esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical and Microbiological Characteristics and Antioxidant Activity of Honey Bee Pollen
## 2 Materials and Methods
### Analysis of Total Phenol Content and Antioxidant Activity  
For the determination of antiradical activity and total phenol content in pollen samples, extracts were prepared according to the following scheme: First, 2.00 \(\pm\) 0.05 g of each sample was weighed into a centrifuge tube; then, 10 mL of 70% ethanol was added, and samples were placed in an ultrasonic bath for 10 min and centrifuged for 20 min at 3500 rpm in a centrifuge (ELMI CM, Riga, Latvia). After centrifugation, the contents of the tube without precipitate were poured into an opaque glass bottle, and the tube was refilled with 10 mL of 70% ethanol and extracted and centrifuged again. The resulting extracts were combined, making up to 50 mL with a 70% ethanol solution, and stored in a refrigerator until the analyses were performed.  
Next, 2.5 mL of Folin-Ciocalteu reagent [22] was added to 0.5 mL of the previously prepared extract. After 3 min, 2.0 mL of 7.5% Na\({}_{2}\)CO\({}_{3}\) solution was added, and samples were left for 30 min. The results were then read on a JENWAY 6300 spectrophotometer (Barloworld Scientific Ltd., Staffordshire, UK) at 765 nm. The total phenol content is expressed as gallic acid equivalent per 100 g of dry weight (GAE mg/100 g dw). For each sample, the total phenol content was determined in nine replicates, and average values were taken.  
Two methods were used to determine antiradical activity in bee pollen samples. The ABTS test was performed for the determination of the radical cation of 2,2-azino-bis (3-ethylbenzthiaazoline-6-sulphonic acid) (ABTS), in which 5.0 mL of ABTS solution was added to 0.5 mL of the previously prepared extract and aged for 10 min. The absorbance of the solution was read on a JENWAY 6300 spectrophotometer (Barloworld Scientific Ltd., Staffordshire, UK) at 734 nm.  
The second method was the DPPH test, in which 3.5 mL of DPPH (2,2-diphenyl-1-picrylhydrazyl reagent) solution was added to 0.5 mL of the previously prepared extract and aged for 30 min. The absorbance of the solution was read on a JENWAY 6300 spectrophotometer (Barloworld Scientific Ltd., Staffordshire, UK) at 517 nm. The activity in the analysed samples was expressed as millimoles of Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) equivalent per 100 g of sample dry weight (mmol TE/100 g dw). Nine replicates were conducted for each pollen sample, with the mean values taken.
## 5 Conclusions  
Due to the special operation of the imaging processing algorithm for the high-resolution sliding-spot SAR mode, the traditional RD model cannot be adopted directly for its geometric processing. This paper illustrates the influence of the dechirp operation on the slant range image first and concludes the new position relationship between the output dechirped range image and SAR. Then a constellation model based on the antenna pointing vector is proposed, and the forward and the inverse calculation flows are further designed for the geometric calibration and positioning, respectively. Finally, the geometric experiments are carried out using the outfield SAR satellite data. The influence of different error factors on the geometric positioning accuracy are quantitatively given sub-item, and it is proved that after the compensation of all the errors, the image uncontrolled positioning accuracy can reach within 2 m. In addition to this, based on the experimental results, a series of suggestions are given about the reference elevation input selection, the calibration scheme design, and the error source compensation, which provide a complete and reliable basis for the design of geometric processing in the sliding-spot imaging mode. In the next research work, we will further analyze the residual error of the above geometric positioning and its influencing factors in detail so as to improve the positioning algorithm. Meanwhile, we will try to provide theoretical support for the demonstration of higher performance requirements for on-board devices on future satellites.  
Writing--original draft preparation, investigation, Y.L.; methodology, conceptualization, and project administration, H.W., D.M., and G.G.; data curation, and writing--review and editing, C.L. and X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China NFSC (61971047).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Related Work and Foundational Concepts
### Software and System Development Life Cycles  
The Software and System Development Life Cycle (SDLC) is a foundational framework guiding the creation and maintenance of software systems through structured phases like requirements analysis, design, implementation, and testing [1,2]. The choice of SDLC methodology significantly impacts a project's efficiency and, critically, its security posture, especially when handling sensitive or classified data [4]. As security requirements become more stringent, development methodologies must adapt to protect sensitive information effectively.
