esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Study
### Simulation of Construction Process  
The simulation process is divided into five steps: earth stress equilibrium, tunnel excavation, section B excavation, section A excavation, and section C excavation as illustrated in Figure 8. In the first stage, the soil is equilibrated under its own weight. In the second stage, the soil inside the tunnel is frozen and the plate elements representing the lining and support piles as well as the contact units are activated to simulate the tunnel excavation and support pile construction process. Since this study focuses on the effect of the pit excavation on the tunnel, the strain of the soil and structure calculated in stages 1 and 2 are reset to 0 at the start of stage 3 so that the deformations due to the excavation can be observed clearly.  
The stress state is not reset because the pre-stress will affect the subsequent deformation level. In stages 3 to 5, the three sections of the pit are excavated sequentially, and the displacement limits at the top of piles are activated while the excavated soil is frozen in each stage.
# Intelligent Environment Monitoring System for University Laboratories
###### Abstract  
In recent years, the laboratory security of universities has become an important issue for students and devices. To solve this security issue, this paper proposes an intelligent monitoring system to realize environment detection in university laboratories. The main purpose of this system is to monitor the laboratory environment data in time and improve the laboratory inspection efficiency. The system consists of a single chip microcomputer, which is the core of this system, a sensor function module and GPRS wireless communication, realizing data monitoring and short message warning. Therefore, three features, front-end data acquisition, data wireless transmission and a security alarm, are achieved by the proposed system. The real experiments show that front-end data acquisition is effective, data transmission is reliable, and the alarm message is received in time. Furthermore, the system, with the modified function modules, can be used in other scenarios to detect environments, and thus has a significant applied value in other areas.  
university laboratory; environmental monitoring; single chip microcomputer; wireless communication +
Footnote †: journal:
## 2 Materials and Methods
### Measurement of Carotenoids  
[PERSON] et al. described the method for carotenoids determination ([PERSON] et al., 2019). A total of 100 mg of powder from freeze-dried leaf sample was added into a plastic test tube, and then 1 mg magnesium carbonate and 5 mL of pure acetone were added. In next step, the solution was mixed carefully using vortexing, and an ultrasonic bath was used for incubation for 10 min at 0 \({}^{\circ}\)C temperature. Then the examples were centrifuged (10 min, 3780\(\times\)\(g\), 5 \({}^{\circ}\)C). HPLC was used to take an aliquot of 800 \(\mu\)L of extract for testing. For carotenoids determination, HPLC (Shimadzu, USA Manufacturing Inc., Canby, OR, USA) was used, consisting of two LC-20 AD pumps, an SIL-20 AC autosampler, a CMB-20A system controller, an ultraviolet-visible detector SPD-20 AV, and an oven CTD-20 AC. A Synergi Max-RP 80i column 250 \(\times\) 4.60 mm (Phenomenex Inc., Torrance, CA, USA) was used, and then elution was done with gradient flow in two mobile phases: (A) acetonitrile/methanol (90:10), and (B) methanol/ethyl acetate (64:36). The used gradient program was: 0-16 min--95% Solvent A and 5% Solvent B; 17-21 min--50% Solvent A and 50% Solvent B; 22-25 min--20% Solvent A and 80% Solvent B. The whole analysis lasted 28 min, the flow rate was 1 mL min\({}^{-1}\), and the wavelength range for detection was 445-450 nm. The external standards (Fluka and Sigma-Aldrich, Poznan, Poland) were used for identification of carotenoids.
## 3 Model Complexity and Space Reduction Techniques for System-Level Optimization
### Surrogate Modeling  
Surrogate models aim to establish a relationship between the input and output parameters of the problem they are attempting to solve. In the design and optimization of electrical machines, the input parameters are the design parameters used in the optimization process, while the output parameters are those that require optimization, such as efficiency, cost, weight, average torque, and torque ripple. Their main advantage lies in the significantly reduced computational time required to simulate an optimization problem. For this reason, they are often preferred over the FEM due to the excessive computational time demanded by the latter method. However, since surrogate models are constructed through analytical formulas, they lack the precision of the FEM, particularly in their inability to accurately simulate the non-linear behaviors of electrical machines, especially SRMs, such as saturation and mutual coupling between phases. Despite this disadvantage, their use is widespread in the field of electrical machine design optimization, as the reduction in computational time is considerably important relative to the accuracy they sacrifice, especially at the system level, where the optimization of both the motor and controller is required. Popular surrogate models include Kriging model, Radial Basis Functions (RBFs), Response Surface Method (RSM), and Artificial Neural Networks (ANNs) [32; 33].  
