esa-sceva/satcom-chunk-collection huggingface dataset:
# Bumblebee Pollination Enhances Yield and Flavor of Tomato in Gobi Desert Greenhouses
## 3 Results  
### Tomato Yield  
Tomato fruit set showed significant differences under different pollination treatments (Figure 2, GLM, pollination treatment: \(F_{2,216}=44.983,p<0.001\)). Greenhouse had no effect on the fruit set (GLM, greenhouse: \(F_{2,216}=0.126,p=0.882\)). Tomato flowers pollinated by bumblebees had the highest fruit set (90.4% \(\pm\) 11.2%), followed by tomato flowerstreated with PGR (79.2% \(\pm\) 16.8%) and vibrator (62.3% \(\pm\) 22.8%). Tomatoes pollinated by bumblebees also had more stable fruit set (smaller CV, CV = 12.6%) than PGR (CV = 21.2%) and vibrator (36.5%).  
Significant differences were also found in seed quantity (Figure 3A, GLM, pollination treatment: \(F_{2,152}\) = 215.5, \(p\) < 0.001) and fruit weight (Figure 3B, GLM, pollination treatment: \(F_{2,152}\) = 11.370, \(p\) < 0.001), but not in fruit diameter (Figure 3C, GLM, pollination treatment: \(F_{2,152}\) = 1.535, \(p\) = 0.219) under different pollination treatments. Bumblebee pollinated tomatoes had the largest seed quantity (115.9 \(\pm\) 19.8), followed by tomatoes treated with vibrator (84.5 \(\pm\) 25.0) and tomatoes treated with PGR had the fewest seeds (48.1 \(\pm\) 21.1). Tomatoes pollinated by bumblebees also had a larger fruit weight (158.5 \(\pm\) 30.1 g) than tomatoes treated with vibrator (133.5 \(\pm\) 30.9 g) and PGR (130.3 \(\pm\) 36.4 g).  
Figure 3: The seed quantity (**A**), fruit weight (**B**), and fruit diameter (**C**) of tomato fruit by different pollination treatments. A total of 157 tomato fruits from three greenhouses were collected and measured. Data are presented as the mean \(\pm\) 95% confidence interval. General linear models were used to compare the seed quantity, fruit weight, and fruit diameter of tomatoes by different pollination treatments. Different letters indicate significant differences in fruit set based on the Duncan test at \(\alpha\) = 0.05.  
Figure 2: Fruit set of tomato by different pollination treatments. A total of 221 tomato plants from three greenhouses were observed for fruit set. Data are presented as the mean \(\pm\) 95% confidence interval. A general linear model was used to compare the fruit set among different pollination treatments. Different letters indicate significant differences in fruit set based on the Duncan test \(\alpha\) = 0.05.  
Positive correlations were found between seed quantity and fruit weight in tomatoes from the bumblebee pollination or vibrator treatments. However, seed quantity was unrelated with fruit weight in tomatoes from PGR treatment. (Figure 4). Like the tomato fruit set, the tomato seed quantity (GLM, greenhouse: \(F_{2,152}=0.138\), \(p=0.872\)), fruit weight (GLM, greenhouse: \(F_{2,152}=0.255\), \(p=0.775\)), and fruit diameter (GLM, greenhouse: \(F_{2,152}=1.880\), \(p=0.156\)) did not differ in different greenhouses.
# Instant Center Identification of Single-Loop Multi-DOF Planar Linkage Using Virtual Link
## 3 Virtual Link Method for Instant Center Identification
### Criteria for Instant Center Identification
#### 3.1.2 Virtual Link Operation  
According to discussion above, for single-loop multi-DOF N-bar (N \(\geq\) 5) planar linkage, two criteria and corresponding virtual link operation are presented to locate instant centers as follows.  
**Criterion 1:** For a single-loop multi-DOF N-bar (N \(\geq\) 5) planar linkage, add the virtual links without changing instantaneous configuration to take shape the two-loop virtual linkage in which the instant centers are easy to be determined. Based on mathematics definition of instant center, some of the obtained instant centers in virtual linkage are equivalent to the ones in the original linkage.  
