esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Model Material for Dynamic Geotechnical Model Tests
## 4 Experimental Results and Sensitivity Analysis
### Engineering Geology Overview  
The selected rock slope was located in the section from Shouwang (Yunnan-Guizhou boundary) to Hongshan (Yunnan-Sichuan boundary) on the highway from Duyun to Shangri-La of G7611, with complex geological conditions and a slope of 40\({}^{\circ}\). The bedrock was sandstone, and the occurrence of the rock was 303\({}^{\circ}\)/15\({}^{\circ}\). The rock level was almost the same as the slope facing surface, which was a bedding slope. Two groups of joint fractures developed in the rock: joint 1 at 50\({}^{\circ}\)/270\({}^{\circ}\) and joint 2 at 160\({}^{\circ}\)/278\({}^{\circ}\). According to the Chinese Ground Motion Parameter Zoning Map GB18306-2015, the peak acceleration of ground motion in this area was 0.15 g, corresponding to the basic earthquake intensity of VIII, and the ground motion reflected the period of the Put sign of 0.45 s. The physico-mechanical parameters for sandstone samples collected from this site are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Indicators** & \(\rho\)/g\(\cdot\)cm\({}^{-3}\) & \(\sigma_{t}\)/MPa & \(\sigma_{c}\)/MPa & \(c\)/MPa & \(\varphi\)/\({}^{\circ}\) & \(\mathbf{G_{d}}\)/GPa & \(\mathbf{E_{d}}\)/GPa \\ \hline F & 27.28 & 41.35 & 53.02 & 28.60 & 30.10 & 53.57 & 51.76 \\ Sig & \(7.52\times 10^{-8}\) & \(2.12\times 10^{-9}\) & \(2.29\times 10^{-10}\) & \(5.06\times 10^{-8}\) & \(3.30\times 10^{-8}\) & \(2.08\times 10^{-10}\) & \(2.85\times 10^{-10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Significance analysis table of the regression equation.  
According to the values of the physico-mechanical parameters of the model materials, combined with the relevant fitting formula and sensitivity analysis results of each factor, MATLAB programming was used to calculate the optimal ratio of components in the model material using the following formula:  
\[S=\sum_{n=1}^{7}\biggl{(}\frac{y-y_{0}}{y_{0}}\biggr{)}^{2} \tag{18}\]  
where \(y_{0}\) represents the value of the required physico-mechanical parameter, \(y\) represents the value of the fitting formula with different proportions of components, and \(S\) represents the error between the fitting value and the actual value with different proportions of components. When \(S\) is the smallest, the optimal regression coefficient value can be obtained. Percentage integer digits are reserved for convenient configuration. Through MATLAB calculations, the results for the proportions were as follows: cementing material/solid material 5%, gypsum/cementing material 76%, quartz sand/aggregate 28% and ferric powder/(barite powder + ferric powder) 64%. The proportion of each material in the solid material is shown in the table below. The physico-mechanical parameters of the rock samples were obtained by splitting, uniaxial and shear tests.
## 2 Materials and Methods
### Data Analysis  
For the purpose of the study, for each patient, two CK-SBRT plans with virtual fiducials tracking were generated in silico using Accuray Precision(r) TPS (Accuray Inc., Sunnyvale, California, USA). Two different isodose prescriptions lines were tested, 95% and 85% (CK-85% and CK-95). More in detail, the prescription isodose line can be defined as the ratio of prescription dose and maximum dose (i.e., prescribing to the 85% or 95% isodose lines means normalizing the prescription dose to the 85% or 95% isodose surface, respectively, that includes the target volume).  
CTsim and contours, including PTV-p and PTV-d with the same margins, were imported from UH-DWA plans, and the same dose prescription was used for both CK plans.  
