esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 General Structure of Fully Pitched SRMs  
In SRMs, phase control is conducted independently. Since each phase is controlled independently, a fault in one phase does not affect the others. In other words, SRM continues to operate even in the event of a phase fault. However, this situation also causes major losses in motor performance [7]. In SRMs, their air gap power or electro-mechanical power \(P_{m}\) can be expressed as given in Equation (5), neglecting friction and windage losses [28].  
\[P_{m}=\frac{1}{2}i^{2}\frac{dL(\theta)}{d\theta}\omega_{m} \tag{5}\]  
where \(i\) is the active phase current, \(L\) is the active phase inductance, \(\theta\) is the rotor position angle, and \(\omega\) is the angular velocity. In this case, the electromagnetic torque \(T_{e}\) is given as in Equation (6) [28].  
\[T_{e}=\frac{P}{\omega_{m}}=\frac{1}{2}i^{2}\frac{dL(\theta)}{d\theta} \tag{6}\]  
In conventional SRMs, only one phase is active at a time. However, in fully pitched SRMs (FPSRMs), also known as mutually coupled SRMs (MCSRMs), two phases are active depending on the selected position [13]. This is because the torque production in FPSRMs relies on the mutual inductance between the phases [23]. Additionally, when two phases are active simultaneously in FPSRMs, they effectively function as a single tooth winding with twice the magnetomotive force (MMF). This configuration enables FPSRMs to achieve a higher torque output compared to conventional SRMs [13]. Additionally, in torque production, while conventional SRMs, also known as short-pitched SRMs, neglect mutual inductance between phases, FFSRMs disregard self-inductance [23; 24]. The generalized electromagnetic torque equation for SRMs is given in Equation (7) [23; 24; 29].  
\[T=\frac{1}{2}i_{a}^{2}\frac{\partial L_{a}}{\partial\theta}+\frac{1}{2}i_{b}^{2 }\frac{\partial L_{b}}{\partial\theta}+\frac{1}{2}i_{c}^{2}\frac{\partial L_{ c}}{\partial\theta}+i_{a}i_{b}\frac{\partial M_{ab}}{\partial\theta}+i_{b}i_{c} \frac{\partial M_{bc}}{\partial\theta}+i_{c}i_{a}\frac{\partial M_{ca}}{ \partial\theta} \tag{7}\]  
where \(i_{a}\), \(i_{b}\), \(i_{c}\) represent phase currents, \(M_{ab}\), \(M_{bc}\), \(M_{ca}\) represent mutual inductance between phases, \(L_{a}\), \(L_{b}\), \(L_{c}\) represent the self-inductance of the phases, \(\Theta\) represents the rotor position angles, \(T\) represents the torque. Since self-inductances are neglected in FFSRMs, the torque expression is expressed as in the Equation (8). Figure 1 shows the 2D image of a 3-phase 6/4 pole FFSRM with a radial structure, and Equation (8) shows the FFSRM torque expression.  
\[T=i_{a}i_{b}\frac{\partial M_{ab}}{\partial\theta}+i_{b}i_{c}\frac{\partial M_ {bc}}{\partial\theta}+i_{c}i_{a}\frac{\partial M_{ca}}{\partial\theta} \tag{8}\]
## 3 Results
### Evaluation of the Methodology  
To show the applicability of the method developed in Section 2, Figures 11-17 show the distribution of the (k\({}_{\rm d}\),SSD) pairs during the years shown in parentheses in column 1, Table 2. The SSD values were derived from the BSRN data, meeting the H\({}_{\rm bn}\) > 120 Wm\({}^{-2}\) WMO criterion; moreover, the SSD values from the ATH station are real 1-min measurements with the aid of an EKO MS-90 direct-normal-irradiance sensor. All (k\({  
Figure 14: Determination of clear, intermediate, and overcast skies for the (**a**) ILO and (**b**) KIS sites.  
Figure 13: Determination of clear, intermediate, and overcast skies for the (**a**) GAN and (**b**) OHY sites.  
Figure 15: Determination of clear, intermediate, and overcast skies for the (**a**) LER and (**b**) LIN sites.  
