esa-sceva/satcom-chunk-collection huggingface dataset:
# Dry Test Methods for Micropumps
## Materials and Methods  
### Design Description  
#### 2.1.1 MEMS Membrane Micropump  
The present article investigates means to test MEMS membrane micropumps in production, considering the important constraints in terms of sterility, contamination, cost, and reliability. A membrane micropump consists of a variable volume pumping chamber, a movable membrane, an actuator, and, at the inlet and the outlet of the pumping chamber, flow-rectifying elements that can be holes, nozzles, or passive check valves [19; 30]. The membrane micropump is built around the reciprocating concept: a cyclic movement of the membrane is obtained with an actuator, and the resulting volume changes in the pumping chamber lead to two alternate pumping phases:  
1. Supply phase: the upward movement of the membrane leads to an increase in the pumping chamber volume and a lowering of the pressure; thus, liquid enters from the inlet toward the pumping chamber.
2. Infusion phase: the downward movement of the membrane lowers the pumping chamber volume and increases the pressure; thus, liquid flows from the pumping chamber toward the outlet.  
The two phases of the actuation cycle of a membrane micropump with check valves are illustrated in Figure 3. The stroke volume \(V_{S}\) is the volume displaced during an actuation cycle. The minimum pumping chamber volume (at the end of the infusion phase) is the dead volume \(V_{D}\). Therefore, the volume of the pumping chamber varies alternatively between \(V_{D}\) (at the end of the infusion phase) and \(V_{0}+V_{S}\) (at the end of the supply phase).  
The compression ratio \(\varepsilon\) is defined by:  
\[\varepsilon=\frac{V_{S}}{V_{D}} \tag{1}\]
## 3 Results and Discussion
### Numerical Simulations
Figure 11 also shows that, at short circuit conditions, the internal electric field inside the device dramatically changes when including mobile ions in the simulation. For both device thicknesses, 350 nm and 650 nm, the electric field decreases becoming almost negligible in the central part of the active layer, and this is more pronounced for the thickest device. In the absence of or at low ion concentrations, the electric field is lower in the thicker layers, but it is still high enough to obtain an efficient current extraction, in accordance with the measured high PCE for pristine devices. On the other hand, the carrier concentration in pristine devices presents a similar profile (Figure 11), with carriers being accumulated at the HTL interface and extracted at the ETL contact, as expected. However, when ions are included in the simulation, carriers remain accumulated in the middle of the active layer, and extraction is less efficient. This effect is more pronounced in thicker devices, where carriers accumulate almost along all of the active layer due to the lower electric field.
## 5 Example Analysis
### Analysis of the Simulation Results  
#### 5.2.1 Active Power Loss Analysis of the Distribution Network Comprising the Micro Energy Grid  
The network's active power losses before and after the grid connection of the micro energy grid, on a typical day in the winter (a), and in the summer (b), are shown in Figure 9. The system network's active power loss was larger when no micro energy grid was connected. However, when the micro energy grid was connected, and the proposed model was used, the average reduction in the network's active power loss was 13.2% in the winter, and 32.4% in the summer. The network's active power loss after the grid connection of the micro energy grid was significantly reduced when compared with that before the grid connection of the micro energy grid. The proposed model established the micro-energy-grid level needed to provide power support to the distribution-network level.  
Figure 8: Cooling, heating, and electrical load curves, and wind power and photovoltaic (PV) prediction curves for the micro-energy grid on a typical day in the winter (**a**), and in the summer (**b**).