In [34], the authors propose the design of a novel SRM drive topology aimed at minimizing torque ripple. Their approach involves investigating the rotor pole shape by introducing specific grooves. A surrogate model, specifically a proxy model trained using quadratic polynomial regression, is employed, alongside the Multi-Objective Genetic Algorithm (MOGA), for optimization. The objective is to reduce the torque ripple while increasing the average torque, implemented within MATLAB/Simulink. Initially, optimization is performed on the motor, followed by controller optimization through the adjustment of the turn-on, \(\theta_{on}\), and turn-off, \(\theta_{off}\), angles. The results indicate a significant reduction in torque ripple--up to 72%--with a slight increase in the average torque. However, the study lacks a dynamic-state performance analysis, which is crucial for assessing whether the controller and, consequently, the motor can effectively respond to dynamic conditions. In many cases, dynamic-state performance evaluation is neglected due to the additional computational burden it imposes on the optimization procedure. Nevertheless, it remains a critical analysis that should be incorporated to ensure robust and reliable system performance.
## 2 Materials and Methods
### Validation Methods
#### 2.5.2 Hydrological Validation: Comparison of Outcomes with Traditional Methods  
As part of an intercomparison analysis or model validation exercise, recommendations of irrigation from SPHY have been compared against quotas of irrigation applied at the SCDAB pilot site during the 2024 crop season. Irrigation at SCDAB is applied according to the ICTTID Baneasa-Giurgiu method [69], a traditional method widely used in the region. This method rests on monthly mean values of potential evapotranspiration derived from the Thornthwaite method, crop-specific coefficients previously adjusted for the typical crops in the region (maize, sunflower, soybean, and wheat), and in situ soil moisture measurements. The method provides the time and amount of irrigation to be applied in the loamy soils of the Braila region based on the Management Allowed Depletion (MAD) concept. MAD is defined in this method as the difference between the field capacity and the wilting point. For irrigated maize, the method states 50% of the MAD as the threshold for applying irrigation. When soil moisture is below this threshold, irrigation for applying is the total volume of water needed to reach the field capacity of the soil. Theoretical recommendations for irrigation are finally adjusted to the particular and complex irrigation system of the Braila county, where irrigation is applied less often but with high quotas per irrigation. During the 2024 summer season, maize was irrigated five times between May and August, accounting for a total of 2250 m\({}^{3}\)/ha. The ICTTID method does not take into account precipitation forecast or in situ meteorological forcings. In order to compare this traditional method with SPHY outcomes, irrigation quotas actually applied in SCDAB were scaled to daily values to cover the same testing forecasting windows simulated in this study.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 5 Results
### Price Comparison: Actual vs. Predicted  
Figure 4 compares the actual and predicted pricing for the GPU instance subset using the model built in the earlier step. The model was developed using historical data from 2021, and it was used to forecast prices for 2022. The illustration shows a line plot, with the x-axis representing time and the y-axis representing price. The plot depicts the actual prices in blue, while the expected values are indicated in red. According to the comparison, the anticipated prices are quite similar to the actual prices.  
This implies that the model's projections for the GPU instance subset are correct and match the prices observed in 2022. The proximity of the actual and expected prices indicates that the model grasped the underlying patterns and trends in the data, allowing it to generate trustworthy forecasts. Overall, this graph indicates the model's efficacy in projecting pricing for the GPU instance subset, offering useful insights for decision making and cost management in cloud computing settings.  
On the other hand, there is the Memory-optimized instance family. As observed in Figure 2, the median price increase between 2021 and 2022 reaches 70%. This is an unusually significant change in price evolution over time that may be characterised as disruptive. The potential to predict such a change is explored.  
Examining the Figure 5 reveals a noticeable disparity between the predicted and actual values. This difference suggests that the predictions of the model for the memory-optimized instance family can not be as accurate. The discrepancy between predicted and actual prices could be attributable to the presence of external factors not adequately accounted for by the model.  
Figure 4: Comparison of actual and predicted GPU instance subset prices for 2022.  
In practice, even though the properties of memory-optimized instances are similar between the years 2021-2022, there is a significant increase in the price, especially for high-end instances. A detailed look at the results reveals that the Memory-optimized instance family has a broader price range than the GPU instance family. As shown in Figure 5, the model struggles to accurately predict prices for instances with higher prices while performing relatively well for instances with lower prices. These higher price instances are the ones subjected to a disruptive price increase. Even so, the model correctly predicted the price increase in all cases; however, the percentage increase was not predicted, especially in cases that exceeded 50% of a corresponding price in 2021.