The shape of the scattered data points in the above diagrams is the expected one, i.e., decreasing SSD values with increasing \(\rm k_{d}\). Nevertheless, this self-evident pattern is not repeated at the GAN and ILO sites; this may be due to the solar radiation data quality, which, in the case of ILO, may be unacceptable. Therefore, these two sites are excluded from further analysis. Moreover, the site of OHY presents some low SSD values for clear skies (i.e., for \(0\leq\rm k_{d}\leq 0.26\)), but the number of such values is small, and, therefore, this site is included in the analysis that follows.  
In each of the remaining 14 sites, the number of the (\(\rm k_{d}\),SSD) hourly pairs is maximum (8760 or 8784 for a non-leap or a leap year, respectively); these numbers include nighttime values, which were not taken into account. The analysis that follows concerns the number of daytime hourly pairs that represent the three sky condition statuses (overcast, intermediate, clear). Table 3 shows the results.  
Figure 16: Determination of clear, intermediate, and overcast skies for the (**a**) PAY and (**b**) REG sites.  
Figure 17: Determination of the \(\rm k_{du}\) (red line) and \(\rm k_{dl}\) (blue line) values for the (**a**) SON and (**b**) SOV sites.  
The differentiation in the total daytime values (column 5, Table 3) is due to the geographical location of the sites, which dictates the daylight pattern, i.e., the daylength. On the other hand, the variation in the SSD values under the three sky conditions is much influenced by the prevailing climatology at each site.
## 6 Example of Verification of Recommended Equations
### ABAQUS Analysis Results and Comparison  
Regarding the base composite joint, and considering the common column space of SMRFs, there were a total of nine specimens used in the ABAQUS analysis by changing the beam length \(L\) (see Table 4) or by changing the \(S_{0}\) (see Table 5). The ABAQUS analysis data and the calculation results of Equations (7) and (8) are listed in Tables 4 and 5.  
Figure 9: Details of the base composite joint in the ABAQUS analysis.  
[PERSON] et al. [29] reported that the concrete slab's effective width appeared to be substantially less than 1.3 times the width of the column flange when subjected to reversed loading; the effective width was approximately equal to the column flange width. In Tables 4 and 5, the \(b_{\text{e}}\) of ABAQUS under sagging bending is from 0.924 times (462 mm) to 1.134 times (567 mm) that of the column width (500 mm). These results show that the ABAQUS analysis in this paper is entirely accurate. These results also show that the effective width under the sagging bending moment is less affected by the \(S_{0}\) and maintains a constant within a specific range. Compared to the smaller effective width under the sagging bending moment, the effective width under the hogging bending moment is larger than the column width. The effective width under the hogging bending moment is greatly affected by the \(S_{0}\) and increases with the increase of \(S_{0}\).  
The data in Tables 4 and 5 show that the calculation results of Equations (7) and (8) are very inclusive and completely cover the effective width of the slab obtained by the ABAQUS analysis. However, compared to the calculation results of Equation (8), the calculation results of Equation (7) are closer to the ABAQUS analysis results. Therefore, it is feasible and safe to design composite joint specimens according to Equation (7) for experimental research.
## 6 Nanofertilizers
### Carbon-Based Nanomaterials in Plant Fortification  
Organic or carbon-based nanomaterials, including fullerenes, fullerols, single-walled carbon nanotubes (SWCNTs), and multi-walled carbon nanotubes (MWCNTs), have been used in crop production and they behave differently. Generally, carbon nanotubes (CNTs) are highly hydrophobic and do not dissolve in aqueous media, but form aggregates due to high van der Waals forces, while SWCNTS are hydrophilic and can hardly penetrate the cell walls of the plants because of their large sizes. Fullerol, which has been reported to be hydrophilic and of smaller size, can be transported into the plant tissues by apoplastic means [112]. On the other hand, fullerenes and MWCNTs can easily interact with the hydrophobic components of the natural organic matters and possibly find their way into plant tissues. However, they have been reported to block the pores of the cell wall of _Allium cepa_, making their translocation insignificant [113; 114; 115]. Overall, carbonaceous materials find their way into the plant tissues [116; 117].  