Comparison between the three treatment approaches focused on the following: (i) PTV coverage metrics, (ii) homogeneity index (HI), (iii) conformity index (CI), and (iv) OARs dosimetric parameters. HI was calculated as (D2%-D98%)/D50%, while CI was calculated  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Organ at Risks** & **Objective** \\ \hline Rectum & \\ V18 Gy & \textless{}50\% \\ V29 Gy & \textless{}20\% \\ V33 Gy & \textless{}10\% \\ V36.25 Gy & \textless{}5\% \\ Rectum Posterior Wall & \\ Dice & \textless{}17 Gy \\ Anal Canal & \\ Dmean & \textless{}15 Gy \\ Bladder & \\ V36.25 Gy & \textless{}5\% \\ V36.25 Gy & \textless{}5 CC \\ V18 Gy & \textless{}40\% \\ Femoral Heads & \\ V15 Gy & \textless{}5\% \\ Bowel Cavity & \\ V30 Gy & \textless{}1 CC \\ Dmean & \textless{}5.4 Gy \\ V17 Gy & \textless{}195 CC \\ Penile Bulb & \\ V29 Gy & \textless{}50\% \\ Penis & \\ V13 Gy & \textless{}1 CC \\ Testis & \\ D20\% & \textless{}2 Gy \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the OARs dose constraint objectives [34].  
Figure 1: Ultra-hypoffractionated stereotactic body radiation therapy dose prescription scheme.  
as V95%/volume. Beam on time was considered, as well. Median dose values across all patients and their respective interquartile ranges (IQR) were reported. Statistical analyses were performed using the Friedman test and the Wilcoxon signed-rank test to assess statistical differences between the techniques. The significance level of multiple comparisons was set as \(p\) < 0.05/3, determined by Bonferroni correction for the Friedman test, and as \(p\) < 0.05 for the Wilcoxon signed-rank test.
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 2 Food Quality Indicators
### Oxygen and Carbon Dioxide  
Regulating the contact between oxygen and carbon dioxide in the context of food is important to avoid degradation and proliferation of micro-organisms. Oxygen can, indeed, trigger browning and other oxidative processes in meat, fish, cheese, and pigments in a variety of fresh foods [32; 33; 34]. Conversely, carbon dioxide inhibits the growth of bacteria and fungi, owing to both its capability to establish an anaerobic environment and decrease the pH of the environment of food [35; 36]. Therefore, controlling the amount of O\({}_{2}\) and CO\({}_{2}\) represents an important strategy to extend the shelf-life of food and several modified atmosphere packages have been developed for non-respiring food products (i.e., products in which the initial atmosphere composition must be controlled to ensure optimal preservation) [37; 38]. As a consequence, monitoring any change in the gas concentration and/or composition may indicate the conservation state of food [39; 40; 41].
## 4 Conclusions  
In this paper, THz spectra of 2,4-D were obtained by using THz-TDS, and its spectral characteristics were analyzed. It was found that there were three distinct characteristic absorption peaks of 2,4-D at 1.36, 1.60, and 2.38 THz, respectively. Combined with DFT, single molecule of 2,4-D geometry optimization and frequency vibration calculation were conducted. It was shown that the theoretical calculation results were in good consistency with the experimental results, except the absorption peak at around 1.93 THz. Then, we demonstrated the feasibility of THz-TDS in the quantitative detection of 2,4-D in Zizania latifolia. The absorption peaks at 1.36 THz could be used as the main peak for 2,4-D quantitative analysis. The limit of detection was lower than 5%. The fitting curve between 2,4-D in Zizania latifolia concentration and absorbance achieved a good linear regression relationship. This work enriches the fingerprint database of pesticide molecules on the basis of terahertz spectroscopy and can provide a technical support for the detection of 2,4-D in food by terahertz spectroscopy. However, the detection of limit is not low enough, and it may be optimized by improving instrument performance and designing suitable signal enhancement materials in the future.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/11/2248/s1](http://www.mdpi.com/2076-3417/9/11/2248/s1)]([http://www.mdpi.com/2076-3417/9/11/2248/s1](http://www.mdpi.com/2076-3417/9/11/2248/s1)), Table S1: Thickness measure of tablet under different pressure.  
**Author Contributions:** The work presented here was carried out in collaboration between all authors. [PERSON] and [PERSON] conceived the idea. [PERSON], [PERSON] and [PERSON] co-worked on associated data collection and carried out the experimental work. [PERSON] drafted the manuscript, [PERSON] and [PERSON] provided their experience and co-wrote the paper. All authors contributed to, reviewed and improved the manuscript.  
