esa-sceva/satcom-chunk-collection huggingface dataset:
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 4 Adaptability of the Formulation
### Foundational Justification of Equations (22) and (23)  
#### 4.3.1 Derivation of Extended Entropy \(\overline{S}\)  
The extended entropy \(\overline{S}\) in Equation (8) is derived from the **maximum entropy principle** under constraints reflecting mechanical and thermodynamic variables. Starting with the Gibbs entropy:  
\[S=-k_{B}\int\rho\ln\rho\,d\Omega, \tag{55}\]  
we impose constraints for energy, momentum, and angular momentum conservation using Lagrange multipliers:  
\[\overline{S}=-k_{B}\int\rho\ln\rho\,d\Omega+\lambda\left(E-\sum_{a}\left\langle E ^{(a)}\right\rangle\right)+\mathbf{a}\cdot\left(\mathbf{P}-\sum_{a}\left\langle \mathbf{p}^{(a)}\right\rangle\right)+\cdots \tag{56}\]  
Varying \(\rho\) and solving \(\delta\overline{S}=0\) yields:  
\[\boxed{\overline{S}=\sum_{a=1}^{N}\left[S^{(a)}\left(E^{(a)}-\left[\text{ mechanical terms}\right]\right)+\left(\mathbf{a}\cdot\mathbf{p}^{(a)}+\mathbf{b}\cdot(\mathbf{r}^{(a)} \times\mathbf{p}^{(a)})+J^{(a)}\right)\right].} \tag{57}\]  
This aligns with [PERSON] formalism for constrained statistical mechanics [39].
## 3 Results and Discussion
### Aerodynamic Characteristics of DU Airfoil with VG  
Based on the results of the CFD analysis, the cross-sectional DU airfoil in the region where flow separation occurs predominantly was selected as the test airfoil for VG application analysis. Figure 9 shows the surface grid of the VG-equipped airfoil used for simulation, and Table 5 contains the number of grids that were used.  
The chordwise velocity contours of the airfoil with and without the VG are shown in Figure 10a,b. The results of the CFD analysis confirm that, in the case of the airfoil without the VG, the fluid did not accelerate along the suction surface and flow separation occurred; however, in the case of the airfoil with the VG, the fluid accelerated along the suction surface and flow separation did not occur. When flow separation occurs on the suction side, a low-pressure region is formed in the wake, which lowers the lift coefficient and increases the drag coefficient. As shown in Figure 11a,b, when the VG is attached, the airfoil experienced a high lift force at an angle of attack of 5.9\({}^{\circ}\) or higher and a low drag force at an angle of attack of 14.8\({}^{\circ}\) or higher.  
Figure 8: Separated flow structure near the root.  
Figure 7: Vortex flow structure near the tip.  
Figure 11: Lift and drag coefficient vs. angle of attack: (a) lift coefficient; (b) drag coefficient.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Area** & **Volume (-)** & **Number of Elements (EA)** \\ \hline \hline \multirow{2}{*}{VG airfoil} & VG & 1,932,483 \\  & Region surrounding the VG & 3,924,000 \\ \multirow{2}{*}{Total} & Total & 5,856,483 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of elements for the numerical analysis of the VG-mounted airfoil.  
Figure 10: Cross-sectional velocity contour of the blade: (a) without VG; (b) with VG.  
Figure 9: Surface mesh of VG-mounted airfoil extruded in the span direction.
# Analysis of the Landfill Leachate Treatment System Using Arima Models: A Case Study in a Megacity
## 2 Materials and Methods
### Information Collection  
Daily water samples at the inlet and exit of the leachate treatment plant over a 12-year period (2003-2015) were collected. The parameters analysed were as follows: BOD\({}_{5}\), COD, pH, N-NH\({}_{4}\), Cd, Zn, Hg, As, Cu, Fe, Pb, Co, Cr, and Ni. The materials used during each sampling were as follows: digital field pH-meter (Hanna Instruments E-316), preservatives, amber bottles, microbiological bottles, refrigerator, buffer solutions, distilled water washing vessel, and a bucket. During each sampling, a bucket was initially filled with leachate, which was then removed to purge it. Subsequently, the bucket was filled again with leachate to obtain the corresponding sample. The pH-meter was calibrated with buffer solutions and measured in situ. Two aliquots were then packed in amber bottles of 1000 mL each. An aliquot was also packaged in a 100 mL microbiological sampling bottle, ensuring that there was no oxygen left in it. The containers were taken to a refrigerator, where they were kept at a temperature below 4 \({}^{\circ}\)C for later transport and laboratory analysis. The leachate samples collection followed the guidelines established by the Standard Methods for the Examination of Water and Wastewater [31].  