MWCNTs have been reported to enhance the seed germination rate of tomato plant and decrease cell concentration of cultured rice cell suspensions. However, there are no observable physiological change or toxicity when applied to crops such as cucumber, lettuce, corn, rape, wheat, etc. [114; 118]. [PERSON] et al. reported that there were no toxic effects associated with long-term exposure of crops to MWCNTs, rather there was positive impact on the general growth of the plants compared to the untreated crops [119]. In the case of single-walled carbon nanohorns (SWCNHs) used to assess the germination of six crops (rice, tomatoes, barley, soybean, corn, and switchgrass), [PERSON] et al. [112] found that, at higher concentration of the SWCNHs (100 ug/mL), all crops germinated except tomatoes. In contrast, at lower concentration (25 ug/mL) of the nanomaterials, the tomato crop showed the greatest rate of germination, while there was no result of early germination for soybean. Overall, no toxicity was recorded and the results showed that different plants respond differently to varying concentrations of the nanomaterials. To control some of the problems associated with the different carbon nanomaterials, they could be functionalized with magnetic nanoparticles, which delivers multiple functions of filling internal space with plant. This protects the chemicals and directs the movement of nanocarriers within the plant structure [94].
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Processing
#### 2.1.2 Multi-Purpose Cold Storages for Various Cooling Applications
However, no cold storage application in the food area was found. The market for the second group of technologies seems to be more diverse. Companies offering ice harvesters or so-called flake ice or slurry ice makers seem to be in large number. Slurry ice makers for example produce ice that has the consistency of slush or snow, being pumpable, thus applicable directly at the cold demand and with high cooling power, even directly used to cool food. Ref. [9] describes the use of ice slurry thermal energy storage for cheese process cooling, specifically a specialized application for a cheese plant in Hanford, California. Raw milk is purchased at local dairies and processed into cheddar cheese. There are several processes requiring product cooling, some are cooled over several hours of a day, others require nearly instantaneous product cooling. Because the cooling load profile varies significantly throughout a day, various types of ice TES systems were considered for load-leveling and electricity cost reduction, and also for offering backup cooling. Finally, in this case the slurry ice system was chosen because of its high ice-making efficiency, the expandability of the system to meet long-term loads, layout flexibility of the components, and the cost relative to the other options. Ref. [10] describe the use of heat storage for dairy, in Bergen, Norway. The system consists of several heat pumps connected to several thermal energy storages, for different purposes of cooling and heating, like pasteurizing and hot water; here the thermal energy storages chosen are all water tanks. Despite the commercial availability of a wide variety of cold storages using different technologies with distinct advantages, there is still need for R&D in several areas: storage materials, storage design, and system design. Ref. [11] present the design and performance of a TES consisting of a pillow plate heat exchanger immersed into a low-temperature phase change material (PCM) with phase change temperature of \(-\)9.6 \({}^{\circ}\)C as the storage medium. According to the authors, it is one of the first experimental investigations featuring a large-scale technical solution that allows for coupling the evaporation and condensation processes of the refrigeration system with the melting and solidification of a low-temperature PCM in the same heat exchanger. For many applications, specific storage solutions are developed, often integrated in the whole system. Such applications are discussed now.
