esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Detailed Multi-Time-Scale Coordinated Operation Model for the Combined WPTHB System
### Real-Time Corrective Scheduling
Since the prediction time period is set to 15 min and the updated period is 5 min, so \(P=3\) here; \(X_{t+\tau\Delta t}^{r}\) is the reference trajectory and \(X_{t0+\tau\Delta t}^{r}=\left[P_{g,i}^{hours,0}(t0+\tau\Delta t),P_{h,j}^{hours,0}(t0+\tau\Delta t),P_{b,1}^{day}(t0+\tau\Delta t)W_{b,1}^{day}(t0+\tau \Delta t)\right]\), \(i\in NG,j\in NH,l\in NB\); \(X_{t0+\tau\Delta t}\) is state variable at the time \(t0+\tau\Delta t\); \(X_{t0+\tau\Delta t}=\left[P_{g,i}(t0+\tau\Delta t),P_{h,j}(t0+\tau\Delta t),P_{ b,1}(t0+\tau\Delta t),W_{b,1}(t0+\tau\Delta t)\right]\), \(i\in NG,j\in NH,l\in NB\); \(\Delta U_{t0+\tau\Delta t}\) is the power adjustments of thermal power units, hydro-pumped storage units, and batteries, \(\Delta U_{t0+\tau\Delta t}=\left[\Delta P_{g,i}(t0+\tau\Delta t),\Delta P_{h,j}(t0 +\tau\Delta t),\Delta P_{b,j}(t0+\tau\Delta t),i\in NG,j\in NH,l\in NB\). (2) Constraints  
\[ \begin{cases}\begin{aligned} \Delta\text{P}^{\text{min}}_{g,i}\leq \Delta P_{g,i}(t+\tau\Delta t)\leq\Delta\text{P}^{\text{max}}_{g,i}\\ \text{P}^{\text{min}}_{g,i}\leq P_{g,i}(t+\tau\Delta t)\leq\text{P}^{\text{max}} _{g,i}\\ \Delta\text{P}^{\text{min}}_{h,j}\leq\Delta P_{h,j}(t+\tau\Delta t)\leq\text{P}^ {\text{max}}_{h,j}\\ \Delta\text{P}^{\text{min}}_{h,j}\leq P_{h,j}(t+\tau\Delta t)\leq\text{P}^ {\text{max}}_{h,j}\\ \Delta\text{P}^{\text{min}}_{h,j}\leq\Delta P_{h,j}(t+\tau\Delta t)\leq\text{P}^ {\text{max}}_{h,j}\\ \Delta\text{P}^{\text{min}}_{h,j}\leq P_{h,j}(t+\tau\Delta t)\leq\text{P}^ {\text{max}}_{h,j}\\ W^{\text{max}}_{h,j}\leq W_{h,j}(t+\tau\Delta t)\leq W^{\text{max}}_{h,j}\\ \Delta P_{m,w}(t+\tau\Delta t)+\Delta P_{m,w}(t+\tau\Delta t)+\sum \limits_{i=1}^{NG}\Delta P_{g,i}(t+\tau\Delta t)+\sum\limits_{j=1}^{NH}\Delta P _{h,j}(t+\tau\Delta t)+\sum\limits_{l=1}^{NB}\Delta P_{h,l}(t+\tau\Delta t)= \Delta P_{m,Load}(t+\tau\Delta t)\end{aligned}\end{cases}  \tag{31}\]  
where \(\Delta P^{\text{min}}_{g,i}\), \(\Delta P^{\text{max}}_{g,i}\), \(\Delta P^{\text{min}}_{h,j}\), \(\Delta P^{\text{max}}_{h,j}\), \(\Delta P^{\text{min}}_{b,l}\), \(\Delta P^{\text{max}}_{b,l}\) are, respectively, the upper and lower limits of the allowable power adjustment of thermal power generators, hydro-pumped storage units, and batteries. \(\Delta P_{m,w}(t+\tau\Delta t)\), \(\Delta P_{m,v}(t+\tau\Delta t),\Delta P_{m,Load}(t+\tau\Delta t)\) are, respectively, the forecast data of wind power, photovoltaic power, and load demand at the time \(t0+\tau\Delta t\).