Additionally, daily information on the quantity of MSW deposited in the study area was collected. This information was obtained using a scale installed at the entrance of the landfill. Lastly, daily rainfall and temperature information was collected from a weather station (4\({}^{\circ}\)28\({}^{\prime}\)52.7\({}^{\prime\prime}\) N; 74\({}^{\circ}\)7\({}^{\prime}\)34.6\({}^{\prime\prime}\) W) administered by the Institute of Hydrology, Meteorology, and Environmental Studies of Colombia (IDEAM). This monitoring station was located 2.29 km from the MSW disposal area under study.
## 4 Discussion
### Limitations and Future Research  
In this study, we introduced an efficient train fault diagnosis model and an extended train fault visual analysis method. To examine the real-time effects and accuracy of the method, applications in fault prediction and diagnosis of key components such as gearboxes, bogies and pantographs will be explored in the follow-up research, to provide a highly feasible implementation scheme for fault diagnosis and predictive maintenance. As a novel fault diagnosis and visualization method, there were some limitations in our study, which we plan to address in our future work.  
Regarding the fault diagnosis model, we are currently using the DNN network composed of stacked auto-encoders. In the single SAE model, since the network input is the sensor sampling data, we used a full connection layer in the model. When prolonging the sampling time or increasing the sampling frequency, the dimension of input data parameters will inevitably increase, which will lead to difficulties to the network training. It is possible to transform the SAE internal network structure and reduce the dimension of high-dimensional data and network parameters (such as using a combination of CNN network and SAE), which is one of our future research directions. For fault visual analysis, as an extension of fault diagnosis, analysis of the whole fault type dimension can assist people in quickly analyzing fault causes. In the case of small-scale data sets, our current method can effectively analyze train faults, fault causes and correlations between faults; however, for large-scale data, it is a great challenge to display global data in a limited view space. Therefore, we need to conduct further study of multi-view layout algorithms and graph data visual analysis methods.
## 3 Results and Discussion  
### Effect of Soil Salinity on Biomass Yield and Sodium Content in F. arundinacea  
Our research provided novel solutions in the field of bioremediation of naturally or re-salinized agricultural land with the use of native facultative halophyte--_Festuca arundinacea_. In the first year of cultivation (2020) on saline soil, the yield of tall fescue hay was obtained at the level of 3.9 Mg\(\cdot\)ha\({}^{-1}\) (one cut), while in the following year, three harvests were performed (25 May, 15 July, and 22 September) with a total yield of 14.8 Mg\(\cdot\)ha\({}^{-1}\) (6.24, 4.29 and 4.24 Mg\(\cdot\)ha\({}^{-1}\), respectively). In Central Europe, the crop of _F. arundinacea_ is usually harvested three times a year (May, July and September), but the total annual yield and individual cuts may vary significantly depending on the year of cultivation, weather conditions, soil abundance, fertilization level, etc. Field experiment conducted in Mydlniki near Cracow (Southern Poland) on non-saline soil in 2004-2006 showedan annual dry matter yield of tall fescue (three cuts) of about 8-10 Mg\(\cdot\)ha\({}^{-1}\)[38]. A high yield of tall fescue was obtained in the cultivation located on the organic soil in the Randow river valley (Northeast Germany). The meadow sward was dominated by _F. arundinacea_ 'Fawn', and its share was 100% in the first and second year of cultivation and 99% in the fourth year. In the first year of cultivation (after seed sowing at first decade of September), three cuts were harvested, while in the second and fourth year--4 and 5 cuts, respectively. In these years, total annual yield of _F. arundinacea_ dry matter was 12.97, 14.40 and 17.43 Mg\(\cdot\)ha\({}^{-1}\)[39]. In the conditions of the Czech Republic, dry matter yield of a mixture of _Lolium perenne_ and _F. arundinacea_ (first cut in June) ranged from 5.3 to 12.7 Mg\(\cdot\)ha\({}^{-1}\) depending on growing season as well as the sowing dates (June, September or October) and seed rate (10 or 20 kg\(\cdot\)ha\({}^{-1}\)) [40]. In our investigations, in the second year of field cultivation (2021), tall fescue formed dense turf without weeds (Figure 1). The yield of above-ground plant parts harvested from the plots along the soil salinity gradient varied within a wide range from 1.75 to 2.90 kg\(\cdot\)m\({}^{-2}\) and from 527.85 to 902.48 g\(\cdot\)m\({}^{-2}\) for fresh and air-dry biomass, respectively. Biomass loss after air-drying averaged 70.64%, falling within a relatively narrow spread of 68.73 to 74.09%. In turn, the average moisture content in the air-dry biomass of tall fescue reached 10.56% (Table 1). The approximate air-dry biomass yield estimated on this basis was 6.9 Mg\(\cdot\)ha\({}^{-1}\).
