esa-sceva/satcom-chunk-collection huggingface dataset:
# Correction of Aero-Optical Effect with Blow-Suction Control for Hypersonic Vehicles
## 3 Methods and Validation
### Validation of Numerical Method  
Although the geometric model of the step compression corner after supersonic flow is straightforward, the flow structure of separation and reattachment exhibits significant similarities with the flow field structure outside the window of future hypersonic seekers. As a numerical example, [PERSON]'s [28] cavity compression corner model is selected as an experimental reference to verify the accuracy of the turbulence model and align with the flow problem studied in this paper. The step height \(h\) = 2.54 cm, the rear wall length \(l\) = 6.19 cm, and the compression corner angle \(\theta\) = 20\({}^{\circ}\). The mesh generated by the pointwise and geometric parameters is illustrated in Figure 8 below.  
FLUENT (2023 R1) is a popular computational fluid dynamics (CFD) software, which has advanced numerical calculation methods. In this paper, FLUENT was used to calculate the temperature, velocity, and pressure distribution of the flow field. Under the working conditions of the wind tunnel test, the incoming Mach number is 2.92, the incoming flow temperature is 95.37 K, and the incoming pressure is 21.24 kPa. Initially, a steady flow field structure is obtained through steady calculation; subsequently, an unsteady flow field structure is acquired using the DDES method, with \(C_{\text{DES}}\) = 0.78 and \(\beta^{*}\) = 0.09, while employing the Roe format for the spatial discretization technique. LU-SGS performs time propulsion with physical time steps set at 3 \(\times\) 10\({}^{-7}\) s. The contour diagram of the time-averaged Mach number and eddy viscosity of the flow field is depicted in Figure 9, while Figure 10 illustrates the root-mean-square distribution of the flow velocity at stations \(x/h\) = 1.5 and \(x/h\) = 3.5.  
It can be observed that at the \(x/h\) = 1.5 station, the position is situated within the cavity of the specimen, resulting in a complex flow structure. The velocity results obtained from the numerical simulation exhibit excellent agreement with the experimental values. At the \(x/h\) = 3.5 location, it is positioned on the wall of the compression corner, behind the cavity. In comparison to the flow inside the cavity, the flow structure here appears relatively simpler; however, the calculated values slightly exceed the test values at \(y/h\) = 0-0.5 range. To summarize, employing the SST-DDES method yields satisfactory RMS speed outcomes, and thus this this method can be used to study the problem in this paper.
## 3 Results
### Performance of TB-MES with Domestic Wastewater
The TN removal in TB-MES was mainly occurred in biocathode, through the combination processes of nitrification, heterotrophic denitrification and bioelectrochemical denitrification. This was really a complicated process, where nitrate could be reduced directly by using organic matter as electron donor, or indirectly by obtaining electrons from biocathode supply. After treatment by the TB-MES, both the COD and nitrogen concentration in the effluent could meet the discharge standard of pollutants for municipal wastewater treatment plant. Therefore, the TB-MES held great promise for efficient domestic wastewater treatment and energy recovery. Figure 5: Variation of (**a**) COD concentration and COD removal and (**b**) NH\({}_{4}^{+}-\)N,NO\({}_{3}^{-}-\)N and NO\({}_{2}^{-}-\)N concentrations, as well as ammonium nitrogen removal and TN removal of TB-MES fed with domestic wastewater (Inf: influent, Eff: Effluent).
# Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos
## 1 Introduction
### Research Objective  
The main objective of this research is to accurately estimate the straining actions on flexible wall silos. This study discusses the use of finite element models to predict pressures in silos with considerable wall deformations during the filling stage. The study will define the behavior of bulk solids and the stresses they apply on walls to provide information on:  
* The pressure distributions in square rigid and flexible wall silos.
* The effect of wall deformability.
* The validation of the numerical models using experimental data and the comparison with the Janssen equation taking into consideration its assumptions and limitations.