## 4 Results and Discussion
Figure 4 shows an approximately linear relationship for both reliability and failure rate as a function of ADT (with different MSL values) at the age of 50 years. The rate of decrease in superstructure reliability is approximately 0.8% to 0.9% per 1000 vehicles, and the rate of increase in the failure rate is roughly 0.12% to 0.16% per year per 1000 vehicles. Figure 5 shows superstructure reliability and failure rates as a function of MSL at a bridge age of 50 years. The analysis was run at different ranges of ADT, and the superstructures types were not separated into steel and concrete. The rate of decrease in superstructure reliability is approximately 0.38% to 0.34% per meter of MSL, and the rate of increase in the failure rate is roughly 0.04% to 0.06% per year per meter of MSL. Figure 4: Superstructure reliability and hazard rate versus ADT at different maximum span length at the age of 50 years (All superstructure types). The superstructure reliability and failure rate are similarly examined at an age of 75 years. The analyses were done for different MSL and ADT values. The results are shown in Figures 6 and 7. Figure 5: Superstructure reliability and hazard rate versus MSL with different ADT values at the age of 50 years (All superstructure types). Figure 6: \(Cont.\)  
Figure 6: Superstructure reliability and hazard rate versus ADT at different MSL values at the age of 75 years (All superstructure types). Figure 7: Superstructure reliability and hazard rate versus MSL with different ADT values at the age of 75 years (All superstructure types). Comparison of results for 50- and 75-year-old bridge structures shows a substantially higher reliability for bridge superstructures at the age of 50 years. For example, the superstructure reliability at the age of 75 years for ADT of 1000 and MSL of 30 m is 14%, whereas the corresponding reliability value at the age of 50 years is 57%.
## 1 Introduction  
South Africa faces significant energy challenges, with approximately 3.5 million rural households lacking access to electricity and relying on traditional biomass due to financial constraints or the absence of grid connections [1, 2, 3, 4]. Limpopo Province has the highest electricity access rate at 98.55%, while the Eastern Cape lags at 82% [5]. These issues are compounded by South Africa's national power utility, Eskom, which faces a USD 24 billion debt, aging infrastructure, and corruption. These challenges hinder electricity supply and result in frequent load shedding as energy demand continues to rise [6]. Wind energy presents a viable solution, with the South African wind energy association estimating it could meet 62% of the country's energy needs [7]. Additionally, the Renewable Energy Independent Power Producer Procurement Programme (REIPPPP) reported a 42% reduction in wind energy production costs, bringing it down to 74 cents per kWh [8].  
Global wind energy capacity grew from 743 GW in 2020 to 837 GW in 2021, with China and the United States leading at 338 GW and 134 GW, respectively. According to the Global Wind Energy Council, this expansion reduced carbon dioxide emissions by over 637 million tonnes, contributing significantly to climate change mitigation [9]. These reductions are crucial in addressing extreme weather events, such as the heatwaves experienced in Cape Town and cyclones that have impacted Mozambique and Zimbabwe [10].
## 2 Materials and Methods  
### Materials  
As stated in the introduction, the aggregates of a hardfill are typically unclassified, and their particle size is not adjusted (except for eliminating oversized particles and conducting crushing for effective utilization). Therefore, the differences in the PSDs of aggregate may be significant even in the same aggregate collection area, making it difficult to maintain a consistent unit water content. A hardfill dam's strength naturally changes owing to the use of materials with variable particle size distributions and unit water content, even if the amount of cement remains constant. Accordingly, when designing a hardfill dam, the hardfill's target strength is typically set after investigating the range of the hardfill strength according to changes in the aggregate PSD, unit cement content, and unit water content.  
