esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of the High-Mix, Low-Volume Manufacturing Industry
## 4 Optimisation of the HMLV Layout and Flow
### Layout and Flow Optimisation
#### 4.2.3 Flow Management  
In HMLV environments, two critical aspects must be considered in implementing the flow in the manufacturing system. First, the variety and volume of products mean that production lines and cell construction differ from low-mix high-volume environments. Equipment and cells are shared among various tasks, in contrast with highly dedicated lines and cells. Next, balancing and stable line production are more complicated when subjected to customer diversity and demand uncertainties.  
In static flow paths, pull production policies can be established, such as Kanban or constant work-in-process (CONWIP). [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [16] suggested that the theories of constraints (TOC) and drum-buffer-rope (DBR) TOC/DBR can be applied to HMLV's dynamic flow path. The control mechanism of TOC/DBR mainly consists of bottleneck detection, production planning on the bottleneck resource, setting and management of the buffer, and control of the production task time. This devised method hopes to better cope with the capacity imbalance and avoids cycle time and WIP increase caused by frequent flow interruption, due to any traditional pull production policy in HMLV environments.  
[PERSON] et al. (2008) developed a mathematical model to optimise adaptive flow paths according to product families to account for flow path management in a dynamic formation corresponding to different product families. Their mathematical model addresses the lack of consideration in machine sharing in the traditional approaches for cell design for part families. Therefore, the model considers variables that define the conditions for machine sharing.  
Flow management in the semiconductor industry was a concern due to the variable nature of the wafer testing environment. Traditionally, the established layout baseline in semiconductor production is in the job shop manufacturing style; However, with customers demanding more integrated functions on a single chip, coupled with high innovation speed and short product life cycles, [PERSON], et al. [41] leverage the advantages of flow production. The developed virtual time-based flow principle (VTBFP) had the idea of using the time aspect of the flow production to logically overlay the job shop with VTBFP. This meant matching the flow times and capacity balancing of consecutive process steps for the continuous flow of material.  
[PERSON], et al. [42] continued the work on the VTBFP approach by applying DES to design the production control for flow with the use case carried out on the wafer test facility. Unlike flow production, the wafer test has varying process times, for which a pure synchronised method is less appropriate. The VTBFP simulation has shown promising results in a partly VTBFP clocking setup. Setups time were reduced, and the optimum window was improved from 15-30% to 30-55%.
# Airglow Observation and Statistical Analysis of Plasma Bubbles over China
## 3 Analysis of the Main Characteristics of Plasma Bubbles
### Drift Velocity of Plasma Bubbles  
Through analyzing the sequence images of the EPB changing with time, as shown in Figure 5, it can be observed that the EPB exhibits the features of drifting eastward, then the eastward drift velocity of EPB was analyzed and investigated. Here, we use the following method to obtain the drift velocity of EPB, the drift distance of EPB can be calculated through projected images from Figure 5, and the interval time of each image was recorded, then the drift velocity can be obtained through drift distance and interval time. In this study, the airglow data used to analyze the drift velocity began from 18:44 UT to 20:27 UT on 2 September 2014. The west wall of EPB is more stable than the east wall; thus, the drift velocity of the west wall was calculated, which can be appropriate for analyzing the drift velocity of EPB. Figure 6 shows the drift velocity results of EPB changing with time.  
In Figure 6, the blue plot shows the actual drift velocity of the EPB, and the red plot represents the fitting results of the drift velocity, which is fitted by a cubic polynomial. It is visible from Figure 6 that the drift velocity of EPB is relatively stable, it fluctuates within a certain range. During the early period of EPB appearance, the EPB exhibits a small drift velocity, about 50 m/s at 18:44 UT. With the increase in local time, the drift velocity of EPB increases gradually, and then reaches a relatively stable condition. During the period of 19:15-19:51 UT, the drift velocity of EPB tends to be more stable, the value of drift velocity is about 70 m/s. When EPB is about to disappear, the drift velocity of EPB continues to increase with time, and the maximum drift velocity reaches about 120 m/s. From Figure 6, it can be concluded that the average drift velocity of the EPB is about 76 m/s (+ / -20 m/s), and the drift velocity of the EPB is in the range of 40-130 m/s (+ / -20 m/s).  
Figure 5: Projected airglow images from 18 UT to 20 UT on 2 September 2014 (**a–t**).