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 5 Discussion
### Estimation of Corn Phosphorus Status Indicators  
Phosphorus is an essential element for crop growth, playing a crucial role in the composition of proteins, phospholipids, enzymes, and nucleic acids. Additionally, phosphorus is integral to metabolic processes such as carbohydrate synthesis, transport, and photosynthesis, including photophosphorylation, sucrose phosphate transport, and cell division [51; 52; 53]. The role of phosphorus in these biochemical pathways significantly impacts the spectral characteristics of crops. Specifically, chemical bond changes, such as those involving N-H and O-H bonds induced by light exposure, alter light absorption and reflection, producing distinct spectral curves that reflect the crop's phosphorus status [54].  
Crop sensitivity to phosphorus is primarily observed in the near-infrared (NIR) spectral region, as phosphorus's key role in photosynthetic activity is closely associated with structural indicators like biomass and leaf area index (LAI) [55]. However, studies have shown that phosphorus-responsive wavelengths are distributed across both visible and near-infrared regions. Research by [PERSON] and [PERSON] demonstrated a significant reduction in chlorophyll content in wheat under phosphorus and potassium deficiency conditions. This reduction is primarily attributed to phosphorus's critical influence on crop chlorophyll and anthocyanin content [56]. Phosphorus deficiency typically leads to increased anthocyanin accumulation [57], which produces specific spectral responses in the 550 nm green light region and the 700 nm red-edge region [58]. The intensity of these spectral peaks is directly correlated with anthocyanin concentration, resulting in decreased spectral reflectance as anthocyanin levels increase [59]. Furthermore, the green light region (540-560 nm) and the red to red-edge region (640-760 nm) are particularly sensitive to anthocyanin concentration, underscoring their potential for monitoring phosphorus nutritional status [60; 61].  
In this study, vegetation indices related to the phosphorus nutritional status of maize were primarily derived from combinations of visible and near-infrared bands. The inversion accuracy of these indices exceeded 0.70, indirectly demonstrating the potential of combining visible and NIR bands to monitor maize phosphorus status. This potential arises from the significant impact of phosphorus application on maize chlorophyll, anthocyanin, biomass, and other structural canopy indices.
## 5 Discussion
### Land Cover Changes Relative to Drought and Wildfire
#### 5.2.3 Breakpoints and Wildfire  
Wildfire generally reduces plant cover, alters habitat structures, decrease rangeland conditions, and requires much longer recovery period [83]. The significant breakpoints with decreased productivity (i.e., irreversible degradation based on Segmented VPR) that coincided with fire events are shown in Figures 11 and 12. Major wildfire incidents occurred in 1989 and 1994 followed by a consistent increase in frequency each year since 2001. This increase was noticeably concurrent with drought events. The number and time of some breakpoints mostly followed these of the fire incidents. The alignment of fire incidents with breakpoints can indicate the ability of TSS-RESTEND to detect the timing of ecosystem changes as suggested by [8]. However, based on Figures 11 and 12, it appeared that only a small number of breakpoints coincided with fire incidents during the study period--suggested that fire may have a limited contribution to the development of breakpoints. While, the timing of these fire incidents can partly explain the occurrence of the breakpoints, it was not rationale to state that these fire incidents were the only direct cause of the breakpoints. Other factors need to be considered to provide a rational explanation of the remaining breakpoints such as vegetation types and their response to environmental and climate disturbance (drought). The accumulation of successive dry conditions can push an ecosystem to pass a threshold of permanent damage of vegetation--ideal fire-prone conditions. Further analysis is needed to identify the causes of breakpoints relative the fire and was out of the scope of this analysis.
## 3 Results and Discussion
### Response Surface Model for HNS Spill Predictions  
The CFD simulation provides deterministic solutions that are bounded by the boundary and operating conditions, which vary with different scenarios. Thus, obvious limitations exist in taking the CFD approach whenever accident scenarios are changed. To reduce the computational cost, the statistical approach has been widely used in many engineering applications and is also used in the present study. As in the meta-modeling, the present study adopted the RSM to provide the surrogate mathematical model from the CFD simulation results. To our knowledge, this is the first study of the near-field propagation characteristics. Of course, it is not easy to create a meta-model with high accuracy because there are insufficient simulation results. However, a more accurate model can be made by accumulating the simulation data and updating the model continuously. The main objective of this work is, therefore, to construct the numerical procedure required for making the meta-model of HNS propagation. The three main parameters, HNS layer thickness (\(\delta_{layer}\)), HNS propagation velocity (\(v_{p}\)), and averaged HNS mass fraction at sea level (\(\phi_{s-l}\)) are the dependent variables used in the mathematical model. The second-order RSM for the three main parameters is fitted to 15 cases using a general least-squares regression method. Each input/dependent variable is obtained from the CFD simulation, as discussed above. The general form of second-order RSM is expressed by  
\[y^{\prime}=a_{0}+a_{1}x_{1}+a_{2}x_{2}+a_{3}x_{1}^{2}+a_{4}x_{2} ^{2}+a_{5}x_{1}x_{2}\, \tag{15}\]  
where, \(y^{\prime}\) is the main parameter, \(x_{1}=v_{c}\), and \(x_{2}=\rho_{lms}\); \(a_{0}\) to \(a_{5}\) are model constants. The obtained model constants are listed in Table 1. Three-dimensional response surface contours of each dependent variable are shown in Figure 8. The predicted values are compared with the corresponding CFD simulation results (black dots), and the goodness of fit is measured. The variables and scatter of the data points around the fitted regression curve are always evaluated between 0 and 1. As listed in Table 2, both values are greater than 0.9, which means that the prediction model provides a good fit.  
