esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Expression Analysis of ADGRG3 Gene in Various Cancers
#### 3.1.5 Expression of GPR97 in PAAD Based on Clinical Attributes  
In pancreatic adenocarcinoma (PAAD), ADGRG3 (GPR97) expression was evaluated across different clinical and demographic factors. Based on sample types (Figure 5A), GPR97 expression was significantly lower in primary tumor samples (\(n\) = 178) compared to normal samples (\(n\) = 4), indicating a downregulation of ADGRG3 in PAAD tumor tissues. Across cancer stages (Figure 5B), Stage 3 (\(n\) = 4) showed the highest expression, followed by normal tissues. Stage 2 (\(n\) = 146) displayed moderate expression, while Stages 1 (\(n\) = 6) and 4 (\(n\) = 4) exhibited the lowest levels.  
Gender-based analysis (Figure 5C) revealed similarly low GPR97 expression in both male (\(n\) = 97) and female (\(n\) = 80) patients compared to patients with normal samples, indicating no significant gender-based differences. Age-based analysis (Figure 5D) showed the lowest expression in the 21-40-year-old group (\(n\) = 9), with moderate expression in older age groups (41-100 years) but consistently lower expression in patients with normal samples.  
These findings indicate that GPR97 is generally downregulated in PAAD tumor tissues, with some variation across cancer stages and age groups, but minimal difference across genders. This suggests that ADGRG3 may play a different role in PAAD compared with  
Figure 4: Expression of GPR97 in HNSC analyzed based on (**A**) sample types, (**B**) individual cancer stages, (**C**) patients’ gender, and (**D**) patients’ age. GPR97 expression was significantly higher in primary tumor samples than normal in HSNC. HNSC: head and neck squamous cell carcinoma.  
cancers where it is upregulated, warranting further investigation into its potential function in pancreatic cancer.
## Appendix B Proof of Theorem 3  
Since the control set \(U=[0,1]^{4}\), by definition, the closure condition in (I) is established. Also, suppose that \(\tilde{y}\) and \(\tilde{z}\) are any arbitrarily two points in \(U\) such that \(\tilde{z}=(\tilde{z}_{1},\tilde{z}_{2},\tilde{z}_{3},\tilde{z}_{4})\) and \(\tilde{y}=(\tilde{y}_{1},\tilde{y}_{2},\tilde{y}_{3},\tilde{y}_{4})\). By applying the definition of a convex set provided in [46], we conclude that  
\[(1-\tilde{\lambda})\tilde{z}_{j}+\tilde{\lambda}\tilde{y}_{j}\in[0,1],\]  
\(\forall\ \tilde{\lambda}\in[0,1]\), \(j=1,2,3,4\). Hence, \((1-\tilde{\lambda})\tilde{z}+\tilde{\lambda}\tilde{y}\in U\) and that proves the convexity of \(U\). It should be noted that we can write \(g(t,x,\
u)\) in (5) as \(h_{1}(t,x)\
u+h_{2}(t,x)\), such that  