## 4 Discussion
### Synergies of Camera, Marker Color, and Lighting  
The resolution of an optical system relates to the interplay of multiple elements; in this case, camera resolution, sensor sensitivity to given colors, and the impact of lighting on detection.  
The Apple iPhone 8 camera used in these studies was 8 megapixels with 1.5 m pixels with a 1334 \(\times\) 750-pixel resolution, i.e., 326 pixels/inch, which is more than adequate to effectively resolve a 2 mm object over the distance ranges tested [42]. The color sensitivity of the camera sensor ranges from 380 nm to 660 nm, with an f/2.2 aperture providing good low-light detection, with 1080p HD video recording up to 60 fps. As such, these specifications provide a more than adequate sensitivity over a range of operating conditions, marker color choice, and lighting conditions to afford system flexibility [43, 44]. The camera also offers significant depth of field allowing for in-focus resolution of the markers over the range tested, as we observed [45]. In utilizing the bright neon colors pink and green, these afford adequate contrast to typical flesh tones or clothing pastel colors, enhancing accuracy of detection. Our finding of a greater sensitivity to pink versus green, affording a greater working length with this color, relates to the sensor detection of the iPhone CMOS chip, though other factors may be operative as well. Nevertheless, this determination is useful in color choice for future patient studies. We also tested lighting intensity over a practical use range that individuals will encounter at home or in the clinic. We found as anticipated that the sensitivity of detection was enhanced with increased illumination, a characteristic of current digital camera CMOS sensor technology [46].
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Blood Pressure
#### 3.3.2 Photoplethysmography and Pulse Transmit Time  
Pulse Transit Time (PTT) is the time a pulse wave takes to travel between two different arterial points, which is an important cue to estimate blood pressure. Several studies were carried out to obtain the correlation between PTT and blood pressure. The authors of [53] introduced a contactless approach to estimate blood pressure using PPT, with seven healthy subjects. Image based (iPTT) and image-based PPG were recorded using a high speed camera at 426 Hz during physical exercise in a stationary bicycle. The exercises were carried out at three different times: rest, peak exercise, and recovery. The study found a highly positive correlation between iPTT and iPPG during exercise, concluding that measuring BP using PTT is reliable. It was shown that skin color changes due to blood pulsation and such changes could be identified by the three-color component processing in facial images.
# Study on the Vibration Variation of Rock Slope Based on Numerical Simulation and Fitting Analysis
## 4 Numerical Simulation of Slope Effect
### Vibration Response Analysis of Slope Blasting
It can be learned from Figure 10 that the attenuation rates of the four working conditions were 21.67%, 29.18%, 31.42%, and 43.16%, respectively, and the changing trend of the vibration velocity attenuation rate was opposite to that of the slope angle. When the slope angle becomes smaller, the attenuation rate of vibration velocity becomes larger, and the elevation effect becomes less significant. Therefore, it can be inferred that when the slope angle is 0, a flat surface will be formed, and the change of blast vibration velocity will not be affected by the height difference. When the slope angle is 90\({}^{\circ}\), the slope surface is perpendicular to the bottom surface of the pit, and a greater vibration velocity will occur at the top of the slope. Due to the limited research content, only the trend of slope vibration velocity variation at 0-90\({}^{\circ}\) can be inferred. In terms of amplification, a significant amplification of 29.71% was observed in working condition I, indicating that the slope effect increases with the increase of slope angle. The amplification rates under working conditions III and IV were \(-\)6.68% and \(-\)20.76%, respectively, and the vibration speed at the characteristic points A\({}_{1}\)-A\({}_{6}\) showed an attenuation trend, but the attenuation was not drastic. This phenomenon can be explained by the whipping effect. When the terrain is flat and homogeneous, the stress state is the same all over the stratum, and there is no big difference in the stiffness and stability of the geotechnical body. The slope area can be interpreted as the protruding part of the stratum, which was subject to weaker constraints and loads than the flat stratum and was less stable [15]. Therefore, compared with the flat stratum, the slope area was more likely to have a larger vibration velocity. Figure 9: The peak vibration velocity decay curve of the A\({}_{1}\) and A\({}_{6}\) points.