This study excluded aggregates with a maximum particle size exceeding 80 mm, as these generally have low applicability as hardfill aggregates, based on previous studies [18; 19]. Two types of PSDs, i.e., maximum PSD (coarse aggregates) and minimum PSD (fine aggregates), were selected based on previous investigations of the aggregate PSD range in major river basins in South Korea [11]. The aggregates used in the experiment were collected from a spoil pit within the Danyang Submerged Weir construction site and a nearby river basin in Danyang-gun, Chungcheongbuk-do, South Korea. They were based on quartzose sandstone and generally well-graded. The shape of the aggregate particles was generally sub-rounded to rounded. An oversize cut was performed by applying the maximum aggregate size of 80 mm using a backhoe and aggregate separator. Then, the screened aggregates were transferred indoors. Figure 1 shows the PSDs of the two types of aggregates used in the laboratory test of this study. The same materials were applied in the laboratory and field tests as described in Section 2.3.  
Prior to this study, standard specimens were fabricated using aggregates with a maximum particle diameter of 40 mm and particle size similar to that of the PSD collected from the same place shown in Figure 1, but with different quantities of cement. Then, compaction and uniaxial compressive strength tests were performed with the standard specimens [29; 30; 31]. Based on the results, a unit of cement content of 80 kg/m\({}^{3}\) and three units of water content of 70, 85, and 100 kg/m\({}^{3}\) were selected as the range of appropriate mixing ratios for the laboratory test investigation with large specimens using aggregates with a maximum particle diameter of 80 mm. Ordinary Portland cement (Type I) was used for the hardfill mix (Table 1).
# Study on Disturbance Mechanism of Squeezed and Non-Squeezed Soil Piles on Soft Soil Foundation
## 5 Discussion
On the other hand, bag grouting  
Figure 29: Comparison chart of crucial indicators. piles offer advantages such as controllable body quality, compaction degree, and feasibility for various geological conditions. Nevertheless, their prolonged construction period limits their practical use. Bored piles offer high bearing capacity, large diameter construction capability, and versatility for various foundations, but they have drawbacks such as slow drilling speeds and the potential for slurry contamination. Thus, in engineering applications, the optimal piling method can be selected by considering factors such as construction difficulty, economic costs, and the allowable range of disturbance. Further, although this study enables a synthetic comparison among three piling patterns on their disturbance to the soft soil foundation, the same conclusion (optimal order) may not be applicable when driving piles into other strata.
## 4 Computational Approach
### Enthalpy--Porosity Method (EP)  
The EP phase-change model, used in approaches A and B, was implemented in Fluent by default for simulations of phase changes (solidification and melting model) [43]. This method solved the phase change by defining the PCM domain as a liquid, for which the solid material was equivalent to a liquid substance of very high viscosity [44]. A finite range of temperatures was established between which the phase change occurred, known as the _mushy zone_, in which the fluid was treated as a pseudo porous medium. \(T_{s}\) and \(T_{l}\) were the lower and the higher range temperatures of the _mushy zone_, according to Table 1. For the mushy zone, \(\beta\) is defined as the liquid fraction, a parameter that represents the fraction of the cell volume that is in the liquid state. When \(\beta=0\), the material of the cell was in the solid phase, and if \(\beta=1\), all volume was in the liquid phase  
\[\beta=\left\{\begin{array}{ccc}0&&T<T_{s}\\ \frac{T-T_{s}}{T_{l}-T_{s}}&&T_{s}\leq T\leq T_{l}\\ 1&&T>T_{l}\end{array}\right. \tag{3}\]  
For this method, the energy equation is defined as [23]:  
\[\frac{\partial}{\partial t}(\rho\cdot H)=K\cdot\
abla T \tag{4}\]  
\[H=h+\Delta H \tag{5}\]  
\[h=h_{ref}+\int_{T_{ref}}^{T}C_{p}dT \tag{6}\]  
\[\Delta H=\beta\cdot L \tag{7}\]  
where the enthalpy of the material, \(H\), was calculated as the sum of the sensible enthalpy, \(h\), and the latent heat of the melting process, \(\Delta H\). The sensible enthalpy was defined as the sum of the reference enthalpy of the material, \(h_{ref}\), and the integral of the specific heat, \(C_{p}\), between the temperature, \(T\), and the temperature of the reference point of the substance, \(T_{ref}\). On the other side, the enthalpy was equal to the liquid fraction times the latent heat of fusion, \(L\).  