\[h_{2}(t,x)=\left( \begin{array}{c}\Lambda-\lambda_{T}S-\mu S-\sigma_{1}S\\ \\ \sigma_{1}S-\phi\lambda_{T}D-\mu D-\sigma_{2}D\\ \\ \sigma_{2}D-\mu C-\varphi\lambda_{T}C-d_{c}C\\ \\ \lambda_{T}S-\alpha_{1}\lambda_{T}L-\tau_{1}L-\mu L\\ \\ \phi\lambda_{T}D+\tau_{1}L-\alpha_{2}\lambda_{T}L_{d}-r_{2}L_{d}-\tau_{2}L_{d}- \mu L_{d}\\ \\ \varphi\lambda_{T}C+\tau_{2}L_{d}-\mu L_{c}-\alpha_{3}\lambda_{T}L_{c}-r_{3}L_ {c}-d_{1}L_{c}\\ \\ \alpha_{1}\lambda_{T}L+r_{1}L-\mu A-\delta_{1}A-\gamma_{1}\sigma_{1}A\\ \\ \alpha_{2}\lambda_{T}L_{d}+r_{2}L_{d}+\gamma_{1}\sigma_{1}A-\mu A_{d}-\delta_{2 }A_{d}-\gamma_{2}\sigma_{2}A_{d}\\ \\ \alpha_{3}\lambda_{T}L_{c}+\tau_{3}L_{c}+\gamma_{2}\sigma_{2}A_{d}-\mu A_{c}- \delta_{3}A_{c}-d_{2}A_{c}\end{array} \right)\]  
and\[h_{1}(t,x)= \begin{pmatrix}\frac{L}{1+q_{d}L}&\sigma_{1}S+\lambda_{T}S&A&0\\ \frac{L_{d}}{1+q_{d}L_{d}}&-\sigma_{1}S+\phi\lambda_{T}D+\sigma_{2}D&0&A_{d}\\ \frac{L_{c}}{1+q_{d}L_{c}}&\varphi\lambda_{T}C-\sigma_{2}D&0&A_{c}\\ -\frac{L}{1+q_{d}L}&-\lambda_{T}S+\alpha_{1}\lambda_{T}L+\tau_{1}L&0&0\\ -\frac{L_{d}}{1+q_{d}L_{d}}&-\phi\lambda_{T}D+\tau_{1}L+\alpha_{2}\lambda_{T} L_{d}+\tau_{2}L_{d}&0&0\\ -\frac{L_{c}}{1+q_{d}L_{c}}&-\varphi\lambda_{T}C+\alpha_{3}\lambda_{T}L_{c}- \tau_{2}L_{d}&0&0\\ 0&\gamma_{1}\sigma_{1}A-\alpha_{1}\lambda_{T}L&-A&0\\ 0&-\gamma_{1}\sigma_{1}A-\alpha_{2}\lambda_{T}L_{d}+\gamma_{2}\sigma_{2}A_{ d}&0&-A_{d}\\ 0&-\alpha_{3}\lambda_{T}L_{c}-\gamma_{2}\sigma_{2}A_{d}&0&-A_{c}\end{pmatrix} .\]  
We follow the concept used in [40] to prove the boundedness of the state system. Then,  
\[ \begin{split}\|g(t,x,v)\|&\leq\|h_{1}(t,x)\|\,\|v\|+\|h_{2}(t,x )\|\\ &\leq b_{1}\|v\|+b_{2}\end{split}  \tag{10}\]  
for some constants \(b_{1},b_{2}>0\) given by  
\[ \begin{split} b_{1}&=\sqrt{max\{s_{1},s_{2},s_{3}\} \bigg{(}\frac{\omega^{2}s_{1}}{(\mu+b\Lambda)^{2}\mu^{2}}\Lambda^{4}+\frac{ \omega s_{2}}{(\mu+b\Lambda)\mu^{2}}\Lambda^{3}+\bigg{(}1+\frac{s_{3}}{\mu^{2} }\bigg{)}\Lambda^{2}\bigg{)}}\\ b_{2}&=\sqrt{max\{s_{4},s_{5},s_{6}\}\bigg{(}\bigg{(} \frac{\omega^{2}s_{4}}{(\mu+b\Lambda)^{2}\mu^{2}}\Lambda^{4}+\frac{\omega s_{5 }}{(\mu+b\Lambda)\mu^{2}}\Lambda^{3}\bigg{)}+\Lambda^{2}\theta\bigg{)}},\end{split} \]  
where  
\[
## 3 Results and Discussion
### Test Result of Superpave Method for SBS Modified Asphal
#### 3.2.3 Bending Beam Rheometer (BBR) Test  
The bending beam rheometer test (BBR) is used to analyze the stiffness of SBS modified asphalt in SHRP test methods, by which the low temperature performance of SBS modified asphalt could be characterized [21]. The SBS modified asphalt sample is conducted short term aging with RTFOT method first, and then conducted stress aging with pressure aging vessel (PAV).  
The test is loaded 240 s at the stress of 100 g (980 mN), and the creep stiffness S(t) at t = 60 s is used as one of the evaluation index of low temperature performance of SBS modified asphalt. The smaller the S(t), the better low temperature performance of SBS modified asphalt. The m value is the other index of BBR test, which characterize the change of S(t) over time. The S(t) value is required less than 300 MPa, and the m value is required more than 0.3 in SHRP method. The test temperature of \(-\)12 \({}^{\circ}\)C and \(-\)18 \({}^{\circ}\)C was used in this study, and three parallel-samples were prepared for the test. The test results of S(t) of SK90# matrix asphalt and SBS modified asphalt are shown in Figure 6.  