## 2 Effects of Plant Extracts on In Vitro Dendritic Cell Biology and Its Effects in Cancer Cells
### Plant Extracts Pulsed DCs-Based Anti-Tumor Immunity through Various Signaling Pathways  
The maturation of DCs is critical for initiating immune responses. In Table 4, plant extract polysaccharide (PLP) from _Pueraria lobata_-activated DCs enhances T-cell stimulation through the TLR4 signaling pathway. Here, extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), p38 mitogen-activated protein kinase (MAPK) phosphorylation, and nuclear factor kappa-light-chain-enhancer of activated B cells p65 (NF-\(\kappa\)Br65) subunit are the downstream molecules of Toll-like receptor 4 (TLR4) [27]. It has been reported that neon leaf glycoprotein (NLGP) is popular for its anti-tumor therapy. NLGP-pulsed DCs activate forkhead box P3+ (FoxP3+) Treg downregulation, T-bet upregulation, signal transducer and activator of transcription (STAT) 1 and STAT4 high phosphorylation, and STAT3 low phosphorylation, which ultimately induces Th1 immune response [4]. [PERSON] et al. reported that polysaccharide from _Plantago Asiatica_ induces DC maturation through the TLR4 pathway [54]. Water extract of _Glucyrrhiza uralensis_ dose-dependently promotes DC maturation and cytokine production through the Toll-like receptor (TLR) 4 signaling pathway [47]. The polysaccharide-derived from _Mori fruct_ and _Platycondan grandiform_-pulsed DCs mediated T-cell proliferation through mitogen-activated protein kinase MAPK (increased phosphorylation of JNK, ERK, c-Jun, p38 and MAPKs) and NF-\(\kappa\)B (increase nuclear translocation of NF-\(\kappa\)B p65 and \(\kappa\)kB\(\alpha/\beta\) degradation) signaling pathway where TLR4 is a membrane target molecule [53; 56]. Angelan from _Angelica gigas Nakai_ plant polysaccharide stimulated dendritic cells mediated tumor immunotherapy, where TLR4 is a membrane target and downstream signaling molecules are NF-\(\kappa\)B/Rel (increased translocation of NF-\(\kappa\)B p50/p65, RelB and c-Rel) and MAPK (increased phosphorylation of p38, JNK, and ERK) [43; 44]. Another study found that _Lycium barburum_ polysaccharides induce TLR2 and TLR4-mediated functional activation of DCs via activation of NF-\(\kappa\)B [57]. [PERSON] et al. reported that maturation of dendritic cells is induced by _Radix Glycyrrhizae_ polysaccharide through NF-\(\kappa\)B and MAPK (increased MAPK, p38, and JNK phosphorylation but no change in ERK) signaling pathways [58]. Fractioned Dioscorea phytoextrct (DsII) enhances
## 3 Analysis of Test Results  
### Natural Vibration Characteristics  
The dynamic characteristics of the tank model will change somewhat after being excited by seismic waves subjected to different peak accelerations. By processing the data in the condition of white noise, the natural vibration frequency of the tank model can be obtained, as shown in Table 3.  
It can be seen from Table 3 that:  
1. After seismic isolation measures are taken, the natural vibration frequency of the tank model is significantly reduced. Before the seismic wave is applied, along the X-direction, the frequencies of the seismic storage tank and the seismic isolation tank are 16.8 Hz and 7.0 Hz. After the seismic isolation, the frequency of the storage tank decreases by 9.5 Hz, with a decrease of 56.5%. Along the Y-direction, the frequencies of the seismic storage tank and the isolation storage tank are 16.1 Hz and 7.0 Hz, and the frequency of the storage tank is reduced by 9.1 Hz after isolation, with a decrease of 56.5%. This shows that the isolation bearing has the same effect on the natural vibration frequency of the tank in the X- and Y-directions.
2. With the increase of peak acceleration of the seismic wave, the natural vibration frequency of the seismic storage tank and the seismic isolation storage tank decreases gradually. This indicates that the tank was damaged, and that the damage was progressive. After the test, along the X-direction, the natural vibration frequencies of the seismic storage tank and the isolation storage tank decreased by 1.8 Hz and 0.9 Hz, respectively; along the Y-direction, the natural vibration frequencies of the seismic storage tank and the isolation storage tank decreased by 2.5 Hz and 1 Hz, respectively. This indicates that the damage degree of the isolation storage tank is smaller than that of the seismic storage tank.
## 3 Methodology
### Hypergraph Contextual Attention Module (HCA)
#### 3.3.1 Attention Mechanism  
The attention mechanism in HCA is dual-level, comprising both sentence-level and hyperedge-level attention, designed to optimize the selection of content for the summary. This dual approach ensures a comprehensive filtering of both individual sentence importance and the collective importance of sentences within hyperedges.  