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 3 Simulation Results and Discussion
### Effect of Interference AOA on Measurements  
The relative direction between jammer and satellite signals plays a critical role in anti-jam applications using antenna array processing. The effect of changing the AOA of the interference source is therefore analyzed. It is assumed that only one chirp interference source is present. Time series plot of the pseudorange and carrier phase measurements in the presence of chirp interference for PM and MPDR are shown in Figure 10. The interference direction is changed after 65 s. For the initial 65 s, the interference is located at (elevation = 30\({}^{\circ}\), azimuth = 10\({}^{\circ}\)) and then changed to (elevation = 30\({}^{\circ}\), azimuth = 225\({}^{\circ}\)). It can be observed that the carrier phase biases observed in PM changes when the interference direction changes. However, MPDR beamformer does not induce any biases in the carrier phase measurements, irrespective of the AOA of the interference. For the initial 65 s, interference location was near PRN 10 and due to this, the standard deviation of the pseudorange measurements for PRN 10 was high. After 65 s, the interference direction was away from PRN 10 and the standard deviation of the pseudorange measurements decreased.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Position Errors**} & \multicolumn{2}{c}{**PM**} & \multicolumn{2}{c}{**MPDR**} \\ \cline{2-5}  & **Mean** & **Standard Deviation** & **Mean** & **Standard Deviation** \\ \hline East error (cm) & 3.2 & 1.1 & \(-\)0.001 & 0.003 \\ North error (cm) & 10.6 & 3.3 & 0.002 & 0.005 \\ Height error (cm) & \(-\)1.4 & 6.8 & \(-\)0.005 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Carrier phase position errors with PM and MPDR.  
Figure 9: Carrier phase position errors with PM and MPDR in the presence of chirp interference (Processed using RTKLib open source software).
## 5 Discussion
### Future Research  
Through a literature review, on-site interviews, and accident case analysis, 12 factors influencing college students' emergency cognitive ability were identified, and an ISM model was constructed along with MICMAC analysis. However, this study has some limitations. The formation of emergency cognitive ability in college students is a complex issue influenced by multiple factors, and it is not feasible to include all influencing factors in the model. Additionally, this study used grounded theory to identify these factors and employed ISM and MICMAC to analyze their relationships, providing a useful analytical framework. Future research will build on this study to further explore and expand pathways for developing college students' emergency cognitive ability.  
(1) Expanding sample diversity. Future research could broaden the scope from current limited samples to a national or even cross-cultural scale, incorporating participants from different ages, academic backgrounds, and regions to increase the generalizability of findings. Collaborating with multiple universities or educational institutions would help access a more diverse sample.  
(2) Integrating smart educational tools. Future research could explore the application of big data and AI technologies in emergency cognitive education to enhance precision and effectiveness. For example, learning management systems (LMS) could track students' learning progress and, combined with intelligent analysis tools, provide personalized feedback. Methods might include developing smart learning systems or mobile applications to monitor learning progress in real time and offer instant feedback and resources. Experimental studies could further assess the effectiveness of these technologies in improving emergency cognitive ability.  