From Figure 6, the S(t) values at \(-\)18 \({}^{\circ}\)C was bigger than that at \(-\)12 \({}^{\circ}\)C, and the S(t) values of both decreased with the increase of SBS modifier dosage at the temperature of \(-\)12 \({}^{\circ}\)C and \(-\)18 \({}^{\circ}\)C, which meant the low temperature performance of SBS modified asphalt had been improved. To analyze the influence of SBS modifier dosage on S(t), the single factor variance analysis for S(t) at different temperature was conducted. The results are shown in Table 8.  
Form Table 8, the \(p\)-value was both 0.001, which meant the SBS modifier dosage had significant influence on S(t) of asphalt binder. Through the compared of two \(F\)-value at \(-\)12 \({}^{\circ}\)C and \(-\)18 \({}^{\circ}\)C, the influencing effect of SBS modifier dosage on stiffness S(t) at \(-\)18 \({}^{\circ}\)C was less than that at \(-\)12 \({}^{\circ}\)C. Therefore, the improvement effect of SBS modifier dosage on the low temperature performance of asphalt binder reduced with the decrease of the test temperature.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **df** & **F** & **Sig.** \\ \hline SBS modifier & \(-\)18 \({}^{\circ}\)C & 4 & 198.68 & 0.001 \\ dosage & \(-\)12 \({}^{\circ}\)C & 4 & 376.57 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Variance analysis for S(t) of SBS modified asphalt.  
Figure 6: Creep stiffness S(t) of SK90# matrix asphalt and SBS modified asphalt.
## 3 Variable Selection and Methodology  
### Variable Selection  
#### 3.1.1 Selection of an Evaluation Index System of UBLGUE  
In accordance with previous empirical experience, the input and output of economic factors can be considered in the index system. An indicator system is conventionally used to measure urban built-up land production inputs and outputs (e.g., land, the labor force, energy, capital, and other elements).  
1. Production inputs indicator. Urban built-up land provides a carrier for various economic activities, and its scale can reflect the potential space of urban development. Here, the total acreage of urban built-up land is used as a proxy variable of land input. Labor and capital are key components of urban built-up land economy systems, and they affect the ability and efficiency of economic development. Thus, the total employees of the secondary and tertiary industries are used as the labor input, and the total fixed-asset investment is used as capital investment. Additionally, the electricity consumption of the whole society is used as the energy input to express energy consumption.
2. Production outputs indicator. The production outputs comprise the desired output elements and undesirable outputs. The added value of secondary and tertiary industries is used as the first desired output element to reflect economic performance. The salary of employees is used as the second desired output element to measure social performance. In addition, this study applies the built-up land greening coverage rate as the third desired output element in built-up land activities. For the undesirable output indicator, existing research has reached a consensus on the measurement indicator. Specifically, the undesirable outputs comprise industrial wastewater, industrial dust, and industrial sulfur dioxide that are applied to denote adverse environmental pollution in the course of built-up land utilization.  
Table 1 presents the index system for evaluating UBLGUE.
# Smooth Optimised A*-Guided DWA for Mobile Robot Path Planning
###### Abstract  
In mobile robot path planning, the traditional A* algorithm suffers from high path redundancy and poor smoothness, while the Dynamic Window Approach (DWA) tends to deviate from the global optimal path and has low efficiency in avoiding dynamic obstacles when integrated with global path planning. To address these issues, a smoothing optimised A*-guided DWA fusion algorithm (SOA-DWA) is proposed in this paper. Firstly, the A* algorithm was improved by introducing a path smoothing strategy and path pruning mechanism, generating a globally optimal path that complied with the vehicle kinematic constraints. Secondly, three sub-functions were introduced into the evaluation function of the DWA algorithm: the distance evaluation between the reference trajectory and the global path, the path direction evaluation, and the dynamic obstacle avoidance evaluation, to enhance the real-time performance of dynamic obstacle avoidance and the consistency of the global path. The SOA-DWA algorithm ensured that the mobile robot could effectively avoid obstacles in complex environments without deviating from the global optimal path. Thirdly, experimental results show that in a static environment, the path length and turning angle of the SOA-DWA algorithm are reduced by an average of 13.3% and 16.25%, respectively, compared with the traditional algorithm. In a dynamic environment, the path length and turning angle are reduced by an average of 10.5% and 14.5% compared to the traditional DWA algorithm, respectively, significantly improving the smoothness of the path and driving safety. Compared to the existing fusion algorithm, the SOA-DWA algorithm reduces the path length by an average of 10.1%, improves planning efficiency by an average of 42%, and effectively enhances obstacle avoidance efficiency. Finally, the effectiveness of the improved algorithm proposed in this paper was further verified by mobile robot experiments.  