Sentence-level attention aims to assess and weight the significance of each sentence independently. It is mathematically expressed as [33]  
\[\alpha_{i}=\frac{\exp(\text{ReLU}(w^{T}v_{i}+b))}{\sum_{j=1}^{m}\exp(\text{ ReLU}(w^{T}v_{j}+b))}, \tag{9}\]  
where \(v_{i}\) is the feature vector of sentence \(i\), \(w\) is a learnable weight vector, \(b\) is a bias term, and ReLU is the rectified linear activation function. The attention scores (\(\alpha_{i}\)) highlight the relative importance of each sentence, guiding extractive summary composition.  
Hyperedge-level attention assesses the importance of each hyperedge's contribution to the overall document context. This attention is calculated as follows:  
\[\beta_{k}=\frac{\exp(\text{LeakyReLU}(u^{T}f_{k}+c))}{\sum_{j=1}^{m}\exp( \text{LeakyReLU}(u^{T}f_{i}+c))}, \tag{10}\]where \(f_{k}\) is the integrated feature vector of hyperedge \(k\), \(u\) is a learnable weight vector, \(c\) is a bias term, and LeakyReLU is used as nonlinear activation to allow a small gradient when the unit is inactive. These scores (\(\beta_{k}\)) dictate the focus on hyperedges, integrating them into the final summary effectively.
## 4 Discussion
### Coverage of Data Quality Aspects
#### 4.2.3 Accuracy-Related Implementations  
Since most packages comprise univariate statistics and almost half also include multivariate statistics, users may trivially use their output to detect some accuracy issues. For example, a histogram may be used to identify unexpected locations, shapes or scales, and aspects of other dimensions, such as range violations. Yet, these descriptors rely on the user inspecting the results, and the quality of the output strongly influences options to detect issues reliably. Despite this, indicators within the accuracy dimension were the least covered by the packages' functionalities. This is likely the case because most related checks are not of a simple Boolean type, but require a range of statistical analyses and metadata on expected distributions and associations to make decisions on the presence of some issue.
## 3 Results
### Orbit Quality
#### 3.2.1 Orbit-Only SISRE  
The orbit and clock SISRE are obtained as follows:  
\[SISRE=\sqrt{\left(w_{1}^{2}R^{2}-2w_{1}RT+T^{2}\right)+w_{2}^{2}(A^{2}+C^{2})} \tag{3}\]  
where \(R\), \(A\) and \(C\) represent the satellite orbit difference in radial, along-track and cross-track directions, respectively. \(T\) denotes the satellite clock difference, and orbit-only SISRE can be obtained by setting \(T\) to zero. Meanwhile, [PERSON] et al. [42] published the recommended value of \(w_{1}\) and \(w_{2}\) in Table 4 of the paper, and the value is adopted in this contribution for our orbit-only SISRE investigation. Table 4 and Figure 7 illuminate the orbit-only SISRE for BDS-3 MEOs and IGSOs computed between B1 CB2a combination, B1 CB2b combination, B1 IB3I combination, CODE and GFZ final orbits.  
In Figure 7, CODE/GFZ represents the orbit-only SISRE between CODE orbit and GFZ orbit. Meanwhile, CODE/B1 CB2a, CODE/B1C2b and CODE/B1 IB3I stand for the orbit-only SISRE between CODE orbit and dual-frequency measurement combinations in this contribution. Similarly, GFZ/B1 CB2a, GFZ/B1C2b and GFZ/B1 IB3I stand for the orbit  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Combination1/AC1**} & \multirow{2}{*}{**Combination2/AC2**} & \multicolumn{2}{c}{**Orbit-Only SISRE (cm)**} \\ \cline{3-4}  & & **MEOs** & **IGSOs** \\ \hline CODE & GFZ & 5.10 & 9.13 \\ CODE & BICB2a & 5.32 & 9.53 \\ CODE & BICB2b & 5.70 & 9.80 \\ CODE & B1 IB3I & 6.68 & 11.87 \\ GFZ & B1 CB2a & 5.18 & 9.30 \\ GFZ & B1 CB2b & 5.50 & 9.63 \\ GFZ & B1 IB3I & 6.40 & 11.40 \\ B1 CB2a & B1C2b & 2.88 & 4.40 \\ B1 CB2a & B1 IB3I & 4.40 & 6.83 \\ B1 CB2b & B1 IB3I & 4.10 & 6.87 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Orbit-only SISRE for MEOs and IGSOs of BDS-3 between different solutions.  