###### Abstract  
This study investigates the effects of alcoholic (AB) and non-alcoholic beer (NAB) consumption on blood pressure and the activity of enzymes regulating the renin-angiotensin system (RAS) in Wistar-Kyoto (WKY) and spontaneously hypertensive rats (SHRs), with WKY rats used as normotensive controls for SHRs. The RAS is crucial for long-term blood pressure regulation, with angiotensin II (AngII) being a potent vasoconstrticor. The aim was to explore the biochemical mechanisms by which beer might influence cardiovascular health. WKY and SHRs were divided into groups receiving tap water (TW), non-alcoholic beer (NAB), alcoholic beer (AB), TW or NAB and TW or AB for 12 weeks. Systolic blood pressure (SBP), body weight, and biochemical parameters (electrolytes, glucose, renal and liver function, lipid profile) were monitored, and the RAS enzyme activity in serum and various tissues was analyzed. Beer consumption, regardless of alcohol content, did not significantly affect SBP or body weight. However, NAB and AB altered the serum electrolyte levels in both strains. AB consumption increased liver enzyme activity. Significant changes were observed in the RAS enzyme activity across tissues, varying by strain, beer type, and tissue. Moderate beer consumption did not elevate blood pressure in WKY or SHRs. Nevertheless, beer modulated RAS-regulating enzyme activities, indicating potential impacts on cardiovascular homeostasis.  
Keywords:hypertension; renin-angiotensin system; aminopeptides; alcoholic beer; non-alcoholic beer; serum biochemistry; hypothalamus-pituitary axis; adrenal gland; testsis; rats _Article_
## 5 Case Studies
### Analysis of the Regulation Efficiency of Distributed Energy Storage
#### 5.6.3 Cost and Environmental Value  
In this study, the system operating costs before and after DES participation were calculated, considering the cost of energy storage lifecycle degradation and PV curtailment. A system carbon emission model was established to calculate carbon emissions in the two scenarios. By comparing the operating costs and carbon emissions under these scenarios, the cost and environmental value of DES regulation were quantified. It was assumed that the carbon emission intensities of GT and power purchases in the DN were 0.45 tons/MWh and 0.55 tons/MWh, respectively, while the system's carbon emission allowance was set at 0.5 tons/MWh. The cost and environmental values before and after DES integration are shown in Table 6. As observed in the table, after the integration of DES, the carbon emission allowance decreased from 2407.9 tons to 2399.4 tons, a reduction of 8.5 tons, while the actual carbon emissions decreased from 1633.2 tons to 1607.4 tons, a reduction of 25.8 tons. This indicates that the DES plays a significant role in reducing carbon emissions. At the same time, carbon emission costs decreased from \(-\)38,736 CNY to \(-\)39,605 CNY, a reduction of 869 CNY. This adjustment not only reflects cost benefits but also significantly enhances the environmental performance of the system, further supporting the strategic goal of low-carbon operation.
###### Abstract  
Background: Relationships between reflectivity, hardness and chemical composition of the dispersed phase, included in orthodontic composites Transbond XTM (Trans), Light-Cure Orthodontic Paste (Leone) and Bisco Ortho Bracket Paste LC (Bisco), were investigated in vitro to evaluate whether reflectivity results can be useful in internal material composition interpretation, thus obtaining information on mechanical behaviours. Methods: Light transmission through 36 resin discs was measured with a UV/Vis spectrophotometer, evaluating the spectral range from 190-1100 nm. To have a benchmark of material hardness and internal composition, Vickers measurements and Cross-Sectional Focus Ion Beam Scanning Electron Microscopy (FIB/SEM) analysis were provided. Results: Bisco has the highest reflectivity, Leone shows an absorption pattern in the UV region similar to Bisco and Transbond has the lowest reflectivity compared to the others. This trend is confirmed by FIB/SEM imaging, showing a more similar induced roughness and internal composition for Bisco and Leone, with respect to Transbond. Higher filler presence in the composition of Bisco and Leone justifies a higher hardness of these two materials, with respect to Transbond, as confirmed by Vickers measurements. Conclusions: Bisco and Leone show similar optical responses and similarities in mechanical performance. This statement is explained by the lower and similar filler content as confirmed also by FIB/SEM analysis. The inner composition of Bisco and Leone provides a higher value of microhardness, as demonstrated by Vickers measurements. Therefore, this study confirms that the UV-Vis analysis can also offer a significant overview on the internal material composition, thus indirectly providing information on the mechanical properties of orthodontic composites.  