(3) Expanded application of the ISM-MICMAC model. Through this study, we have validated the effectiveness and advantages of the ISM-MICMAC model in analyzing the factors influencing college students' emergency cognitive ability. In future research, this model can be further applied, particularly to improve other educational strategies in higher education. For instance, beyond cognitive ability, the model could be used to analyze factors affecting students' disaster preparedness, health management, and crisis response capability. By incorporating real-world case studies and teaching practices, future studies could explore how the ISM-MICMAC model can enhance frameworks for disaster preparedness and health education, ultimately improving students' overall crisis response skills.
# A Proposal for a Processing Line for Cauliflower and Broccoli Florettling
## 6 Summary  
The designed processing line for cauliflower and broccoli floreting will consist of two main devices, a vegetable crusher, and a calibrator. They can be used separately or as parts of the line. In the second scenario, to ensure the continuity of the process, minimum three conveyor belts and five containers (for whole heads and fractions of leaf fragments, stalks, excessively crushed florets, and appropriately sized florets) are required.  
The unit will meet the requirements of small-scale agricultural producers because it is expected to be inexpensive, and it does not occupy a large area, especially when the crusher and calibrator are used separately.  
The technological process of cauliflower and broccoli floreting with the proposed unit will consist of the following stages: whole inflorescences are picked from the container by the operator, stalk diameter is evaluated and one of the two conical knives is selected, the inflorescence is pushed onto the knife and successive florets are separated; the apical  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Value of Parameter**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{1}{c}{**Coefficient of**} \\ \cline{2-5}  & **Minimum** & **Maximum** & **Average** & **Deviation of** & **Parameter** \\  & **Minimum** & **Maximum** & **Average** & **Parameter** & **Variability (\%)** \\ \hline Mass of excessively crushed florets (g) & 3.0 & 148.1 & 33.3 & 31.0 & 93.0 \\ Mass of oversized florets (g) & 0 & 139.2 & 4.8 & 25.9 & 538.5 \\ Mass of appropriately sized florets (g) & 119.0 & 604.6 & 290.6 & 114.7 & 39.5 \\ Number of appropriately sized florets (-) & 7 & 32 & 15 & 4.9 & 32.5 \\ Floret width (mm) & 20 & 60 & 38 & 9.2 & 24.1 \\ Floret thickness (mm) & 13 & 58 & 33 & 8.8 & 26.6 \\ Floret height (mm) & 11 & 62 & 32 & 8.1 & 25.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical distribution of floret parameters.  
floret is removed by making a horizontal cut with an additional knife, and the stalk falls into a container under the processing table; leaf fragments are separated and placed in a separate container, and the florets are moved to the crushing chamber where large florets are broken into smaller pieces; the raw material is transported by the conveyor belt to another container or it is fed into a hopper in the calibrator via an additional conveyor. Since screens with two different pore sizes will be used, the raw material will be divided into three fractions: florets that are too small, appropriately sized florets (with a diameter of 2 to 6 cm) and florets that are too large which can be redirected to the crushing chamber of the vegetable crusher.
# Optimization Strategies in Quantum Machine Learning: A Performance Analysis
## 5 Comparative Analysis
### COBYLA Optimizer  
Using COBYLA, a gradient-free optimizer, with a maximum of 100 iterations, we obtained the following confusion matrix. Since COBYLA is gradient-free, a learning rate is not applicable.  
\[\begin{bmatrix}25&4\\ 1&31\end{bmatrix} \tag{6}\]  
True positive (TP): Twenty-five instances were correctly predicted as positive.  
False positive (FP): Four instances were incorrectly predicted as positive.  
False negative (FN): One instance was incorrectly predicted as negative.  
True negative (TN): Thirty-one instances were correctly predicted as negative.  
Using the confusion matrix values, the performance metrics for the COBYLA optimizer were calculated as follows:  
\[\text{Accuracy}=\frac{31+25}{31+4+25+1}=0.918=92\%\]  
\[\text{Precision}=\frac{31}{31+4}=0.886=89\%\]  
\[\text{Recall (Sensitivity)}=\frac{31}{31+1}=0.969=97\%\]  
\[\text{F1 score}=2\times\frac{0.886\times 0.969}{0.886+0.969}=0.925=93\%\]  
Additionally, the model trained using the COBYLA optimizer achieved these results in just 1 min, making it the most computationally efficient optimizer.