# Analytical Investigation of a Novel System for Combined Dew Point Cooling and Water Recovery
## 5 Conclusions  
The idea of the DPC-WR system is to utilize the dew point cooling phenomenon implemented in two stages in order to obtain both air cooling and water recovery. The system performance is described by different indicators calculated for various climatic zones using a validated analytical model based on the convective heat transfer coefficient.  
It is concluded that the DPC-WR system can achieve the COP and GOR values, being an order of magnitude higher than for other cooling and water recovery technologies. The highest COP and GOR values are noted in semi-arid, hot desert, and subtropical climatic zones, which are characterized either by high air temperature or by low humidity ratio, or by both. On the other hand, the lowest COP and GOR values are noted in tropical climatic zones, which are characterized by the high air humidity ratio.  
The SEC value for the DPC-WR system ranges from 0.5 to 2.0 kWh/m\({}^{3}\) and the SDWP value ranges from 100 to 600 L/day/(kg/s). It is stated that SEC and SDWP are most favorable in climatic zones where COP and GOR are highest, and respectively, SEC and SDWP are least favorable where COP and GOR are lowest.  
Performed analyses proved that there are climatic zones where the effectiveness of the DPC-WR system will be exceptionally high. Nevertheless, it was also proved that even in less favorable climatic zones where either air temperature is low or humidity ratio is high, combined cooling and water recovery is still possible with acceptable effectiveness (COP > 100, GOR > 100, SEC < 2.0 kWh/m\({}^{3}\), SDWP > 100 L/day/(kg/s), and average EUF = 0.90).  
The proposed DPC-WR system requires future research and development, which include the full-scale prototype construction. One of the limitations that needs to be addressed is the use of proper materials that allow for effective distribution of water in the secondary channels, and for intensive removal of condensed water from the primary channels. Future research will also address the optimization of the DPC-WR system in order to obtain the most suitable operating parameters for the conditions in which the system will be used. So far, the research carried out shows that the DPC-WR system is a promising technology for combined cooling and water recovery that can be applied in various climatic zones. Moreover, the DPC-WR system is a modular technology, which means that it is suitable for micro, small, and large applications. By multiplying the system modules as needed, any cooling or water recovery capacity can be achieved without affecting the system effectiveness.
## 1 Introduction
In addition, the strength of the stiffened connections was estimated based on the yield lines on the chord face occurring along the perimeter of the stiffener. [PERSON], [PERSON], and [PERSON] [9] investigated the effects of compressive and tensile loads applied to the chord member on the connection strength. The results showed that compressive loads significantly reduced the chord face plastification strength, while tensile loads had little effect. Figure 1: Rectangular hollow structural section truss for long-span building structures: (**a**) Rectangular hollow structural section (RHS) roof truss; (**b**) Longitudinal plate-to-RHS K-connection. At the plate-to-RHS connection with chord face stiffener (see Figure 2b), the stiffener can reduce deformation of the chord face and can increase strength and stiffness by distributing the force of the longitudinal plate directly to the side wall of the chord member. According to the previous studies by [PERSON], [PERSON], [PERSON], etc. [5; 6; 7; 8; 9], the strength of such stiffened connection increases as the thickness and width of the stiffener (i.e., \(t_{s}\) and \(b_{s}\), respectively) become larger. In addition, the connection strength agrees well with the chord face plastification strength based on the yield line theory. However, the previous studies have focused mainly on T- or Y-connections, and thus, available test data on the behavior of the K-connections which are common in roof trusses with slope are rare. This study investigated the behavior of RHS truss K-connections with chord face stiffener. Cyclic loading tests of three truss connections where one hollow chord member and two composite branch members were jointed were performed, and the stiffening effects and governing limit states depending on the chord face stiffener were investigated. The theoretical strengths of the K-connections with and without the chord face stiffener were compared with the test strengths, and the applicability of current design codes was discussed.