In the end, the method used an iterative process between Equation (4) and the enthalpy equations to solve the temperatures.  
The EP method added a new term to the Navier -Stokes momentum equation, which defines the motion of a fluid. \(S\) is a momentum sink, defined from the Carman-Koseny equation, which extinguishes the velocity of solids due to their high porosity.  
\[S=\frac{(1-\beta)^{2}}{\beta^{3}+\in}\cdot A_{mush}\cdot\left(\overrightarrow {v}-\overrightarrow{v}_{p}\right) \tag{8}\]where \(\in\) is a smaller constant to prevent division by zero, \(A_{mush}\) is the mushy zone constant, and \(\overset{\rightarrow}{v}_{p}\) is the solid velocity due to the pulling of solidified material out of the domain. As in this case, the solid material was not being pulled from the domain, \(\overset{\rightarrow}{v}_{p}=0\). The mushy zone constant regulated the velocity of the phase change: the higher this value, the faster the transition between phases.
## 3 Materials and Methods
### Gait Phase Classification  
In this study, we select the support vector machine classifier (SVM) with Gaussian kernel and the decision tree classifier (DTC) to evaluate the impact of fatigue and the effectiveness of the training strategy. SVM can project data into the high-dimensional space in different forms depending on the choice of the kernel function to generate the classification boundaries, and it has been wildly used in the field of sEMG recognition with proved performance [42]. DTC is chosen, due to the small account of calculation, the easiness of understanding, and the extensive utilization in the classification of the biomedical signals [43].  
As the experiment progressing, the muscle fatigue of the subjects continuously deepens. In order to investigate the relationship between the classifiers' performance and muscle fatigue, we consider that the subjects are not fatigued in session 1 and fatigued in session 3.  
The classifications in this study are divided into two parts. (1) We chose the data in session 1 as the training set, while the data in session 3 as the test set. In this way, we try to determine how much muscle fatigue affects the gait classifiers and which classifier performs better under the fatigue. (2) The first half of the data of session 3 is added into the training set, and the rest of the data is used as the test set. In this way, we propose the training strategy that aims to improve the performances of the classifiers under the fatigue condition. Figure 5 shows the split of the data set in the classification.  
Figure 5: Split of the data set in the classification.  
In each part, we classify the gait phases by SVM and DTC with the eight features, respectively. Optimizations of the hyperparameters are carried out by the 5-fold cross-validation in the training set, and then the models with optimized hyperparameters are trained by the training set and tested by the test set. The accuracies of the cross-validation and of testing on the test set are recorded. These procedures are performed in each subject separately, and the results are averaged over all the subjects.
# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 3 Proposed Methodology  
We provide three methods for the prediction of stuck pipe. The first method targets the detection of friction coefficient which can represent the trend of stuck pipe. The second method targets the prediction of probability for stuck pipe using ANN. The last model establishes a comprehensive indicator based on the first and the second method using fuzzy mathematics. Finally, we compare these three methods with the data from a well in Tarim Oilfield, China.
## 6 Conclusions  
Miniaturization and multifunctioning are the basic performances required by radar systems, while a proper heat dissipation system is a crucial mechanism for ensuring the reliability and life-span of the system. Based on the specifications of the no-load AESA radar T/R module, and a system that is limited by its size and weight, this study integrated a fluid circulation-type cold plate and array radar T/R Module to develop an integrated heat dissipation system that was suitable for high-power radar equipment. In the proposed system, the temperature equalization and heat dissipation performances of the fluid circulation-type cold plate are the key components that directly affect the radar performance. On this basis, three kinds of fluid circulation-type cold plates were designed in advance in this study. In the cold plate design, we use the simulation method and the Taguchi method to optimize the design of the parameters in the initial stage, so as to initially obtain a better design scheme for the cold plate, and then manufacture the physical samples based on the optimized design parameters to ensure that the physical samples can meet the performance requirement. This can reduce the risk of poor product performance. According to the simulation and experimental results, the proposed integrated heat dissipation mechanism can control the temperature equalization and temperature of the system within the specified requirements; thus, this system can operate within the requirements of energy saving and performance specifications. Moreover, the effectiveness and practicability of the heat dissipation mechanism, as proposed in this work, can be verified, and a reference can be provided for the design of high-power electronic equipment in a heat dissipation system. In addition, as the velocity of flow has a great influence on the heat dissipation efficiency of the three-dimensional cold plate, the factors affecting the heat dissipation performance can be seen in the research process.  