Orthodontic composite; reflectivity; chemical composition; mechanical behaviour; UV-Visible Spectrophotometry; Cross-Sectional Focus Ion Beam/Scanning Electron Microscopy; Vickers indentation +
Footnote †: journal: Applied Sciences
# Reconfiguration for the Maximum Dynamic Wrench Capability of a Parallel Robot
## 4 Examples
### Results and Discussion
The obtained proportional coefficients are [\(\eta_{1}\),...\(\eta_{16}\)] = [0.2038, 1.0000, 0.0000, 1.0000, 0.4576, 1.0000], and the corresponding maximum fitness value = 583.9285, the dynamic wrench force is 514.9908 N and the dynamic wrench moment is 34.4688 Nm. Also, the corresponding dynamic wrenches along the prescribed trajectory are shown in Figure 9. It can be found that the improved allowable dynamic wrench capability stems mainly from the increase of the minimum dynamic wrench force. Table 2 gives the comparisons for the initial base point locations, the equidistant displaced base point locations and the respectively displaced base point locations. It is seen that the base locations have a critical effect on the allowable dynamic wrench capability of the reconfigurable parallel robot. Especially, in this case study, the allowable dynamic wrench capability for the optimal configuration with the respectively displaced base point locations can be increased 119.2%, better than that with the equidistantly displaced base locations which increases by 53.34% in the allowable dynamic wrench capability. Moreover, if we look into the torques for these three configurations as shown in Figure 10, some actuators saturate in order to maximize the dynamic wrench capability. Especially, at time \(t=2.3\) s, it can be seen that the dominant actuator 3 can exert larger torque through the optimal reconfiguration so that the minimal dynamic wrench force improves more. Finally, the numerical efficiency comparison is shown in Table 3. It is seen that a large amount of time is taken for computation for the conventional discretizing and searching method as compared to the proposed hierarchical optimization algorithm. Figure 8: Base point positions _versus_ iteration number for base point locations being displaced individually. Figure 9: Variations of dynamic wrenches along prescribed trajectory for [n\({}_{1}\),...,n\({}_{6}\)] = [0.2038, 1.0000, 0.0000, 1.0000, 0.4576, 1.0000]. Figure 10: Torques with the maximum dynamic wrench capability for (**a**) initial; (**b**) equidistantly displaced; (**c**) individual displaced base point locations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Results** & \(\mathbf{[n_{1},\dots,n_{6}]}\) & **Allowable DWC** & \(\mathbf{[f_{w},n_{w}]}\) \\ \hline Initial base point locations & [0.1,..., 0.1] & 266.394 & [189.696, 38.349] \\ \hline Equidistant displaced base point locations & [0.3684,..., 0.3684] & 408.490 & [224.877, 91.806] \\ \hline Respectively displaced base point locations & [0.2038, 1.0, 0.0, 1.0, 0.4576, 1.0] & 583.929 & [514.991, 34.469] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for three different types of base locations.
# An Evolutionary Computation Approach for Twitter Bot Detection
## 6 Discussion  
Detecting bot accounts on Twitter is extremely difficult because of the high variety and heterogeneity that bot profiles exhibit. However, it is possible to build ML models that can leverage different sources of information to detect a good percentage of bot accounts. Currently, apart from [6], there are no consistent baselines and benchmark datasets that can be jointly leveraged in order to learn more sophisticated and effective models. For this reason, we believe that more research effort is required. The key challenge is providing datasets with high heterogeneity and different types of information. For example, every attribute that can be associated with a single tweet, such as the favorite count and timestamp, can be used to build more effective ML models that take advantage of different kinds of properties. Finally, in light of what we discovered through the experiments described in this work, we can try to give an answer to the research questions presented in Section 1.  