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 3 Results
### Design of the ARC
#### 3.1.3 Design Specifications  
Based on the design above, the multi-band ARC can hold for the following specifications shown in Table 1.  
Except for the fairly good specifications, the multi-band ARC is also designed for several operational modes such as the transponder mode, receiver mode, standby mode, self-calibration mode, and RCS calibration mode.  
In the transponder mode, the active RCS setting is supported by a 5 dB step attenuator, and the accuracy of RCS is monitored and guaranteed by the gain calibration circuit. Moreover, tunable delay is possible in the range, which means that the point target response in the final image is not at the location of the ARC, which reduces the re-radiation effects of the structure of itself.  
In receiver mode, the azimuth pattern of the SAR can be recorded by one ARC, and the elevation pattern of the SAR can also be acquired if some ARCs are placed in dedicated areas. The characteristics of the pulse transmitted by the SAR such as the amplitude flatness, the orthogonality, and the imbalance between the I and Q signals, the pulse width, and so on can also be measured and monitored in receiver mode.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Frequency & L/C/X/Ka-bands \\ Band & 200 MHz (L-band), 240 MHz (C-band), 1200 MHz (X/Ka-bands) \\ Radar cross section (RCS) & 35–55 dBsm \\ Range delay & 0.5 us, us, us, 2 us, \\ RCS step & 5 dB \\ Polarization Isolation & More than 40 dB \\ RCS stability & 0.2 dB (L/C-bands), 0.3 dB (Ka-band) \\ Gain flatness & 1 dBp-p (L/C-band), 3 dBp (P-band) \\  & L: approx. –15.9 dBm/m\({}^{2}\) –69.9 dBm/m\({}^{2}\) \\  & C: approx. –3 dBm/m\({}^{2}\) to –68 dBm/m\({}^{2}\) \\ Dynamic range & X: approx. 1.5 dBm/m\({}^{2}\) to –58.5 dBm/m\({}^{2}\) \\  & Ka: approx. –9.25 dBm/m\({}^{2}\) to –43.25 dBm/m\({}^{2}\) \\ Polarization & H-pol (L-band), H and V-pol (C/X-bands), V-pol (Ka-band) \\ Antenna pointing precision & Better than 0.2\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of the multi-band ARC.  
Figure 5: Gain calibration circuit scheme.  
In standby mode, the control unit waits for the satellite to pass by. The control and data collection subsystem will be booted up at a pre-programmed wake-up time to power the whole ARC and set the proper operational mode.In self-calibration mode, the ARC will check the status itself and record the status information. Also, the gain will be calibrated to a standard to ensure the accuracy of RCS and the stability of the receiver.  
In RCS calibration mode, an outside test range and a metal plate with a calculated radar cross section are used. A calibration pulse is transmitted from the ARC, reflected by the plate, received by the ARC, and retransmitted after a 2 us delay until the pulse is attenuated to a small enough size. A series of decaying pulses are monitored and recorded within the RF loop from which the absolute RCS of the ARC can be extracted. [22; 23]
## 2 Materials and Methods
### Analysis of Flavor Compounds of Fermented Chambourcin Juice  
#### 2.6.1 Analysis of Nonvolatile Compounds by UHPLC  
To analyze nonvolatile compounds, sterile juice and inoculated fermentations were filtered through a 0.2 \(\upmu\)m membrane. Nine nonvolatile compound standards and mobile phase were prepared including glucose (99%, Acros Organics, Thermo Fisher Scientific, Waltham, MA, USA), fructose (99%, Alfa Aesar, Haverhill, MA, USA), glacial acetic acid (VWR International, Radnor, PA, USA), tartaric acid (99%, TCI America, Portland, OR, USA), citric acid (99.5%, VWR International, Radnor, PA, USA), malic acid (99%, Acros Organics, Thermo Fisher Scientific, Waltham, MA, USA), succinic acid (99%, Acros Organics, Thermo Fisher Scientific, Waltham, MA, USA), glycerol (VWR International, Radnor, PA, USA), ethanol (Decon Labs, King of Prussia, PA, USA), and sulfuric acid (Sigma-Aldrich, St. Louis, MO, USA) were purchased. Standards and mobile phase were dissolved in deionized water to a desired concentration and filtered through 0.2 and 0.45 \(\upmu\)m membrane filters, respectively. Standards were prepared according to previously published concentration of compounds in juice or wine [48; 49]. Five concentrations of each compound were made by a two-fold serial dilution to construct a standard curve. The highest concentration of each compound was glucose and fructose: 40 g/L; glacial acetic acid: 0.8 g/L, tartaric acid: 8 g/L, citric acid: 2 g/L, malic acid: 6 g/L, succinic acid: 2 g/L, glycerol 6 g/L, and ethanol: 7% (\(\
u/v\)). Serial dilution standards were injected to construct a standards curve and the retention time (RT) of each compound was recorded and validated. Equations of each standard curve had \(R^{2}\) = 1.  