**Criterion 2:** Based on coupled loop chain characteristic, in a certain linkage, if several common instant centers exist when combining any two loops of the linkage, once any pair of common instant centers obtained from different loop chain come cross, the rest common instant centers must coincide, accordingly.  
Note that, virtual link operation is an assumption, and the virtual links are unreal. Virtual link operation is an auxiliary means to change the single-loop linkage into a virtual two-loop linkage which do not affect the motion of the original single-loop linkage. That is, the virtual two-loop linkage is actually still the single-loop linkage. The inputs and the motions of the links (not including the virtual links) in the virtual two-loop linkage are the same compared to the original single-loop linkage. The corresponding mathematical proof is discussed in detail in Section 4.
## 2 Background
### Topic Tracking
Nevertheless, only a limited number of works have tackled real-time topic detection in Twitter. [PERSON] and [PERSON] [31] proposed a method to detect emerging topics on Twitter using high utility pattern mining (HUPM), which takes the frequency of appearance and the utility of words into account. Although their approach gives good results when detecting topics in known datasets, it is not designed to dynamically use the resulting topics for extraction. With a different approach, the work of [PERSON] et al. [32] aims to detect relevant events from a set of twitter posts, but it displays a similar problem as the analysis is conducted in postprocessing, instead of detecting events in near real time and tracking them to extract all the relevant tweets produced by the users. Such adaptation is found in [PERSON] et al. [33], who proposed a system able to progressively refine its query to include new relevant terms, reflecting the emergence of new topics or trends. In their conclusions, they also noted how \"other systems were unable to capture the social aspects of the observed events [...] every time the users left the main topic and started to talk about unexpected events\". Their work, however, presents a couple limitations that could have a relevant impact in many contexts, as an initial set of hashtags must be provided in advance, and afterwards, the adaptation is solely based on the extracted tweets, without considering the relevancy (or authority) of their authors. This could easily become an issue, as irrelevant hashtags can get into the system, gradually drifting to diverging topics. Our proposedsolution aims to overcome the previous shortcomings by proposing a method which works in real time, provides precise outputs, and is adjustable against spurious emerging topics. It is important to clarify that the problem our model solves is a simplification of the general problem of trend detection and monitoring. As previously mentioned, other works monitor hashtags or topics in the tweets published by any user, while we analyze those tweets produced by a known set of users only. This set of users is not necessarily fixed--this can be customized--but it is quite stable and constrained. In any case, the size of the corpus is still enormous, and the variations in time and volume scale of our datasets are still there. To control these two aspects, and to do so dynamically in one pass is still challenging. As we will see in the next section, our solution provides excellent results without the need for any preprocessing or the creation/use of additional corpora.
## 4 Case Study and Results
### Investment Costs and Revenue  
Notwithstanding the fact that the scope of this paper is on evaluating the impact of short-term operational planning cost savings of the MVDC interface when compared to individual P2 Ps, a simple cost analysis was carried out to evaluate the operational cost savings in Table 4, weighing against the initial investment cost of the MVDC implementation. Investment costs to build SOP and  
Figure 11: The comparison of loading deviations from the base case.  
Figure 12: The comparison of tap ratio deviations at different case studies.  