**Funding:** This work was supported in part by National key research and development plan (2018 YFD0700704).  
**Acknowledgments:** We would like to thank [PERSON] from Zhejiang University for his experimental guidance and help.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**Actual Concentration (\%)** & **Predicted Concentration (\%)** & **Recovery (\%)** \\ \hline
17 & 16.40 & 96.47 \\
23 & 21.46 & 93.29 \\
27 & 25.72 & 95.26 \\
33 & 32.59 & 98.75 \\ \hline \end{tabular}
\end{table}
Table 2: Recovery of 2,4-D detection in Zizania latifolia.  
Figure 5: Fitting curve between 2,4-D concentration and absorbance at 1.36 THz.
## 3 Results and Discussion  
### Synthesis  
Schiff bases **4a-c** under study were synthesized from the reactions of 1-(un)alkyl-benzimidazol-2-yl-amine with heteroaromatic aldehydes **3a-c** using two different synthetic approaches, as shown in Figure 1.  
The first synthetic approach follows the recommended methodology by [PERSON], [PERSON] et al. [21] for the synthesis of azomethines, involving the reflux of the amine with the corresponding heterocyclic aldehyde in a 5:1 mixture of anhydrous ethanol and benzene, in the presence of a catalytic amount of glacial acetic acid, for 8 h.  
In contrast, the second approach [22] was carried out at room temperature (~20 \({}^{\circ}\)C) in ethanol with 5% aqueous sodium hydroxide, with a reaction time of only 2 h. The target compounds **4a-c** were obtained in comparable yields (69-76%), regardless of the difference in the reaction conditions used. Adding a 5% aqueous NaOH solution to the reaction mixture proved to be a more efficient approach, allowing the reaction to proceed smoothly at room temperature with a significantly shorter reaction time.  
For the preparation of hydrazones **6a-c**, 2-hydrazino-1\(H\)-benzimidazole **5** was reacted with the respective heterocyclic aldehydes **3a-c** in ethanol under reflux [1].  
The spectral characteristics and elemental analysis results detailed in the Experimental section are consistent with the proposed structures of the azomethines **4a-c** and the hydrazones **6a-c**. The IR spectra of compounds showed a strong absorption band at 1582-1616 cm\({}^{-1}\) characteristic of the group C=N. The presence of the imine proton was confirmed by the \({}^{1}\)H NMR spectra of all methanamines **4a-c**, which display a one-proton singlet at \(\xi\) 9.54-9.60 ppm, while in the \({}^{1}\)H NMR spectra of hydrazones **6a-c** the imine proton resonates at \(\xi\) 7.96-8.59 ppm. A one-proton singlet at 12.58 ppm was assigned to the imidazole group NH of **4a**. The NH protons from the benzimidazole heterocyclic appear in the \({}^{1}\)H NMR spectra of hydrazones **6a-c** as a broadened singlet at 11.32-11.72 ppm. The  
Figure 1: Synthesis of the target benzimidazole-2-yl-methanimines **4a-c** and 1\(H\)-benzimidazol-2-yl hydrazones **6a-c**. Conditions: (i) 145–150 \({}^{\circ}\)C, 5 h in ampoule [20]; (ii) 99% NH\({}_{2}\)NH\({}_{2}\), reflux 3 h [1]; (iii) abs. ethanol, cat. 100% CH\({}_{3}\)COOH, reflux 8 h; (iv) 5% aq. NaOH, ethanol, ~20 \({}^{\circ}\)C, 2 h; (v) abs. ethanol, reflux, 2–4 h.  
product identification of hydrazzone derivatives is described in more detail in a previous publication [1].
## 2 Evolution, Virulence and Pathogenicity of _E. coli_ O157:H7
### Transmission of E. coli O157:H7 to Humans and Animals
#### 2.1.2 Contaminated Water as a Transmission Vector  
The transmission of _E. coli_ O157:H7 through water has been documented in various settings, including both drinking water that has been contaminated [56] and recreational water bodies [57; 58]. Furthermore, studies conducted in Nigeria and South Africa demonstrated that water used for irrigation had notable influence on the dissemination of _E. coli_ O157:H7 through the contamination of food products [59; 60].  