## 4 Materials and Methods  
### Plant Material and Growth Conditions  
All the plant material (petals, leaves, and fruits) used in this study were harvested from adult trees of oranges (_Citrus sinensis_ L., cv. Valencia Late) grafted on Citrange carrizo rootstock (_Citrus sinensis_\(\times\)_Poncirus trifilata_) grown at the Citrus Germplasm Bank of the Instituto Valenciano de Investigaciones Aggararias (+39\({}^{\circ}\)35\({}^{\prime}\)22\({}^{\prime\prime}\), \(-0\)\({}^{\circ}\)23\({}^{\prime}\)40\({}^{\prime\prime}\), Moncada, Valencia, Spain). Leaves were collected at two developmental stages: young developing leaf (Y) and mature about 12 months old (M). Petals were carefully collected from closed flowers (CF) and at anthesis (AF). Fruits were harvested at 10 developmental stages: from small fruitlets (June and July) collected after the physiological June-drop to full mature fruits (March) and over-mature fruits (about 14-months after flowering, indicated as June*). Immediately after harvest, the plant organs were delivered to the laboratory and selected for uniformity and the absence of any lesion or injury. The fruit equatorial diameter was measured in 20 fruits with a manual caliper, and the same fruits were used to determine fruit weight during development. Peel color of the same fruits was measured using a Minolta CR-330 colorimeter (Minolta, Osaka, Japan) on three locations around the equatorial plane of the fruit. Color index was expressed as the \(a/b\) Hunter ratio [66; 67], which is negative for green fruit, around zero for yellow fruit at color break, and positive for orange colored fruit. The flavedo (outer colored part of the fruit peel) and pulp of fruits at the different development stages were separated with a scalpel. All plant tissues were frozen in liquid nitrogen, ground to a fine powder and stored at \(-80\)\({}^{\circ}\)C until analysis.
## 2 Materials and Methods
### Device Setup and Data Collection
#### 2.2.2 Experimental Setup  
Platform Leveling: Prior to each experiment, the microdevice is positioned on a precision-leveled platform to minimize potential disturbances caused by any tilting or unevenness.  
Number of tests: Five devices were fabricated, which were reused after removing the adhesive from the top, cleaning with distilled water, and drying with compressed air (5 bar). The experiment was repeated three times on each device, resulting in a total of 15 tests.  
Sample Preparation: A precisely measured 20 \(\upmu\)L volume of high-purity distilled water (grade 0) is loaded into a calibrated pipette. This distilled water is pre-prepared with a biocompatible vegetable dye to visualize the fluid flow. Figure 3 shows the main parts of the manufactured device.  
Color Placement and Sequential test: The experiment begins by introducing blue-colored distilled water into the designated reservoir 1 (Figure 5a). The capillarity-driven flow spontaneously initiates, propelling the blue fluid into the microchannel. Once the microchannel is sufficiently filled with the blue color, it stops at the end of the channel due to a trigger valve (TV). Figure 4 illustrates the detailed view of the TV (Trigger Valve) component within the device. Subsequently, yellow-colored distilled water is introduced into reservoir 2 (Figure 5b). At the end of the channel, it triggers the blue liquid to cross through the mix tank, initiating the sequential mixing process. This allows for the observation and analysis of the mixing efficiency as the two colored fluids merge and progress through the microchannel network.  
Figure 4: Trigger valve (TV). (**a**) Stop valve at end of channel 1, (**b**) 3D view of TV, (**c**) TV microscope image.  
Figure 3: Data analysis process.  