Chromatographic separations were performed on an Ultra High-Performance Liquid Chromatography (UHPLC) system (Vanquish UHPLC Systems, Thermo Fisher Scientific, Waltham, MA, USA) equipped with a refractive index (RI) detector (RefractMax 521, Thermo Fisher Scientific, Waltham, MA, USA) at the HUCK CSL Behring Fermentation Facility at Penn State. Targeted compounds were separated and analyzed on an Aminex HPX-87H column (300 \(\times\) 7.8 mm) (Bio-Rad Laboratories, Hercules, CA, USA), protected by a Micro-Guard Cation H guard column (30 \(\times\) 4.6 mm) (Bio-Rad Laboratories, Hercules, CA, USA) and kept at 60 \({}^{\circ}\)C. The analytical conditions used were as follows: 10 \(\upmu\)L of injection volume, flow 0.5 mL/min, eluent 5 mM \(H_{2}SO_{4}\). Temperatures set for autosampler and RI detector were 4 and 35 \({}^{\circ}\)C, respectively. A standard curve was prepared using standards to determine the relationship between concentration and the peak area of a particular compound eluted. The chromatographic peak corresponding to each compound was identified by comparing the retention time with that of standards. All standards and samples were injected in technical triplicate.
# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 3. Irrigation Remote Sensing
### Microwave-Based Methods
#### 3.2.1 Mapping Methods
The study was carried out over six maize plots in southwestern France and showed that the best retrieval of irrigation would be achieved with local measurements of surface soil moisture every 3-4 days. The authors also noticed that the technique is not adequate for the timing of small irrigation events (<10 mm) because of the 6-daily measurement frequency of S1, and that irrigation might be confused with rainfall events.
## 3 Results and Discussion
### In Vitro CT Imaging  
The use of AuNPs as a CT contrast agent is much more appropriate due to enhanced X-ray attenuation compared to the standard CT iodine-based contrast agent [66]. In order to assess the ability to use AuNPs as a contrast agent for CT, different AuNP concentrations of X-ray attenuation have been tested using agar as a control sample (Figure 7). Images were analyzed directly by using the MICRODICOM (v3.3.2) program based on the signal strength of the HU values. The intensity of the CT image has been shown to increase with the increase in Au concentration. Figure 7 (inset) shows that the value of AuNPs CT (HU) increases linearly with an increase in the concentration of Au (HU = 395) [67]. CT imaging experiments show that the AuNPs produced have a higher attenuation strength than the clinically utilized iodine contrast agent, Omnipaque (a popular iodine-based CT contrast agent currently used in the clinic) [68], at the same molar concentration of active elements (Au or iodine) and activate significantly enhanced CT imaging. Figure 7 reveals that 2.3 mg Au/mL was equal to 7.2 mg/mL of iodine in Omnipaque (viz., 178 HU) in X-ray absorption. It leads to an X-ray attenuation nearly 3.1 times that of the iodine contrast agent, which may be due to the presence of electron-dense nanogold. The AuNPs have a higher atomic number as well as density (79 and 19.32 g/cm\({}^{3}\), respectively) relative to iodine (53 and 4.9 g/cm\({}^{3}\)) leading to this effect [69]. Therefore, instead of conventional iodine-treated materials, such as barium sulfate or zirconium dioxide contrast agents, being used in CT, the toxicity of which is of major concern [70], we suggest the use of biocompatible nanomaterials produced via non-toxic methods, with high X-ray attenuation. The findings are consistent with a previous study [71]. [PERSON] et al., indicated that ultrafine AuNPs had more X-ray attenuation than larger counterparts, likely because of their higher volume-to-surface ratio [72]. This is extremely important because the ability of AuNPs to attenuate X-rays is needed as a contrast agent for the potential use of CT images. The in vitro (HU) findings support the effectiveness of AuNPs in CT imaging. Overall, the influence of AuNPs on the X-ray attenuation of contrast CT imaging agent depends on the concentration of AuNPs.  