Untreated sewage released from hospitals, farms, and residential areas containing _E. coli_ O157:H7 into nearby water bodies also increases the risk of human infections significantly [61]. Based on the evidence, _E. coli_ O157:H7 easily survives in water and may persist for several weeks and even longer [58]. Strong rainstorms may cause sediments to be re-suspended, which could lead to an abrupt rise in _E. coli_ O157:H7 concentrations in the water [62]. [PERSON] et al. [58] reported the detection of _E. coli_ O157 in urban recreational water and provided evidence of the correlation between contact with these water bodies through activities such as swimming, boating, bathing, and sailing and the development of infections. In addition, an outbreak of _E. coli_ O157 infections occurred among a group of seven children in the United Kingdom who came in contact with recreational water at a coastal beach [63]. The pathogens originated from water in a Creek that was contaminated with faeces from cows grazing upstream, and the transmission was facilitated by rainfall runoff [63]. This finding highlights the potential vulnerability of humans when in contact with recreational beach environments.
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 2 Method of PD Detection Based on Different Types of Signals Emitted during PD Activities
### Optical Method  
Power transformer oil can be tested for PD activities using the optical technique. Scientists have used a variety of light detection techniques to conduct PD analysis on transformer oil/paper insulation. Mach-Zehnder interferometry, Fabry-Perot interferometry, and fiber Bragg grating are typical examples of optical sensors used for detecting PD [33]. Using a single-mode fiber and laser, MZI was the first optical fiber-based sensor. At first, a fiber coupler separates the incoming light into two separate fibers. The first detecting fiber optic coil is placed in the oil tank's PD signal zone, while the second fiber serves as a reference for the light's raytracing path. The EFPI sensor incorporates a silica diaphragm inside a capsule-shaped silica glass tube to create a single optical fiber. Because of their excellent dielectric property and immunity from electromagnetic interference, FBG sensors are currently employed in power transformers and kept directly in the oil. For a visual representation of the FBG's operating principle, see Figure 5[92], where \(\Lambda\) is the grating period, \(n\) is the refractive index, and \(\lambda_{B}\) is the reflected wavelength called the Bragg wavelength. It works as narrowband reflective optical sensors since they reflect only one wavelength of light by grating and let all the other wavelengths be transmitted through it.  
Initially, it was determined that fluorescent optical sensors could only detect PD from light emission in air, and not in transformer oil. In 2013, it was discovered that the optical method, using the fluorescent sensor and an unconventional technology, was accurate for PD measurement in power transformers. However, the fluorescent sensor research for PD detection in transformer oil produced dubious results with numerous shortcomings. The attempts to establish a link between photon activity, PD via an optical signal, and PD charge restrictions in oil have been conducted aggressively. Based on [93], the measurement was made possible in the oil-immersed transformer. This, however, proved difficult, especially when dealing with dated transformer oil [94]. Nevertheless, the technique used in [93] works best to measure a wide variety of chemical elements and physical properties, and it has the advantages of having a high-frequency response and being immune to electromagnetic interference [32]. Major limitations include the inability to calibrate PD detection, a lack of data on PD magnitude, and the inability to reliably identify discharges within transparent media. Current research is focused on improving methods for detecting and locating PD in transformer oil. On the other hand, it is also being studied about the X-ray emission as a method of PD detection since it does not require the complicated geometry of a power transformer.
# A Low Torque Ripple Direct Torque Control Method for Interior Permanent Magnet Motor
###### Abstract  
This paper proposes a simple method to generate stable voltage commands for permanent magnet synchronous motors to achieve low torque ripple based on space vector modulation direct torque control (SVM-DTC). The relationship between applied voltage and electromagnetic torque is first analyzed and the effect of the voltage and flux linkage on torque ripple is discussed. Then, the new method is developed to reduce torque ripple by giving low-fluctuation voltage commands to the voltage source inverter. The magnitude of the voltage command is calculated by avoiding the effect of flux linkage angle that can be estimated inaccurately during motor operation. Therefore, the fluctuation of the voltage command can be significantly reduced. In order to validate the proposed method, Simulink is first used for simulation and the performance of the proposed method is evaluated. It is found that the torque ripple can be substantially reduced while keeping a low switching frequency and improving system response. Then, a hardware-in-the-loop (HIL) technique is applied to test the developed algorithm that is written into a Texas Instruments microcontroller. This validates the simulation results and the proposed method.  
permanent magnet synchronous motor; direct torque control; space vector modulation; torque ripple +
Footnote †: journal: Applied Sciences
# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
## 4 Results
### Variables and Machine Learning Model Selection  
#### 4.1.1 Logistic Regression  
The analysis of data from the logistic regression model indicates strong and balanced performance in classifying vitamin D levels. The results are visually summarized in Figure 9, which presents the ROC curve and a comparative visualization of the primary evaluation metrics.  