mobile robot; improved A* algorithm; local path planning; DWA algorithm; dynamic obstacle avoidance +
Footnote †: journal: Applied sciences
# Spatiotemporal Analysis of Evapotranspiration and Effects of Water and Heat on Water Use Efficiency
## 3 Results and Discussion
### WUEs Responds to Temperature and Precipitation  
From 2000 to 2014, a significant spatial difference in the sensitivity of WUEs to precipitation and temperature in Xinjiang can be observed (Figure 9), while the negative region of precipitation to WUEs was significantly larger than the positive regions. Precipitation is positively correlated with eWUE and tWUE Figure 9d,e in central Xinjiang, while the vast majority of the rest is negatively correlated. Among them, the negative correlation between precipitation and EWUE accounts for 88.8% of the region (Figure 9d); the negative correlation between precipitation and tWUE accounted for 84.8% of the region (Figure 9e); and the negative correlation between precipitation and pWUE prevailed throughout the region (Figure 9f).  
It is shown that the sensitivity of vegetation transpiration to precipitation is relatively negative compared with evapotranspiration, and the difference is estimated in part because ecosystem evapotranspiration includes evaporation from exposed soils. The increase in precipitation in arid areas leads to the decrease in WUE, since the increase in ET is greater than the increase in photosynthesis. On the contrary [57], the decrease in precipitation leads to the increase in WUE in the region, because the decrease in ET is greater than the decrease in GPP [58], and the threshold effect on WUE may be related to the effective precipitation in the region.  
From the spatial perspective, there is a positive correlation between temperature and WUEs, and the proportion of positive correlation is 70.5% (eWUE), 68.3% (tWUE), and 81.3% (pWUE), respectively (Figure 9a,b,c). The positive correlation area is mainly distributed in the north of Xinjiang, because the north of Xinjiang is the main agricultural area in Xinjiang, and the response of irrigation and farmland to temperature is more positive [45]. However, the southern region of Xinjiang is more scattered and has both positive and negative relationships, which may be related to the local geographical location and temperature inequality caused by deserts. The negative values are mainly distributed in the central and southern regions of Xinjiang, as the negative values are located near low latitude and have sufficient thermal energy. This region is highly sensitive to precipitation (Figure 9d-f).  
Figure 9: WUEs sensitivity coefficient to precipitation and temperature (**a**), (**b**), and (**c**) respectively represent the sensitivity coefficient of temperature to eWUE, tWUE, and pWUE; (**d**), (**e**), and (**f**) respectively represent the sensitivity coefficient of precipitation to eWUE, tWUE, and pWUE; the upper left illustration shows the proportion of positive (_Pos_) and negative (_Neg_) sensitivities.
## 4 M2M-Based Division of MAC Layer Protocols
### Adaptive Traffic-Load-Slotted MACA (ATL S-MACA)  
For IoT nodes not capable of carrier sensing, a slotted MACA is extended with the help of traffic-load estimation, collectively termed ATL S-MACA. The basic MACA scheme of RTS-CTS-DATA-ACK is slightly modified, and the RTS is adaptively controlled based on the estimation of traffic load [32]. The protocol finds the optimum traffic-load (Gopt) value, which is the maximum achieved load while using current traffic G for estimating it as Gopt/G for the RTS contention. The ATL S-MACA allows all nodes to send RTS packets at the beginning of each slot; thus, collisions are increased while not adding much to network performance in a constrained environment.
## 3 Results and Discussion  
### Effects of Fluxes Concentration on Weld Bead Geometry  
The weld bead geometries of different concentration fluxes are shown in Figure 4a-d, at a speed of 120 mm/min. It can be observed that all the fluxes increased the depth of penetration (d) and reduced the bead width (w), as compared to the TIG weld without flux. The extent of their effect on weld bead parameters is characterized. The effect of various fluxes on depth of penetration (d), bead width (w), and depth of penetration to bead width ratio (\(d\)/\(w\)) are shown in Figure 5. TIG welding without flux results in the widest (8.5 mm) and shallowest (2 mm) weld bead with the lowest \(d\)/\(w\) ratio of 0.24. Whereas, the use of 1:1 flux results in the greatest depth of penetration (3 mm) with a \(d\)/\(w\) ratio of 0.55. The Fe\({}_{2}\)O\({}_{3}\) flux with the ratio of 1:5 was found to yield the depth of penetration, bead width and \(d\)/\(w\) ratio as 2.8 mm, 5.7 mm and 0.49, respectively. As can be observed from the result, the depth of penetration, bead width and the \(d\)/\(w\) ratio all improve when the concentration of the flux grows increases. However, as flux reaches the 1:1 ratio, the change in the results also gradually reach a limit. Thus, the most concentrated one (1:1) was chosen for further characterization of the full penetration and the maximum \(d\)/\(w\) ratio.  