# A Scientometric Review of Obstructive Sleep Apnea and Obesity
## 4 Discussion
### Cluster #6: OSA during Pregnancy  
The major citing articles in cluster #6 were authored by [PERSON] et al. [178], with a coverage of 17 articles and a GCS of 17; [PERSON] et al. [179], with a coverage of 17 articles and a GCS of 25; and [PERSON] et al. [180], with a coverage of 15 articles and GCS of 76. The main theme of cluster #6 is OSA during pregnancy, which can be observed from the citing articles [179; 181; 182; 183; 178; 184; 179; 177; 178; 179; 185] and the cited references [186; 187; 188; 189; 190; 191; 192]. Notably, the prevalence of OSA ranges from 15 to 20% in obese pregnant women [179]. From the literature, while pregnancy is associated with many risk factors for OSA (e.g., weight gain, gestational diabetes), pregnancy itself appears to be a vulnerable time window for the development and worsening of OSA [179]. In fact, physiological modifications to the upper airway during pregnancy play an important role in the development and worsening of OSA. However, pregnant women have often been under-recognised as a population at risk for breathing disturbances during sleep. Addressing this problem is crucial, as women with breathing disturbances during sleep have a higher risk of experiencing pregnancy complications and negative pregnancy outcomes. Furthermore, these women have higher risk for adverse medical outcomes (e.g., severe respiratory suppressions) and mortality even after delivery [PERSON] et al. [178; 179]. For example, [PERSON] et al. [190] found an independent association between hypertensive disorders in pregnancy and OSA. [PERSON] et al. [181] also outlined the cascading effects of intermittent hypoxia and endothelial dysfunction characteristic of OSA as mechanisms of this association. There are significant health implications for this association since hypertensive disorders of pregnancy are a major cause for maternal and neonatal morbidity [193; 194], as well as increased lifetime risk for cardiovascular disease in mothers. In the cluster, the cited references also revolve around postoperative outcomes for OSA, as discussed in cluster #43. Postoperative outcomes for OSA are a pertinent concern during pregnancy in the context of the Caesarean procedure during delivery (e.g., [195; 196; 197]), as well as diabetes (e.g., [198; 199; 190], another common condition during pregnancy. This is further reinforced by the finding that pregnant women with OSA have a higher risk of requiring a Caesarean delivery and the association between OSA and perinatal outcomes such as neonatal intensive care [189]. In terms of treatment, as for the general population, continuous positive airway pressure is the recommended approach in pregnant women with OSA [178]. However, it is worth noting that only a limited number of studies with small sample sizes have investigated the effect of continuous positive airway pressure in pregnant women with OSA [178].
## 2 Materials and Methods  
In this section, we present and explain the proposed system.  
In the Table 1 we present parameters and decision variables that are used to formulate the models in this paper:  
\begin{table}
\begin{tabular}{l l} \hline \(t\) & instant time. \\ \(\Delta t\) & time period length. \\ \(T\) & total simulation time. \\ \(S_{A}\) & stock capacity for new products. \\ \(S_{A}{}^{*}\) & optimal stock capacity for new products. \\ \(S_{Ar}{}^{*}\) & stock capacity for reconditioned products. \\ \(S_{Ar}{}^{*}\) & optimal stock capacity for reconditioned products. \\ \(s_{A}(t)\) & stock level of new products at time \(t\). \\ \(s_{Ar}(t)\) & stock level of reconditioned products at time \(t\). \\ \(s_{r}(t)\) & stock level of worn products at time \(t\). \\ \(u_{A}(t)\) & production rate of the machine \(M_{1}\) at time \(t\). \\ \(U_{A}\) & maximum production rate of the machine \(M_{1}\). \\ \(u_{Ar}(t)\) & production rate of the machine \(M_{2}\) at time \(t\). \\ \(U_{Ar}\) & maximum production rate of the machine \(M_{2}\) \\ \(d_{A}(t)\) & demand for new products at time \(t\). \\ \hline \end{tabular}
\end{table}
Table 1: Parameters and decision variables.  
The aim of our work is to find the optimal stock capacity for new products, \(S_{A}\)*, for reconditioned products, \(S_{A}\)*, and the percentage of the returned products, \(p\)*, that maximizes the total profit, \(F(t)\).
# Computational Thinking Measurement of CS University Students
## 4 Discussion
### RQ3: Do Students' Computational Thinking Skills and Programming Abilities Vary by University Level?  
In general, there were no significant differences between both courses. Although they were not statistically significant, changes can be seen between the first and fourth years for the programming concepts of repeat loops, functions, arrays, and matrices.  