# Metabolism and Regulation of Ascorbic Acid in Fruits
## 5 Effects of Hormones on the Ascorbic Acid Accumulation in Fruits
Postharvest cherry tomato fruits treated with MeJA contain significantly higher AsA and carotenoids, especially lycopene [139]. MeJA treatment could inhibit the activity of AO and increase the activity of DHAR, thereby increasing the content of AsA in loquat fruit and delaying the occurrence of internal browning caused by chilling injury [140]. MYC2 acts as a regulatory center of JA signaling and is involved in cold resistance in many horticultural crops [141]. _MYC2_ participates in the AsA-GSH cycle and regulates the cold tolerance of tomato fruit by regulating the accumulation of AsA [142]. Treatment with 0.25 mM MeJA can significantly increase 'Kumato' tomato fruit yield and AsA content [143]. The treatment of pomegranate and blueberry fruits with MeJA can also increase the AsA content and total antioxidant activity in the fruits and improve the fruits' antioxidant capacity and storage stability [144; 145]. In addition, MeJA pretreatment effectively prevents wound-induced loss of AsA and organic acids and the deterioration of the flesh color of freshly cut pitaya fruits [146]. Melatonin (MT), a hormone found in the pineal gland, has also been found in plants. As a new research-hot phytohormone, melatonin plays a vital role in scavenging reactive oxygen species and improving the resistance of plants to environmental stress [147]. Little research has been conducted on the relationship between melatonin and AsA. A 100 \(\upmu\)M melatonin treatment increases the levels of AsA and delays senescence in sweet cherries [148]. After treatment of pomegranate fruit with 100 \(\upmu\)M melatonin, the activities of APX and GR are increased and the activity of AAO is decreased, resulting in a higher accumulation of AA and GSH and improved resistance of the fruit to cold [149]. As well as being essential antioxidants, there may also be a deeper relationship between melatonin and AsA.
# Chemical Diversity of Mediterranean Seagrasses Volatilome
## 2 Materials and Methods
### Gas Chromatography-Mass Spectrometry (GC-MS) Analyses  
Analyses were performed on a GC-MS instrument (7890B GC, Agilent Technologies(r), Santa Clara, USA) equipped with an HP5-MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m; J&W Agilent Technologies(r), Santa Clara, CA, USA) coupled to a mass spectrometer (MSD5977A, Agilent Technologies(r)). The HP-5 MS column, commonly paired with SPME fibers in studies, is recommended for complex volatile mixtures due to its excellent resolution across various polarities and molecular weights. The GC-MS-temperature program has been adapted from [72]. Thermal desorption of the fibers was directly carried out to the GC column through the injector for 15 min at 250 \({}^{\circ}\)C in splitless mode. The gradient temperature was initially set at 70\({}^{\circ\circ}\)C (2 min), then reached 200\({}^{\circ}\)C at 3\({}^{\circ}\)C min\({}^{-1}\), and finally reached 315 \({}^{\circ}\)C at 15 \({}^{\circ}\)C min\({}^{-1}\). Helium was used as a carrier gas with a constant flow of 1 mL min\({}^{-1}\). The EI mode was operated at 70 eV and the mass range was 40-450 amu. The identification of VOCs was based on the comparison of their retention indices (RIs), determined using the retention times of a series of \(n\)-alkanes (C\({}_{8}\) toC\({}_{20}\)), and on a spectral match with the NIST20 mass spectral libraries. To confirm the identity of certain terpenes, several standards were injected (\(\beta\)-cyclocitral, \(\alpha\)- and \(\beta\)-citrals, geranylacetate, and \(\alpha\)- and \(\beta\)-ionones). The relative abundance, that is, the relative percentage of each compound per sample, was calculated. The final table of all detected BVOCs, including relative abundance per compound and per species for each compound and classified according to the metabolomic pathways (benzenoid, fatty acid derivative, furanoid, sulfur-containing compound, terpenoid, and unknown), is available in Supplementary Information (Table S1). Compounds only found in one sample (singleton compound) and all compounds detected in blanks were removed from the final table.