Figure 6: Cell viability using WST-1 assay. HEK-293 cells treated with specific concentrations of AuNPs (0.1–0.5 mg/mL) for 24 h and 48 h. An asterisk indicates a significant difference between the experimental groups and control (using Mann–Whitney test, \({}^{**}\)_p_-value \(<\) 0.01).
## 5 Case Study  
### Overview of a Particular Bus Route in Nanjing  
Taking the departure frequency of a bus route in Nanjing as the research object, the departure schedule of the line is optimized considering the bus operating cost and the convenience of citizens. The bus passenger flow of this route during each period is shown in Table 5. The bus line is 21.4 km long, has 32 stations, with a fixed departure time interval of 15 min, the implementation of a uniform fare (2 yuan/person), and a line operation time of 6:30 a.m. to 10:30 p.m. (a total of 16 h).  
From Table 5, it can be seen that the peak hours of this route in Nanjing include the morning peak hour from 7:30 a.m. to 8:30 a.m., the noon peak hour from 12:30 a.m. to 1:30 p.m., and the evening peak hour from 5:30 p.m. to 6:30 p.m. The nonpeak hours refer to other time periods. The division of operating time periods is shown in Table 6.  
According to the established mathematical model of public transportation scheduling, in order to reduce residents' travel costs, enhance the attractiveness of public transportation, and adjust the travel structure, the weight coefficient is set, as shown in Table 7.  
The weight coefficient is determined by the analytic hierarchy process (AHP): the hierarchical structure model is used to compare the relative importance of each index, and the weight coefficient is calculated.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Time Period** & **Peak Type** & **Duration (in min)** \\ \hline
1 & 6:30–7:30 & Nonpeak hour & 60 \\
2 & 7:30–8:30 & Peak hour & 60 \\
3 & 8:30–12:30 & Nonpeak hour & 240 \\
4 & 12:30–1:30 & Peak hour & 60 \\
5 & 1:30–5:30 & Nonpeak hour & 240 \\
6 & 5:30–6:30 & Peak hour & 60 \\
7 & 6:30–10:30 & Nonpeak hour & 240 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Division of operating time periods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Time Period** & **Passenger Flow (Person)** \\ \hline
1 & 6:30–7:30 & 274 \\
2 & 7:30–8:30 & 486 \\
3 & 8:30–9:30 & 301 \\
4 & 9:30–10:30 & 224 \\
5 & 10:30–11:30 & 191 \\
6 & 11:30–12:30 & 260 \\
7 & 12:30–1:30 & 337
## Methodology
### Methods
For each cluster within a given range, [PERSON]'s Bayesian Criterion (BIC) is calculated as  
\[\mathrm{BIC_{R}}=\ -2\sum_{\mathrm{r}\ =1}^{\mathrm{R}}\ \xi_{\mathrm{r}}+ \mathrm{m_{r}}\mathrm{log}(\mathrm{N}) \tag{4}\]  
with  
\[\mathrm{m_{r}}=\mathrm{R}\Bigg{\{}2\mathrm{K^{A}}+\sum_{\mathrm{k=1}}^{K^{B}} \left(\mathrm{L_{k}}-1\right)\Bigg{\}} \tag{5}\]  
where  
\(\mathrm{BIC_{R}}\)--Bayesian information criterion;  
\(\mathrm{m_{r}}\)--ratio in r-cluster developed during the hierarchical clustering stage;  
\(\mathrm{L_{k}}\)--number of groups in k categorical variables [41]. The \"optimal\" number of clusters has the lowest BIC value, and a lower BIC value indicates the optimal number of clusters. Furthermore, the huge ratios of the distance measures and BIC changes are also tracked. Nevertheless, in the statistical-analytical application IBM SPSS 29, the ideal number of clusters is automatically determined [42] without the author's input group calibration. The silhouette value expresses how cohesively an object belongs to its cluster, as opposed to how separate it is from other clusters. The average distance between a sample and every other data point in the same cluster is known as the cluster cohesiveness. On the other hand, the average distance between a sample and every other data point in the closest cluster is denoted by the cluster separation [43]. The range of this metric is 1 to \(-1\). The silhouette value identifies  
* poor classification from \(-1.0\) to 0.2;
* fair classification from 0.2 to 0.5;
* good classification from 0.5 to 1.0. \[\mathrm{s(i)}=\frac{\mathrm{b(i)-a(i)}}{\mathrm{max(b(i),\ a(i))}} \tag{6}\]\[\overline{S}=\frac{1}{N}\sum_{i=1}^{N}S_{i} \tag{7}\]  
where  
\(S_{i}\)--silhouette coefficient for i-th object,  
\(b_{i}\)--average of minimum distance between i-th object in different cluster (average inter-cluster distance);  
\(a_{i}\)--average of minimum distance between i-th object in same cluster (average intra-cluster distance);  
S--average value of silhouette coefficients;  
N--total number of observations [44].