Figure 7: The HNS concentration at sea level for the input variables (\(v_{c}\) and \(\rho_{lms}\)).
# Line Scan Spatial Speckle Contrast Imaging and Its Application in Blood Flow Imaging
## 3 Results and Discussion
### Imaging Blood Flow Changes during Vascular Clipping Lsing LS-SSCI  
LS-SSCI was employed to observe blood flow changes during the vascular clipping of chick embryos. Chick embryo (day 3, NO. 3) was used for the experiment. A silk suture (Ethicon, w1814, shown in Figure 6 using the red circle) was used for the vascular clipping. Figure 6a,c shows the wide-field image with white light illumination before and after the clipping, respectively. Figure 6b,d shows the corresponding speckle contrast image using LS-SSCI before and after the vascular clipping, respectively. The difference between Figure 6b,d is illustrated in the figures using the yellow circles. The vessel in the yellow region indicated in Figure 6b can be clearly seen, but this vessel is invisible in Figure 6d. The observations demonstrated that the clipping caused a severe reduction in blood flow in the regions of almost 100%. The experimental results proved that LS-SSCI shows great potential in diagnosing vascular diseases, such as angiosclerosis, angiostenosis, or angiemphraxis.  
Figure 5: The laser speckle contrast images of a chick embryo (day 3.5) under the exposure time 4 ms using the (**a**) LS-TSCI; (**b**) LS-SSCI. The image area is 10.24 mm \(\times\) 8.64 mm.  
Figure 6: **(a)** A wide-field image of the chick embryo (day 3) with white light illumination. (**b**) The corresponding laser speckle contrast image using LS-SSCI before clipping. (**c**) The intensity image of the same chick embryo after vascular clipping. The silk suture is indicated in the red circle. (**d**) The corresponding laser speckle contrast image using LS-SSCI after clipping. The vessel in the yellow circle is more visible in (**b**) compared to (**d**). The image area is 4.26 mm \(\times\) 6.78 mm.
# FE Analysis of Motorcycle Helmet Performance under Severe Accidents
## 2 Injury and Strength Assessments
### Impact Test According to TIS 369-2557  
The Ministry of Industry enforces testing standards for commercial helmets in Thailand, as established by Thai Industrial Standard no. 369-2557 [11]. This standard, which was adopted from DOT and ECE, intends to ensure the overall performance of helmet structures as well as the ability to protect from a head injury in severe motorcycle accidents. There are six tests used in the TIS 369-2557 to investigate the performance of each helmet structure component, including the impact test, visor test, chin strap test, penetration test, retention test, and rigidity test. The impact test causes the most serious damage to the helmet and directly assesses the structural strength. To perform the impact test, three related pieces of equipment are required for this regulation: the motorcycle helmet, headform, and anvil (flat or kerbstone). The headform represents the motorcyclist's head and measures the severity of injury when the helmet structure is impacted by the anvil. The flat anvil has a diameter of 130 \(\pm\) 3 mm, whereas the kerbstone anvil has a slope of 52.5\({}^{\circ}\)\(\pm\) 2.5\({}^{\circ}\) toward the vertical with 125 mm of length.  
The impact test can be performed at four points of the helmet's structure: frontal (B), side (X), top (P), and rear (R), as shown in Figure 1. The motorcycle helmet is placed on the headform and then fastened using the chin sharp before installing the completed sample set in the testing machine. The impact test is then conducted by dropping the sample to impact with the anvil under an initial velocity of 7.5 m/s. The results are recorded for both acceleration and HIC at the CG of the headform. A commercial helmet will pass the criteria of the TIS 369-2557 regulation as long as the peak acceleration during the test and HIC do not exceed 275 g and 2400, respectively. TIS 369-2557 currently accepts only the test results to clarify the safety based on these criteria. Only a few sample helmets pass the standard's requirements before being sold commercially in stores.  
The nomenclatures of B, X, P, and R in Figure 1 are only defined to indicate the positions of each impact point of the motorcycle helmet according to TIS 369-2557. These four positions will be investigated for a severe head injury based on the energy absorption ability under the impact test in Section 3.