Figure 3: Measurement configurations for (**a**) arc voltage and (**b**) angular distortion of TIG weldment.
## 3 Proposed Methodology
### Classification
#### 3.4.4 Random Forest (RF) Classifier  
A machine learning (ML) method called a random forest is employed to resolve regression and classification issues. It makes use of ensemble learning, a method for solving complicated issues by combining a number of classifiers. In a random forest algorithm, there are many different decision trees. The random forest algorithm creates a \"forest\" that is trained via bagging or bootstrap aggregation. Based on the decision trees' predictions, the RF algorithm determines the result. It makes predictions by averaging or averaging out the results from different trees. The accuracy of the result grows as the number of trees increases. The decision tree algorithm's shortcomings are eliminated with a random forest. It improves precision and lowers dataset overfitting.
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 3 Results and Discussion
### Phosphorus Elimination
#### 3.5.2 Phosphate Precipitation Using Microalgae Hydrolysate
Furthermore, soluble ferric-phosphate and ferric-hydroxide complexes might form, which hinders the conversion of soluble phosphorus to the particulate form by binding up Fe\({}^{3+}\) or PO\({}_{4}\)\({}^{3-}\) ions in soluble complexes. Phosphorus elimination of the microalgal biomass hydrolysate using FeCl\({}_{3}\) generates significant amounts of precipitated FePO\({}_{4}\). Ferric-phosphate has potential applications in various industries, including its use in anti-corrosion coatings in the steel industry, as a cathode material for Li-ion batteries, and in chemical waste immobilization [57]. Since phosphate is a valuable nutrient, the production of fertilizers using ferric-phosphate is also an extensively researched topic. The potential uses of the FePO\({}_{4}\) precipitated from biomass hydrolysate depends, however, on the size and morphology of the iron phosphate crystals formed [57].
## 3 Performance Analysis
### Average Bit Error Rate
The increased optical power amplifies the effects of amplitude and phase fluctuations caused by turbulence, and in addition, pointing errors cause misalignment between the transmitter and receiver, which leads to a degradation in signal quality and an increase in average BER.
## 6 Results and Discussions
### Results of Decoupling Heat and Power  
As mentioned in the previous part, system peak load regulating capacity and system economy can be improved to some extent by installing an extra portable energy system in the demand side. To improve CHP energy efficiency, reduce battery installation capacity, and improve system reliability, adding an extra thermal storage tank to work with the CHP unit is necessary, because this can help to decouple heat and power. Figure 7 shows the simulation results of the output power of the battery and CHP in the proposed system before and after decoupling. In Figure 8, the optimization results of the total electrical energy that needs to be stored in the battery is shown to reveal the minimum capacity of the battery with which it needs to be equipped.  
Comparing Figure 8 with Figure 7, it is easy to find that before decoupling heat and power, the output of CHP shows an opposite trend to the daily electrical loads. At the off-peak time, power generated by the CHP unit exceeds the electrical demands, and therefore the battery needs to be charged at higher power to collect the redundant power generated by CHP. However, at peak demand time, CHP output power is reduced to a relatively low value because of lower thermal demand, and thus the battery needs to be discharged at higher power to supply the electrical demand. In this situation, the system needs to equip a higher-capacity battery to meet the electrical demands, which is clearly shown in Figure 8.  
After decoupling heat and power, thermal demand can be supplied by the heat storage tank independently when CHP is switched off. Alternatively, when CHP is switched on, CHP always works at its rated maximum output, which can significantly promote power generation efficiency.  
Figure 7: Simulation results of the output power of the battery and CHP.  
Additionally, for a full operation cycle, CHP only needs to be switched off once, which simplifies the operation and gives some maintenance time for the CHP.  
In summary, decoupling heat and power by installing an extra heat storage tank is helpful for improving CHP energy generation efficiency, reducing battery installation capacity, and improving system reliability.