Overall, the LR model achieved an accuracy of 73.95%. Figure 9a illustrates the Receiver Operating Characteristic (ROC) curve, which represents the model's ability to differentiate between the two classes. The area under the curve (AUC) was 0.84, indicating an 84% probability that the model correctly classifies a positive instance as more likely to be positive than a randomly chosen negative instance. Figure 9b indicates that nearly three out of four predictions were correct. This performance aligns with the individual class-specific metrics. For Class 0 (insufficient vitamin D levels), the model demonstrated a precision of 68% and a sensitivity of 84%, suggesting an adequate ability to correctly identify most true positive cases within this class. The overall F1-score for Class 0 was 73%, reflecting a balanced trade-off between precision and recall.  
For Class 1 (adequate vitamin D levels), the model exhibited optimal performance. The precision for this class was 76%, and the sensitivity reached 93%, demonstrating the model's ability to correctly classify nearly all actual instances of this category. The F1-score for Class 1 was 84%, confirming the model's reliability in distinguishing sufficient vitamin D levels.  
This result validates the discriminative capacity of the model and supports its predictive effectiveness. The model's performance metrics demonstrate consistency across evaluations, with most values ranging between 0.7 and 0.8, suggesting a reliable classification framework. However, slight differences were observed between Class 0 and Class 1 metrics, where Class 1 outperformed Class 0 due to an asymmetric class distribution in the training data. This discrepancy could potentially be addressed through techniques such as class balancing methods or weight calibration, which may further refine model performance.
###### Abstract
It represents a step forward for precision irrigation by combining high-resolution, multi-source datasets, real-time monitoring, and predictive analytics, ensuring sustainable agricultural water management and improved extreme weather forecasting under climate change conditions. This paper is structured as follows: Section 2 describes the materials and methods, MAGDA demonstrator sites, and the advanced observation technologies deployed, including GNSS, Meteorones, in situ sensors, and satellite data. It also outlines the forecasting models, the integration of MAGDA services into Farm Management Systems, and the validation methods used for meteorological and hydrological assessments. Section 3 presents the results, followed by a discussion in Section 4 and final conclusions in Section 5.
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 5 Results and Discussion
### Throughput  
Figure 13a, illustrates the throughput of protocols; it decreases with mobility. In AOMDV, there is a decrease from 1110 to 870 Kbps; in AOMR-LM, a reduction from 1120 to 940 Kbps; SRMP decreases from 1000 to 665; and the proposed AOMD-ER decreases 1080 to 810 Kbps. The AOMR-LM performs best because it requires least time to discover the route even despite the link error encountered.  
Figure 13b shows the throughput decreases with packet size. In AOMDV decreases from 1110 to 830 Kbps, AOMR-LM reduces from 1117 to 950 Kbps, SRMP decreases from 1010 to 720 Kbps, and the proposed AOMD-ER only reduces from 1080 to 790 Kbps. The AOMR-LM performed better as it requires the least time to discover the route.  
Figure 11: (**a**) Energy consumption with time; (**b**) Exh. nodes with time; (**c**) PDR with time x.  
Figure 12: (**a**) PDR vs. Node Speed; (**b**) PDR vs. Packet Size; (**c**) PDR vs. Sim. time.  
Figure 13c shows the throughput increasing with the simulation time of the network. The throughput of AOMDV increases from 150 to 980 Kbps, in AOMR-LM varies from 180 to 1110 Kbps, in SRMP increases from 70 to 651 Kbps and in AOMD-ER from 121 to 801 Kbps. The comparison shows that the performance of AOMR-LM is better than other routing protocols because AMOR-LM takes less time in load balancing between high level path and average level during packet transmission.