Figure 7: Histogram of the orbit-only SISRE for MEOs and IGSOs of BDS-3 between different solutions.  
only SISRE between GFZ orbit and dual-frequency measurement combinations. Finally, B1 CB2a/B1 CB2b, B1 CB2a/B1 IB3I and B1 CB2b/B1 IB3I symbolize internal orbit-only SISRE comparison results for the three dual-frequency measurement combinations.  
For external consistency comparison with CODE and GFZ final orbits, B1 CB2a/CODE, B1 CB2b/CODE, B1 CB2a/GFZ and B1 CB2b/GFZ are at the same level as CODE/GFZ, the orbit-only SISRE approximately 5 cm for MEOs and 9 cm for IGSOs, respectively. Meanwhile, B1 IB3I/CODE and B1 IB3I/GFZ are about 1-2 cm worse for both MEOs and IGSOs, at approximately 6 cm for MEOs and 11 cm for IGSOs, respectively. For internal coincidence comparison, the consistency of B1 CB2a/B1 CB2b is also superior to that of the B1 CB2a/B1 IB3I and B1 CB2b/B1 IB3I combinations. Moreover, the orbit-only SISRE for IGSOs is significantly larger than that of the MEOs, mainly due to the ground tracking station coverage for these IGSOs and SRP modelling difficulties caused by large communication antennas.
## 1 Introduction
The WPD calculation has two input quantities: (i) the Hamiltonian and (ii) the \(\psi_{0}\left(\overset{\rightarrow}{r}\right)=\psi\left(\overset{ \rightarrow}{r},t=0\right)\) initial state. In our former calculations \(\psi_{0}\left(\overset{\rightarrow}{r}\right)\) was a simple 3D Gaussian wave packet (WP), but this made it necessary to start the initial WP from a region of space, where \(V\left(\overset{\rightarrow}{r}\right)=0\), or at least constant, and the WP had to be injected from this region into the nanosystem through a simulated interface layer. This, however, made it difficult to create a WP with pre-determined initial spectrum on the nanosystem, because the interface layer can considerably distort the spectrum, which can also affect the scattering processes and thus the final state strongly depends on the way the initial state was prepared. For example, when the initial WP is injected on the graphene sheet from a simulated STM tip, a complicated multiple scattering process occurs between the tip and the graphene surface, which selects only certain components. The further spreading on the graphene surface will be determined by the initial state formed by these specific components [18]. Such processes have a profound influence on the STM imaging, resulting in anisotropic currents even on the Fermi energy. In this paper, to avoid this problem, a tailor-made initial WP on the nanosystem is built by constructing the WP as a superposition of quasiparticle Bloch states, which is presented in Section 2. These precisely determined WPs are used to study the scattering on defect states. In Section 3, we introduce and apply an alternative WPD technique, which has the advantage that it completely circumvents the need to calculate a pseudopotential. We work out the time evolution of the WP directly from the \(E\left(\overset{\rightarrow}{k}_{\text{\emph{ Bloch}}}\right)\) dispersion relation. Formerly, we applied this method for studying the WPD on pristine graphene and MoS\({}_{2}\) single layers [20] and now we extend it for graphene structural defects opening the way for the WPD investigations of defect scattering processes in other 2D materials.
# Research Progress of Enhanced Thermal Evacuation and Cooling Technology for High-Speed Motors
## 2 Different Application and Cooling Requirements for High-Speed Motors
### Motor Housing
#### 3.1.1 Flow Channel Configuration  
The cooling effect is enhanced by the increase in convex structure and is not elevated after a certain value. The flow heat transfer efficiency is dramatically affected by the shape of the coolant flow path. [PERSON] et al. [27] investigated the impact of convex structure shapes and numbers in water-cooled flow channels on cooling efficiency. The convex structure was found to considerably enlarge the strength of the fluid vortices, thereby enhancing cooling efficiency, as shown in Figure 6. Among the different convex structures, the triangular convex structure has the best effect in respect of low flow loss. With the number of convex structures reaching as high as twelve, the convective heat transfer of the cooling water increases significantly, and the cooling effect of the water-cooled channel reaches its maximum value.  