According to the literature review, a trigger valve (TV) effectively operates when the gap between the channel and the bottom of the main channel maintains a minimum difference of 300 um [43]. In this configuration, the liquid in the small channel halts, awaiting activation, and is triggered once the main channel is filled with another liquid. In our current device, this gap measures 400 um. The small channel is filled with blue distilled colored water, while the main channel contains yellow distilled colored water.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 4 Methods for Measuring the SUHI
### LST Differences between Urban and Surrounding Reference Areas  
The intensity of SUHI has been most commonly estimated by the LST difference between urban and surrounding reference areas, with the support of auxiliary land surface information, such as land cover and ISA [30]. SUHI intensity was typically quantified in two steps in those studies. First, urban and reference areas were defined and delineated from the land cover or ISA maps. Urban areas were usually referred to as the lands with a relatively higher portion of ISA [4, 6, 52, 76], whereas reference areas were defined variously by different studies. Different-sized rural [52, 53, 76, 109] and suburban [2, 4, 6, 62, 110, 111, 112, 113, 114, 115] zones have been used as the reference areas. Other surface cover, such as water body [116, 117], cropland [117, 118, 119], forest [120], and low-intensity ISA [121] have also been taken as the references in a few studies. Second, the area-weighted mean urban-reference LST differences were calculated to reflect the SUHI intensity or magnitude. A positive value of SUHI intensity indicated an urban heating effect, while a negative value represented a sink effect. Few studies also quantified the SUHI intensity using few representative pixels in urban and reference areas instead of the area-weighted mean value for the purpose of surface-air UHI comparison [122, 123, 124] or UHI attribution analysis [125, 126]. The urban-reference difference method facilitates the comparison analysis of SUHIs among cities, in particular the SUHI distribution at a regional or global scale, but can be limited by the large uncertainties associated with urban and reference definitions [68]. This limitation is discussed further in Section 6.4.
# Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles
## 2 Material and Methods
### Experiment Designation  
This study comprises four distinct experiments (Figure 1). Firstly, we evaluate our model based on the PY estimation from EARTHSTAT and other crop models. Secondly, we conduct simulations to examine the impacts of climate change on wheat growth. Throughout the model configuration process, we omit the potential variation in wheat phenology, opting to use a fixed sowing date derived from the year 2000 CE. Subsequently, our focus shifts to predicting alterations in the sowing date through an approach centered on optimizing wheat yield. This involves forecasting shifts in the sowing date that are conducive to attaining optimal yield outcomes. Lastly, this study endeavors to synthesize the climate's influence on wheat growth with the dynamically adjusted sowing date. The PYs are predicted based on wheat planting area proportion and the predicted PY of each grid. The temporal variation trend is then calculated by applying a linear fit for each major wheat-producing province. Within the scope of these simulations, we operate on a weekly temporal resolution. This choice is underpinned by the recognition that plants necessitate a period to adapt and acclimate to their surroundings.
# Add projections
## 6 Data Classification  
The logistic regression algorithm is a popular binary classification algorithm [14]. In this model, the probabilities describing the possible outcomes are modeled using a logistic function known as sigmoid function. This function estimates the probability of an outcome to a value of 0 or 1, which helps to determine the class of output variable. It is an S-shaped curve that can take any real-valued number and map it into a value between 0 and 1. Usually, zero is predicted as negative class and 1 as positive class.  
Below is an example of the logistic equation. Input values (x) are combined linearly using weights or coefficient values to predict an output value (y). The output value being modeled is a binary value (0 or 1) rather than a numeric value which denotes the probability of the class.  
\[Y=\mathcal{E}(B0+B1*x)/1+\mathcal{E}(B0+B1*x)\]  
where  
\(B0\): Bias or intercept term  
\(B1\): Coefficient of single input value (x)  
In the Mg-alloy data, we chose the tensile properties YS, UTS as the independent input variables to the algorithm to predict the dependent output variable ductility. We have applied a logistic regression model to classify the Mg-alloy data. The classification result one is classified as ductile metal and zero as brittle metal. The classification accuracies are calculated, and spot check algorithm comparison is done for all three classification models; namely, Logistic regression, Support vector machine (SVM) and Random forest.