end{tabular}
\end{table}
Table 7: Statistic description of the biochemical variables.  
these variables are necessary to diagnose the MetS. The study found that the total prevalence rate was 42.60% divided into 44.94% for men and 40.8% for women.  
Moreover, healthcare professionals collected anthropometric and clinical variables such as Age, Sex, Weight, Height, WHR, HC, WC, SBP, and DBP in each patient. Other variables were calculated, such as BFP and BMI. Table 8 shows the statistical description of the total data of the study with the variables between people with MetS, No MetS, and total. These variables were measured, given that several studies [31; 32; 33] suggested to take into account these variables to diagnose the MetS without a blood sample. Doctors can obtain those variables at the first medical consultation. We can observe that the average of each of these variables obtained from patients with MetS is higher than that of patients without MetS and present a statistical significance in the groups MetS and Non-MetS that evidence a difference between groups. On the contrary, the Height variable's behavior in the two groups demonstrates a (\(p\) = 0.068) very low probability of difference.  
Other variables that should be taken into account to diagnose the MetS are those found in the clinical history such as the Previous Obesity Diagnosis (POD) due to the relationship with the occurrence of coronary heart disease [37; 40]. Therefore, in this MetS study, the researchers asked patients about their history of a previous obesity diagnosis and found that 42.37% were MetS and 23.23% were Non-MetS with a significant difference of \(p<0.001\) in the chi2 test. Therefore, there is a possible association between POD and MetS. The odds ratio indicates that patients with POD are 2.43 times more likely to have MetS.
## 7 Fog Computing
### Challenges of Deploying Fog Computing for Smart Cities  
Many challenges face fog-based smart city systems. These challenges are summarized as follows [79; 81]:  
1. Virtualization technology selection Virtualization is the main factor in executing computing tasks at fog nodes. Fog nodes use lightweight containers for hyper-visioning as operating system (OS)-level virtualization. The defect of container-based virtualization is that it cannot adapt to other platforms.
2. Privacy and security Despite the security benefits of integrating fog computing into smart cities, fog computing faces many internal security issues that should be resolved. Privacy and security are among the greatest challenges facing fog computing. To overcome this, we should control and interrupt the location framework by supporting each layer of fog nodes. The sensors and computing nodes must be more secure when transferring information, especially critical data. Privacy is also a challenge in the smart city for protecting users' privacy and service providers' priorities to maintain it. Integrating different services in a smart city platform would result in mistakes for users and service providers, such as sending information to unauthorized people.
3. System management Managing distributed fog nodes is a challenge that can be solved by deploying SDN and NFV technologies. However, integrating SDN and NFV into fog computing systems is challenging. The problem comes from restructuring APIs of northbound, southbound, and east/westbound interfaces.
4. Mobility model Fog nodes always have a level of mobility that should be considered while designing communication models for such systems. Due to the massive number of devices in smart cities, devices with efficient resources can be deployed as fog nodes. The mobility in fog-based smart cities records a pattern of behavior and mobility. Applications that are data-sematic and context-aware need the capability of users' locations and equipment.
# Lactic Acid Bacteria as Biocontrol Agents against Potato (_Solanum tuberosum_ L.) Pathogens
## 3 Results and Discussion
### Antimicrobial Activity of Lactic Acid Bacteria In Vitro
Antifungal activity was previously indicated for Leuconostoc mesenteroides against _R. solani_[42] and _F. oxysporum_[43]. Our study confirmed these results only for a single strain of _L. mesenteroides_ (out of nine tested). Most of the tested _L. mesenteroides_ strains did not exhibit any antifungal activity against potato pathogens. However, a significant antibacterial activity against potato pathogens (_P. carorotovorum_ and _S. scabiei_) was observed for both _L. lactis_ and _L. mesenteroides_ strains. The remaining species of investigated LAB demonstrated a wide spectrum of antibacterial and antifungal activity, yet they were represented by a small number of strains (Table 2, Table S1). Ten most active strains (Figure 1, Table S1), selected based on both the widest spectrum of activity and growth inhibition zone of pathogens, were further assessed. Steptonymes scabiei was the most susceptible pathogen to selected LAB strains. Growth inhibition zones for this pathogen ranged from 19.7 \(\pm\) 0.6 to 34.0 \(\pm\) 1.0 mm for _L. plantarum_ BPC2 and _L. acidophilus_ LOCK932, respectively. In turn, _F. oxysporum_ and _Phoma exigua_ showed the highest resistance among all inhibited pathogens by selected LAB (Figure 1). Their growth inhibition zones did not exceed 7.0 \(\pm\) 1.2 and 10 \(\pm\) 1.7 mm in diameter, respectively, by any of the selected LAB strains. _F. oxysporum_ was similarly indicated as the most resistant fungi in a study concerning the application of LAB as biocontrol agents of tomato diseases [35].