## 1 Introduction
The RVE size for a single-phase polycrystalline pure copper was determined for 3D polycrystalline pure copper with computational homogenization [23; 24]. RVE size for monotonic and cyclic loading was determined for single-phase polycrystals with full-field computational homogenization using CPFEM [25; 26; 27]. However, the study of size dependency using the moving window and SVE approach within CPFEM is a recent idea. There are many aspects that affect the RVE size and, in general, the size dependency of a material and its corresponding formulated models. The existence of an internal length scale in a material model is a major source of size dependency due to its interaction with the domain size. Examples include gradient and generalized elasticity models [28], non-local [29] and gradient [30] damage models, and phase field fracture models [31; 32]. Another influence is the stochastic nature of material microstructure, i.e., (micro)crack, void, and grain distribution. As the domain size increases, the likelihood of encountering more critical defects increases, resulting in another form of size effect [33]. When modeling the plasticity response, the grain size significantly contributes to various size effects. In general, intrinsic factors such as the grain size, dislocation density, grain boundary effects and the domain size as an extrinsic factor result in size dependency [34]. For example, smaller grains have been shown to increase material strength by restricting dislocation motion [35; 36; 37]. As previously mentioned, internal length scales also introduce a size effect. CP models, however, are often devoid of such length scales. Because gradients in strain are particularly significant near grain boundaries and interfaces, the use of CP models in combination with internal length scales and gradient effects is advocated [38]. The goal of this study is to evaluate the size dependency of elastic and plastic properties of two-phase polycrystalline materials. SVE-based statistical homogenization is performed in a CPFEM framework for ferrite and martensite materials with varying volume fractions. Convergence rates for different properties are evaluated through a statistical study to determine the size dependency and calculate the RVE limits. The size effects study is focused on the stochastic nature of material microstructure. Specifically grain orientation and phase distribution. The size effect due to grain size is not significant when the grain size distribution is constant across all analyses [35], as in our study. Moreover, Ref. [39] has shown that the effect of the internal length scales diminishes as the domain size becomes 10 to 100 times larger than the internal length scale. The grain size is typically the length scale in CP models [34; 38; 40]. In this research, the smallest domain is 10 times larger than the grain size, therefore, excluding the internal length scale effects is reasonable herein.
## 4 Optimization of Grouting Materials
### Laboratory Test of Grouting Materials  
The rock-soil mass structure of the injected formation is loose, for which the crack or hole rate is high. The infiltration grouting material should have good fluidity and stability, reasonable cementing time, and large solidification strength. Therefore, the laboratory test needs to test the viscosity of the grouting materials (the model 1006 mud viscosity is often used for measurement and the unit is \"s\"), water separation rate, gel time, stone rate, compressive strength, and other parameters to provide a basis for the rational selection of grouting materials.  
A large number of experimental studies [19; 20; 21] show that the critical value of the water-cement ratio for cement slurry to convert Bingham fluid to Newtonian fluid is 1:1. Therefore, the water-cement ratio of the slurry in this test is close to or equal to the critical value. The water cement ratio is 0.8:1, 1:1, and 1.2:1. Performance analysis was conducted on twelve groups of proportioning materials under four conditions, namely, ordinary Portland cement slurry, ordinary Portland cement external accelerator slurry, ultra-fine cement slurry, ultra-fine cement external accelerator, and water reducer slurry (Table 2).