new distribution lines have been estimated in [34] as given in Appendix B. Referring to Table A1, the investment cost for two VSC stations in back-to-back configuration (i.e., SOP) is \(\epsilon\)90,000 or approximately $57,000 per VSC station and the cost for a new line installation is \(\epsilon\)65,000/km or approximately $131,000 per mile in length. We assume the distance between feeders is around half a mile for the additional lines and make use of the existing tie lines to form a MVDC interface in this study. Table 5 presents an estimated investment cost and return on the investment for the analysed configurations. A net present value (NPV) for the revenue generated from each technology is calculated from the total operational cost savings during period \(t\) (denoted as \(Revenue_{t}\)) and the total initial investment cost (denoted as \(Investment_{0}\)) using the expression in Equation (20) with an interest rate, \(dr\) at 5%. The first year operational savings of the P2P and MVDC configurations can obtained from the difference between operational cost of the base case and the analysed configuration in Table 4. The operational costs are expected to increase over the years (due to inflation) and therefore, total operational cost savings vary at period \(t\). For simplicity, we consider the operational costs increased in proportion to the interest rate, \(dr\), and the lifetime of the invested technologies, \(T=30\). As shown in Table 5, the NPV at 30 years for the MVDC configuration is higher than the P2P configuration, which means MVDC is more profitable. The payback period is also presented in Table 5, obtained when the NPV = 0 (i.e., total revenue equals to the investment cost). It is clearly shown that the MVDC configuration requires less than two additional years as compared to the P2P configuration in order to get payback from the total investment cost. This indicates that the implications on the investment cost are not drastic and can be compensated with the potential technical benefits that can be gained in terms of demand shifting, DG power curtailment, line loadings, and tap ratio settings as discussed earlier.  
\[\text{NPV}=\sum_{t=1}^{T}\frac{Revenue_{t}}{(1+dr)^{t}}-Investment_{0} \tag{20}\]
# Analytical Methods to Assess Polyphenols, Tannin Concentration, and Astringency in Hard Apple Cider
###### Abstract  
The production of hard apple cider frequently involves considerations based on the balance between fermentable sugars and tittatable acidity and/or pH. However, these are not the only attributes that influence the sensory properties of cider. Various groups of polyphenols and tannins influence bittermess, astringency, and mouthfeel, as well as color of the product. In this study, 19 single variety hard apple ciders were evaluated using analytical methods originally developed for wines. The aim was to determine how the cider matrix affects the functionality of each method. The results indicate that most assays work with sufficient accuracy with the exception of protein precipitation methods. Due to interferences of polysaccharides with the protein reaction, those assays are poorly reproducible and do not reflect the actual polyphenol or tannin concentration. The analytical determination of astringency is also influenced by this interference, since it traditionally uses the concept of protein precipitation as well. Liquid chromatography as a selective method can be readily applied to cider. Other colorimetric methods or non-protein precipitation methods, such as the Folin-Ciocalteu assay, the Methylcellulose Precipitation assay, and the Iron-Reactive Phenolics assay, can be used to classify apple ciders and make decisions based on the desired bittermess and astringency profile.  
cider; tannins; astringency; polysaccharides; phenolic profiling Article  
Analytical Methods to Assess Polyphenols, Tannin Concentration, and Astringency in Hyd Apple Cider  
1 Grape and Wine Institute, University of Missouri, 223 Eckles Hall, Columbia, MO 65211, USA 1  
2 Viticulture and Emology Research Center, California State University, 2360 E. Barstow Ave., Fresno, CA 93740, USA 2
# Biomechanical Study of Proximal Femur for Designing Stems for Total Hip Replacement
## 2 Materials and Methods
### Boundary Conditions  
With the increasing success of hip replacements, the decreasing average age of patients and growing expectations about the implant durability, [PERSON] et al. (2017) measured the contact forces in the joint, using instrumented stems in ten people during nine of the most physically demanding and frequent activities of daily living. This study is performed considering that pre-clinical strength tests on stems require realistic in vivo loads from younger and more active patients. Likewise, these loads require simplifications to be applicable for simulator tests and numerical analyses (2017). These loads depend of the body weight of each patient. In this study we employ the average weight of Peruvian men (71.3 kg). For obtaining the correct loads (shown in Table 4) a factor is used, which results in dividing the average Peruvian men's weight by 100 kg, because the data from the reference study were given for that average weight. ISO (International Standardization Organization) force, employed widely to test the stems according to the ISO 7206-4 standard, was also considered.