* **RQ1**: _is it possible to obtain interpretable models with good generalization capabilities?_ It is possible to obtain models with good detection accuracy and a low number of components. A simple model has clearly more chance of being interpretable, but it is not trivial to ensure that interpretable models are able to exhibit good qualitative performance at the same time. In this work, we demonstrated that with evolutionary algorithms it is possible to discover interpretable models with good detection accuracy. However, we believe that using additional types of features that better encode anomalous behavior of Twitter profiles would enable us to learn more effective models that can be interpretable at the same time.
* **RQ2**: _can we trust the predictions of ML models based on Twitter bot detection?_ It is not trivial to define a measure of reliability for an ML model. In this work, we defined a confidence score and we checked whether this measure is high when the model performs a correct prediction, based on the data available. The results are encouraging and, therefore, the possibility of using other consistent datasets as benchmarks would increase the probability of defining a trustworthy confidence score that allows us to know whether we can trust a given prediction.  
Figure 8: Pareto frontiers that can be extracted from the models described in Table 8. Each point in this scatter plot is a solution presented in the aforementioned table. Non-dominated solutions are thus grouped together using the same color.
# Triple-Attention Mixed-Link Network for Single-Image Super-Resolution
## 4 Experiment
### Study in Model Structure  
In this section, we study the effect of network structure and the model performance. For fair comparison, no attention modules or self-ensemble are included in these networks. We replaced the mixed-link connections in the units with dense and residual connections to create a pure dense network and a residual network. We trained the models on DIV2k dataset and we compare the performance and model size of these network structures. The results for scale \(\times\) 2 on Set5, Set14, and BSD100 are shown in Table 2. The experiment result shows that mixed-link structure could gain at least 0.09 dB performance with a decrease of 0.17 M model parameters compared with residual structure and gain at least 0.07 dB performance with a decrease of 0.69 M model parameters compared with dense structure. Figure 9 also shows that our proposed mixed-link topology could achieve better performance than residual and dense structure with the least model parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Structure** & **MixNet** & **ResNet** & **DenseNet** \\ \hline Parameter & 1.86 M & 2.03 M & 2.55 M \\  & & (+0.17) & (+0.69) \\ \hline Set5 & 37.81 & 37.69 & 37.74 \\  & & (\(-0.12\)) & (\(-0.07\)) \\ \hline Set14 & 33.40 & 33.28 & 33.32 \\  & & (\(-0.12\)) & (\(-0.08\)) \\ \hline BSD100 & 32.05 & 31.96 & 31.98 \\  & & (\(-0.09\)) & (\(-0.07\)) \\ \hline \end{tabular}
\end{table}
Table 2: Model parameter and performance on Set5, Set14 and BSD100 with different network structures.  
Figure 8: Visual comparison of TAN+, EDSR+, and RDN+.
# Experimental Study on the Effect of Wind Speed on Hail Impacts
## 3 Experimental Analysis and Results
### Investigating the Influence of Wind Speed on Hail Impact Force Dynamics
As shown in Table 4, the peak impacts of 2.4 cm, 2.8 cm, and 3.2 cm hailstones were 110.9 N, 139.7 N, and 159.2 N at a wind speed of 0 m/s, respectively. Under a wind speed of 13.5 m/s, these values increased to 126.0 N, 173.6 N, and 190.3 N, corresponding to a minimum increase of over 10% in structural panel impact forces. These results demonstrate a clear positive correlation between peak impact forces and both wind speed and hailstone diameter. Figure 6 shows the comparative curves of the impact forces for hailstones with varying diameters at a constant velocity of 23 m/s under different wind speeds. As depicted in Figure 6, the peak impact forces exhibit a positive correlation with wind speed, increasing as wind speed rises. Notably, larger hailstones demonstrate more pronounced increases in peak impact forces. Furthermore, significant gaps between the peak force curves of different hailstone diameters indicate that particle size markedly influences impact forces, with larger hailstones generating greater impact forces. Additionally, the effect of wind speed on impact forces varies with hailstone diameter: larger hailstones are more significantly influenced by wind speed, as evidenced by steeper slopes in their peak force curves and more substantial increases in peak forces.