The faster the flow rate, the better the heat dissipation efficiency, and the greater the relative internal pressure; however, the greater pressure may cause structural damage to the cold plate. Subsequently, the trial-produced hardware was introduced into the optimization parameters, and experiments were carried out to determine the balance between the flow rate and the pressure, which can make the operating system more stable and reliable.  
Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing--original draft, J.-Y.L. and Y.-L.L.; Resources, Software, Validation, J.-Y.L.; Supervision, Writing--review and editing, Y.-L.L., S.-W.M. and M.-D.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Electronic Systems Research Division, National Chung-Shan Institute of Science and Technology of Taiwan, R.O.C., under Grant No. 09163P-CS.  
Figure 15: Temperature distribution of the fluid cold plate.
## 5 Simulation Results and Discussion  
Simulated analysis of the proposed control scheme was carried out in the Matlab/Simulink environment. Three separate case studies were taken into account to demonstrate the effectiveness of the control method. Comparing the performance and robustness to the techniques outlined in the aforementioned literature allowed for evaluation.
# Thermal-Structural Coupling Analysis of Subsea Connector Sealing Contact
## 5 Numerical Simulation of Thermal-Structural Coupling of the Subsea Connector
### Coupling Stress Analysis in the Pressure Shock Mode  
The pressure shock mainly simulates that the connector is subjected to a shock higher than the working pressure in a short time when the temperature is stable. As shown in Figure 17, the shock pressure used is 1.5 times of the rated working pressure (51.75 MPa). Figure 18 shows the change of the maximum contact stress of the lenticular sealing gasket and the maximum equivalent stress of each component. As shown in Table 2, the parameters under this working condition are extracted.  
Figure 16: Stress distribution in the sealing width when \(z_{0}\) is 0.27 mm. (**a**) Distribution of \(\sigma_{r}\) at \(z_{0}\) = 0.27 mm; (**b**) Distribution of \(\sigma_{\theta}\) at \(z_{0}\) = 0.27 mm; (**c**) Distribution of \(\sigma_{z}\) at \(z_{0}\) = 0.27 mm.  
Figure 17: Pressure shock mode.  
After analysis, it can be seen that the change trend of the maximum contact stress of the lenticular sealing gasket with the pressure shock is similar to that under the condition of rapid pressure rise and pressure reduction. Under the steady-state temperature distribution, when the oil gas pressure load reaches 1.5 times of the rated working pressure, the maximum contact stress of the lenticular sealing gasket increases from 458.76 MPa to 492.59 MPa, which meets the contact stress conditions of the oil and gas sealing. Under the impact load of oil and gas pressure, the maximum equivalent stress of the finger is 113.00 MPa, and the maximum equivalent stress of the hub is 195.21 MPa, both of which are below the theoretical yield limit (310 MPa) of the 12 Cr2 Mol material. At the same time, the change of the maximum equivalent stress of the finger is 1.21 MPa, and the change of the maximum equivalent stress of the hub is 18.57 MPa, which shows that the finger is affected by a smaller impact. The maximum equivalent stress at the spherical surface of the lenticular sealing gasket is 275.22 MPa, which is 23.37 MPa higher than the rated working pressure, and this stress further aggravates the elastoplastic and plastic deformation of the sealing spherical surface. It shows that the lenticular sealing gasket is more sensitive to high pressure under high temperature conditions and is prone to produce large plastic deformation. During the service period of the connector, it is necessary to avoid opening and closing the wellhead several times in a short time, so as to prevent the connector from pressure impact and fatigue wear on the sealing surface under high temperature.