# Real-Time Control System for Model Railway Based on SIMIS W Interlocking System
## 3 Materials and Methods
### Function Algorithms of Signalling Systems Used on Model Railway  
The foundational algorithms for the level-crossing and block systems were derived from previous bachelor's theses [9,10]. The elaboration of these algorithms is not essential to understand and replicate paper results. Algorithms of signalling systems are tied to the regulations of the railway line manager in a given country; therefore, their basic functions can be altered to meet regulations and standards of given country. Block system algorithms were initially implemented for the SIMATIC S7-300 PLC while level-crossing system algorithms were designed for the SIMATIC S7-1200 PLC. To streamline and consolidate the control processes, we opted to unify all control algorithms within a single SIMATIC S7-1200 PLC. These algorithms were subsequently adapted to interface with the ILTIS-N control system and further modified for implementation within the SIMATIC S7-1200 PLC.  
The SIMIS W interlocking system algorithms are distributed between a desktop PC application and the PLC. The PC application manages the logical dependencies of the SIMIS W, block, and level-crossing systems and their interactions with the ILTIS-N control system. The ILTIS-N dependencies encompass algorithms for building and cancelling train paths within stations and for the manual operation of switches and signals. Flowcharts illustrating selected functions can be found in Appendix A Figure A1.  
In contrast, the PLC contains algorithms dedicated to direct communication with external elements on the model railway, such as signals and switches (see Appendix A Figure A2). By distributing the system's logic in this manner, we maintained a clear separation between high-level decision-making processes handled by the PC application and the real-time control of physical track elements managed by the PLC.
## 4 Results
### Crack Conditions at the Failure Stage
#### 4.1.1 The Uniaxial Compression Test with Low Strain Rates  
Uniaxial compression stress-strain curves in three low strain rates are also represented in Figure 6. It can be seen that:  
1. A rapid decline after the maximum peak stress was observed, corresponding to the failure state. The sharp decline of limestone strength after the failure state is due to the macroscopic cracks' expansion and the strain softening of the residual strength.
2. An interesting phenomenon is that the stress growth curve shows several peak spikes after the line elastic phase. Moreover, for curves 1, 2, and 3, the compression phase curve is approximated, while the linear elastic phase curve grows sharply as the strain rate decreases slowly. Similar behavior was observed by [PERSON] [50] for different kinds of rock under compression loading and the phenomenon was given a name by [PERSON] [51].
3. The difference in the maximum stress is due to the load strain rate and the initial damage. For \(D\) = 0.043, the difference in the maximum uniaxial compression becomes more apparent than that of \(D\) = 0.357, 0.625, and 0.754. The value of the maximum compression stress in the load strain rate \(\overline{\epsilon}\) = \(2\times 10^{-4}\) s\({}^{-1}\) is 176 Mpa, which is 11.4% and 15.0% higher than that in \(\overline{\epsilon}\) = \(2\times 10^{-5}\) s\({}^{-1}\) and \(\overline{\epsilon}\) = \(2\times 10^{-6}\) s\({}^{-1}\), respectively. The stress strength of the limestone depends upon the degree of development of crack pores in the rock. A greater initial damage means the cracks inside the rock are more complicated, leading to a destruction in the compression test.  
Figure 5: Comparison before and after uniaxial compression test of clastic limestone: (a) Before compression test; (b) After compression test.