## 2 Materials and Methods
### SHPB Tests  
The SHPB tests were conducted on the disc specimens after freeze-thaw cycling treatments with different freezing temperatures. Figure 2b shows the sketch map of the SHPB system, which includes a gas gun, a striker bar, an incident bar, a transmitted bar, an absorbed bar, a damper, strain gauges, a strainmeter, and a data-processing unit. Both of the diameters of the incident bar and transmitted bar were 100 mm. The lengths of the incident bar and transmitted bar were 4000 mm and 3000 mm, respectively. The elastic modulus of the bar is 190 GPa. During the SHPB tests, the strain gauge on the incident bar was used to record incident waves and reflected waves. The transmitted wave was recorded by the strain gauge on the transmitted bar.  
After the SHPB tests, the strain rate \(\dot{\varepsilon}(t)\), strain \(\varepsilon(t)\), and stress \(\sigma(t)\) of specimens can be derived, according to the methods suggested by ISRM [40; 44].  
\[\left\{\begin{array}{c}\dot{\varepsilon}(t)=-\frac{2C_{0}}{L}\varepsilon_{r}(t )\\ \varepsilon(t)=-\frac{2C_{0}}{L}\int_{0}^{t}\varepsilon_{r}(t)dt\\ \sigma(t)=\frac{L}{A_{S}}\varepsilon_{t}(t)\end{array}\right. \tag{1}\]  
where \(L\) and \(A_{S}\) are the length and cross-sectional area of the specimen, respectively. \(A\), \(C_{0}\), and \(E\) are the cross-sectional area, elastic wave velocity, and Young's modulus of the bar, respectively. \(\varepsilon_{r}(t)\) and \(\varepsilon_{t}(t)\) are the strain of reflected wave and transmitted wave, respectively.  
The velocity of the striker bar can be changed by charging different levels of pressures in the gas gun, which further vary the strain rate on the specimen [45]. The impact pressure in each group of experiments was set to 0.055 MPa, and then the variation of dynamic mechanical behavior of sandstone specimens with freezing temperature was studied. In addition, different impact pressures were applied on the specimens subjected to 10-times freeze-thaw cycling treatments with a freezing temperature of \(-40\)\({}^{\circ}\)C. The variation of mechanical behavior of sandstone specimens after freeze-thaw cycling treatments with strain rate was also studied. The impact pressures applied on the specimens were 0.050, 0.055, and 0.060 MPa, respectively.  
Figure 2: Experimental devices. (**a**) Uniaxial compressive testing machine. (**b**) Schematic diagram of SHPB apparatus.
## 4 Discussion
times of each ionization path are 17.55, 16.25, 7.28, and 8.59 fs, respectively. Considering that the ionization from (0, 1) to (1, 1) also takes some time, it is reasonable to expect that the entire ionization process is completed within a laser pulse of 70 fs (FMHM). At this point, we can reproduce a complete picture of all these configurations in Figure 6, where the allowed processes are shown by a solid arrow and the internuclear distance is marked when ionization takes place. With the stretching process of the molecular ions IBr\({}^{2+}\) under the effect of Coulomb repulsion, enhanced ionization processes occur to form a highly charged parent ion. This observation presents more proof that ionization is predominantly controlled by states with intense coupling to the laser field. These states are strongly pushed by the laser field, leading to double ionization. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Process** & **Channel** & **R\({}_{\text{JJ}}\) (Å)** & **T\({}_{\text{jj-nm}}\) (fs)** \\ \hline \multirow{4}{*}{(f)} & (1, 1)\(\rightarrow\)(1, 2) & 3.80 & 17.55 \\  & (1, 2)\(\rightarrow\)(2, 2) & 4.84 & 16.25 \\  & (2, 2)\(\rightarrow\)(2, 3) & 4.86 & 7.28 \\  & (2, 3)\(\rightarrow\)(4, 3) & 5.42 & 8.59 \\ \hline \multirow{4}{*}{(g)} & (1, 1)\(\rightarrow\)(1, 2) & 3.80 & 17.55 \\  & (1, 2)\(\rightarrow\)(2, 2) & 4.84 & 16.25 \\  & (2, 2)\(\rightarrow\)(4, 2) & 5.04 & 9.21 \\  & (4, 2)\(\rightarrow\)(4, 3) & 5.52 & 8.47 \\ \hline \multirow{4}{*}{(h)} & (1, 1)\(\rightarrow\)(3, 1) & 4.19 & 20.34 \\  & (3, 1)\(\rightarrow\)(3, 2) & 5.22 & 14.62 \\  & (3, 2)\(\rightarrow\)(4, 3) & 5.25 & 2.10 \\ \hline \multirow{4}{*}{(i)} & (1, 1)\(\rightarrow\)(3, 1) & 4.19 & 20.34 \\  & (3, 1)\(\rightarrow\)(3, 3) & 5.18 & 14.32 \\  & (3, 3)\(\rightarrow\)(4, 3) & 5.35 & 4.49 \\ \hline \multirow{4}{*}{(j)} & (1, 1)\(\rightarrow\)(3, 1) & 4.19 & 20.34 \\  & (3, 1)\(\rightarrow\)(4, 2) & 5.03 & 13.16 \\ \cline{1-1}  & (4, 2)\(\rightarrow\)(4, 3) & 5.52 & 8.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The calculated R\({}_{\text{JJ}}\) and the travel time, T\({}_{\text{JJ-n,m}}\), for each channel of one- and two-electron sequential ionization. Figure 6: Molecular IBr’s one- and two-electron ionization pathways. The values situated above the arrow signify R (Å) during ionization.
## 6 Engineering Application and Case Analysis
### Case Analysis and Verification
Under the conditions of rapid push mining, with the rapid movement of low level strata, the leading dynamic bearing pressure of the stope rapidly moves forward, while the high level strata have no time to slowly move or move forward, which causes the static bearing pressure to slowly move or move forward, resulting in the superposition of the influence of dynamic bearing pressure and the influence of static bearing pressure at a certain point in advance of the stope, which may lead to rockburst [46; 47]: The second measure is implement pressure relief by large diameter drilling. Large-diameter boreholes are used for pressure relief treatment in local areas with rockburst risk. Through the implementation of large-diameter drilling, the surrounding rock at a certain depth on the roadway will be structurally damaged, forming a weakening zone, causing the high stress in the surrounding rock around the roadway to be transferred deeper, so that the surrounding rock around the roadway will be in the low-stress zone. When rockbursts occur, on the one hand, the space of the large-diameter drilling can absorb the washed coal  
Figure 19: Energy and frequency distribution of microseismic events in July. (**a**) Frequency distribution cloud map of microseismic events. (**b**) Energy distribution cloud map of microseismic events (lg(E)). and prevent the coal from rushing out, while on the other hand, the closure of the roof and floor in the pressure relief zone will produce a \"wedge\" resistance zone. This can prevent the coal body from rushing out to a certain extent [48; 49; 50]. (1) According to the previous analysis, when the daily pushing and mining speed of the 1300 working face is 4.0 m/d, rockburst will not occur. When the pushing and mining speed is 5.6 m/d, the leading dynamic abutment pressure and static abutment pressure of the stop synchronously move forward. When the pushing and mining speed is 6.4 m/d, after a period of pushing and mining, the influence of the leading dynamic abutment pressure and the static abutment pressure of the stope is superimposed, thus inducing the rockburst. It can be seen that the upper limit of mining speed can be determined by studying the evolution law of leading dynamic pressure and static abutment pressure in the stope based on the prevention and control of rockbursts. (2) After relief treatment, such as pressure relief using large-diameter boreholes, the 1300 working face resumed production on 21 July and smoothly passed through the large fault area until the end of the mining work of the working face. The application of this technology ensures the safe production of the working face and its adjacent areas.