When the study focused on Rey Juan Carlos University (URJC), in the case of CTScore, the median increased slightly in the fourth year, while the dispersion also clearly increased. In the case of CTProg, the median also increased from the first to the fourth year, and the dispersion in the data increased again. Also, very little change occurred in the medians, depending on the course, except in the algorithmic thinking skill from CTScore. In the case of the programming variables, the median increased in the conditionals and repeat loops and decreased in the matrices.  
When the study focused on Atlantic Technological University (ATU), it was observed that, although the difference between medians was not statistically significant, the medians of the two variables, CTScore and CTProg, lowered their values from the first to the fourth year. The dispersion, in the case of CTScore, increased a lot, while remaining similar in CTProg. Very little change was seen in the medians, depending on the course, except in the case of the CT skills of abstraction, where it decreased, in creative thinking, where it increased slightly, and in pattern recognition, where it decreased. Regarding, the programming concepts, in the case of repeat loops and arrays, the median increased.  
RQ4: Do Students' Computational Thinking Skills and Programming Abilities Vary by University Location?  
There existed a significant difference by university in the two global scores in CTScore and CTProg. This significant difference was manifested in significantly higher values on average (almost two points of difference in both the mean and median), with the dispersion being lower at URJC. Also, the median values of the variables studied were greater or equal, in all cases, at URJC.
## 4 Evaluation of the Coordination of Three Types of Asphalt Pavement Structures  
### Definition  
The sensitivity is the ratio of the mechanical response value (including stress and defection) of a point in the Y direction of the YZ section to the maximum mechanical response value in the Y direction of the section, and is calculated as in Equation (1). This study mainly analyzes stress sensitivity and deflection sensitivity.  
\[\omega_{\text{i,h}}=\frac{\delta_{\text{i,h}}}{\delta_{\text{max,h}}}\times 100\% \tag{1}\]  
In Equation (1), \(\omega_{\text{i,h}}\) is the mechanical index sensitivity of point i at depth h; \(\omega_{\text{i,h}}\) is dimensionless; h is the depth along the Z direction and the unit is m; \(\delta_{\text{i,h}}\) is the mechanical response value of point i at depth h; and \(\delta_{\text{max,h}}\) is the maximum mechanical response value of all points at depth h.  
**Example 1**.: _Calculation of the deflection sensitivity of point 20 in the road surface (Z = 0 m)._  
Given data: \(\delta_{20,0}=16.23\) (deflection, 0.01 mm), \(\delta_{\text{max,0}}=\ \delta_{7,0}=17.95\) (deflection, 0.01 mm)  
\[\omega_{\text{i,h}}=\frac{\delta_{\text{i,h}}}{\delta_{\text{max,h}}}\times 100 \%=\omega_{20,0}=\frac{\delta_{20,0}}{\delta_{\text{max,0}}}\times 100\%= \frac{16.23}{17.95}\times 100\%=90.4\% \tag{2}\]  
Therefore, the deflection sensitivity of point 20 in the road surface is 90.4%.  
**Example 2**.: _Calculation of the transverse stress sensitivity of point 15 in the bottom of the lower surface layer (Z = 0.13 m)._  
\[\omega_{\text{i,h}}=\frac{\delta_{\text{i,h}}}{\delta_{\text{max,h}}}\times 100 \%=\omega_{20,0}=\frac{\delta_{15,0.13}}{\delta_{\text{max,0.13}}}\times 100 \%=\frac{-0.05427}{-0.2766}\times 100\%=11.5\% \tag{3}\]  
Given data: \(\delta_{15,0.13}=-0.05427\) (stress, MPa), \(\delta_{\text{max,0.13}}=\delta_{7,0.13}=-0.2766\) (stress, MPa).  
Therefore, the transverse stress sensitivity of point 15 in the bottom of the lower surface layer is 11.5%.  
According to the definition: (1) at point imax of the maximum mechanical response value, \(\omega_{\text{imax}}=100\%\), and at point i\(\infty\) at infinity, \(\omega_{\infty}=0\); (2) for different types of pavement structure, the greater the sensitivity of deflection, the stronger is the rigidity of pavement structure; and (3) the greater the stress sensitivity, the more uniform is the stress distribution, and the higher is the material utilization efficiency.