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Yield Limits (MPa)** & **Cortical Bone** \\ \hline \(\sigma_{z}^{T}\) & 35.5 \\ \hline \(\sigma_{x}^{T}\), \(\sigma_{y}^{T}\) & 21.3 \\ \hline \(\sigma_{z}^{C}\) & 71.1 \\ \hline \(\sigma_{x}^{C}\), \(\sigma_{y}^{C}\) & 42.6 \\ \hline \(\sigma_{xy}\) & 10.7 \\ \hline \(\sigma_{yz}\), \(\sigma_{xx}\) & 17.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Yield limits of cortical bone (tensile, compression and shear limits).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tsai–Wu Coefficients** \\ \hline \(F_{1}\) (mm\({}^{2}\)/N) & 0.0141 \\ \hline \(F_{2}\) (mm\({}^{2}\)/N) & 0.0235 \\ \hline \(F_{11}\) (mm\({}^{4}\)/N\({}^{2}\)) & \(3.96\times 10^{-4}\) \\ \hline \(F_{22}\) (mm\({}^{4}\)/N\({}^{2}\)) & \(1.1\times 10^{-3}\) \\ \hlineProximal femur was placed in \(\text{NX}^{\textregistered}\) according to the femoral coordinate system [30] and was rotated 45 degrees clockwise around the Y axis for simulating the load transfer that occurs between the hip and femur in the acetabulum (Figure 4B) and considering the accepted neck shaft angle of 135\({}^{\circ}\)[31]. This angle was used to cut the femoral neck to insert the stem into the femoral cavity, so it may be possible compare the stress between intact and implanted femur applying the same loads to the stem because it will be in the same position (Figure 4A). This region was composed of 44,772 nodes, upon which the forces were applied (Figure 4C). The flat parts of both bones were fixed.
## 3 Results and Discussions
### Temperature Distribution and Solidification Process  
The surface temperature of the moving die mold was captured by a thermal image (shown in Figure 8). When the die mold was in a position where it made direct contacted with the liquid metal, it had the highest temperature. The temperature of the internal position of the die mold decreased sharply. Additionally, as the melt injection temperature increased, the die surface temperature increased accordingly. When the melt injection temperature was 620 \({}^{\circ}\)C, the highest die surface temperature was about 265 \({}^{\circ}\)C. When the melt injection temperature was increased to 680 \({}^{\circ}\)C, the highest die surface temperature was 321 \({}^{\circ}\)C.  
Figure 6: Simulation results of the liquid wave and air entrainment in a chamber during the slow stage of injection: (**a**) piston velocity was 0.1 m/s, (**b**) piston velocity was 0.2 m/s, (**c**) piston velocity was 0.3 m/s and (**d**) piston velocity was 0.4 m/s.  
Figure 8: Thermography images of ejector die after the castings being ejected under conditions of different melt injection temperature: (**a**) melt injection temperature was 620 \({}^{\circ}\)C, (**b**) melt injection temperature was 640 \({}^{\circ}\)C, (**c**) melt injection temperature was 660 \({}^{\circ}\)C and (**d**) melt injection temperature was 680 \({}^{\circ}\)C.  
Figure 7: Simulation results of a fluid vector at the ingate and the cavity during the fast stage of piston velocity: (**a**) piston velocity was 0.8 m/s, (**b**) piston velocity was 1.6 m/s, (**c**) piston velocity was 2.4 m/s and (**d**) piston velocity was 3.2 m/s.  
The simulation results of the casting solidification are shown in Figure 9. The inagate solidified rapidly due to its lack of thickness and the strong cooling capacity of the die. Because of the uneven thickness of the casting body and dress trap, the solidification process of the whole part was different. Specifically, the thin wall and edge of the casting solidified first, and the casting body and dress trap solidified later. The solidification time of hot spots was much longer than that of the ingate. It could be seen that the solidification time of the ingate was very short. When the ingate was solidified, the solidifying metal in the die cavity was difficult to feed; therefore, it was easy to form shrinkage porosity in the hot spots.
# Data-Completion and Model Correction by Means of Evanescent Regularization
## 2 Correction of the Available Model
### Results: Model Correction  
In this section, for the sake of simplicity, a simple model with \(N=5\) is considered (given in the Appendix A), and a single dense neural network layer connecting the inputs and the outputs. No input parameters \(p\) are used in this case, the time is the only model input. The trainable parameters \(\mathbf{\theta}\) and \(\mathbf{y}_{0}^{s,nm}\) are initiated with the values coming from the available simulation. The initial curves are shown in Figure 1, for the exact model (in solid lines) and the simulated one (in dashed lines). In this section, only the last output is assumed to be unmeasurable.  
The initial condition for the unmeasurable variable is deliberately set to a wrong initial value \(\mathbf{y}_{0}^{h,nm}=0.8\), while the exact initial conditions are considered for the measurable ones, \(\mathbf{y}_{0}^{h,m}=1\). The training is performed on a time domain \(t\in[0,10]s\) with \(n_{t}=1000\) time steps and \(\Delta t=0.01s\). The training with an adjoint-free neural ODE is performed on a portable PC within half an hour. The results after the training are illustrated in Figure 2 for the measurable and non-measurable variables.The used values of the regularization coefficients in this section are \(\lambda_{1}=10^{-5}\), \(\lambda_{2}=10^{-5}\), and \(\lambda_{3}=1\).  
The results illustrated in Figure 2 highlight the ability of the network to correct the existing simulation even though neither the initial value nor the output of the last quantity of interest are available. The mean relative errors are:  
* Relative error on \(\mathbf{y}_{0}^{s,nm}\), the initial condition of the non-measurable quantity: 0.49%
* Relative error on the measurable quantities \(\mathbf{y}^{m}\): 0.01%
* Relative error on the unmeasurable quantities \(\mathbf{y}^{nm}\): 0.43%  
The predictive ability is highlighted, and shows that the proposed approach is able to correct an existing simulation through time variation of only several quantities of interest.  
Figure 1: Exact solution and simulation of the considered model with \(N=5\).
## 3 Finite Element Model Creation
### Wetting-Drying Cycle Simulation Programs  
Since matrix suction is also a type of force, which is manifested as a tensile effect and will make the slope more stable, the effect of matrix suction cannot be neglected in simulations. In the theory of unsaturated soil, matrix suction is negatively correlated with the degree of saturation, and when the degree of saturation is 1, the matrix suction will be reduced to 0. However, in unsaturated seepage, due to the defect of the software itself, the degree of saturation of the region will be forced to be set to 1 when the matrix suction, i.e., negative pore water pressure, is taken into account. Moreover, the degree of saturation is related to the permeability coefficient of the rock, so the rainwater will inflow according to the permeability coefficient of the completely saturated region when the rainwater infiltrates. As saturation is related to the permeability coefficient of the rock, the rainwater infiltration into the actual unsaturated zone will follow the permeability coefficient of the fully saturated zone, which will make the numerical simulation results inaccurate and counterintuitive. Therefore, when using the FLAC3D software to simulate the seepage field in the slope and the slope stability under the influence of the seepage field, it is necessary to carry out the secondary development of the software.  
Based on the above unsaturated soil seepage theory, this numerical simulation firstly imports the Bang Nau dump slope model, groups the slope model, sets the boundary conditions, inputs the initial mechanical parameters and seepage parameters of each stratum, and then runs the program to derive the distribution of the flow field in the slope after a certain period. At this time, the saturation degree of each cell in the field is linked with the unsaturated seepage coefficient, cohesion, and angle of internal friction through a specific relationship equation to become a Fish function. Then, the negative pore water pressure is taken into account, and the stability coefficient of the slope and the distribution of the mechanical field are derived from the discounted strength method. In the next step, the saturation degree of 1 regional unit through the Fish function is extracted and, statistically, this saturation degree of 1 unit is assigned to the same group, and the water level above the unsaturated area of distinction is determined. At this time, the water level above the surface can be divided into two groups, a saturated group and an unsaturated group. In the third step, the grouping of the second step is imported into the model, and the physical and mechanical parameters of this cycle, such as the permeability coefficient, water content, saturation, cohesion, etc., are corrected. The seepage field and mechanical field changes are run and recorded. In the fourth step, we repeat the second and third steps until we reach 5 cycles.  
The flowchart of the wetting-drying cycle simulation scheme is shown in Figure 2.