Additionally, heat evacuation performance is significantly affected by the configuration of the coolant channels. [PERSON] et al. [28] studied the thermal efficiency of heat evacuation under different forms of coolant channel in the motor housing. Three commonly used cooling sleeves are shown in Figure 7. Under the same working conditions, the best cooling effect can be achieved with a spiral curved flow path, which can be structurally optimized and designed. This is due to its small flow losses, the maximum flow velocity and the minimum temperature gradient between the flow channels in this structure. [PERSON] et al. [21] also investigated the influence of cooling channel shape on heat evacuation efficiency. Cooling efficiency is subject to flow loss, temperature uniformity, heat transfer efficiency and other factors. It has been found that the most widely used water-cooled runners have four main structural forms, as shown in Figure 8. Among these forms, the quasi-series possesses the best cooling effect based on a combination of flow loss and heat transfer efficiency.  
Figure 6: Four main convex structure types for motor housing cooling channels [27].  
In addition to thermal efficiency, temperature uniformity of the motor is also a key research direction. [PERSON] et al. [29] have proposed a water-cooled housing with balanced cooling capacity, which is optimized for the purpose of temperature uniformity and cooling efficiency. The contact surfaces of the thermal conduction casing are optimized so that the cooling output and heating input in different areas can be balanced, as shown in Figure 9. The results show that this structure can minimize the shell temperature difference with good heat dissipation efficiency.  
Figure 8: Typical designs of stator cooling sleeves and resulting flow paths in the fluid domain [21].  
Figure 7: Expansion diagram of different cooling water channels [28].
# A Polymer Inclusion Membrane for Sensing Metal Complexation in Natural Waters
## 3 Results
### Evaluation of Zn Speciation in a River Water Sample  
The developed PIM sensor was used to determine Zn complexation of a water collected in Osor River. As mentioned in the experimental section, this river receives inputs from an abandoned mine, resulting in an abnormal Zn concentration in the water. This water also contains trace levels of Fe, Cu, and Mn [28].  
The water sample was fully characterized, and the results are shown in Table 2. No Cu was detected at levels higher than the LOD of the instrument (20 \(\mathrm{\SIUnitSymbolMicro g}\) L\({}^{-1}\)). Moreover, at this particular sampling point, water had high conductivity, which is attributed mainly to sulphate ions originating from the mineral content of the soil.  
This water sample was analyzed using the PIM sensor. It is important to point out that water samples did not receive any treatment before their analysis. The metal measurement of the receiving solution revealed an accumulation of 0.034 \(\pm\) 0.001 mM Zn. This value represents a 78% decrease of the accumulation value (0.15 mM) calculated using the calibration curve shown in Figure 4 (which considers all metal as a free species). Taking into account that the extraction of both Zn and Cu with D2 EHPA is not affected at pH > 5 [15; 29; 30] results obtained with the PIM device can be related with the complexation of metal in the water sample.  
This result may be relevant from the point of view of the impact of the metal on the ecosystem.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline pH & 7.67 & Alkalinity & 144.90 mg HCO\({}_{3}\)– L\({}^{-1}\) \\ Conductivity & 601 mS cm\({}^{-1}\) & [NO\({}_{3}\)–] & 1.04 mg L\({}^{-1}\) \\ [Zn\({}^{2+}\)] & 0.76 mg L\({}^{-1}\) (=0.012 mM) & [PO\({}_{4}\)3–] & 0.12 mg L\({}^{-1}\) \\ [Cu\({}^{2+}\)] & n.d. & [Cl\({}^{-}\)] & 16.24 mg L\({}^{-1}\) \\ [Na\({}^{+}\)] & 31.26 mg L\({}^{-1}\) & [SO\({}_{4}\)2–] & 132.89 mg L\({}^{-1}\) \\ [K\({}^{+}\)] & 3.77 mg L\({}^{-1}\) & [F\({}^{-}\)] & 1.66 mg L\({}^{-1}\) \\ [Ca\({}^{2+}\)] & 130.38 mg L\({}^{-1}\) & TOC & 0.92 mg C L\({}^{-1}\) \\ [Mg\({}^{2+}\)] & 15.23 mg L\({}^{-1}\) & & \\ \hline \hline \end{tabular} n.d.—not detected.  
\end{table}
Table 2: Water physico-chemical parameters at the sampling point of the Osor River.  
Figure 7: Effect of Cu presence on Zn accumulation. Feed phase: 100 mL of a 0.03 mM Zn and 0.008 to 0.03 mM of Cu in 0.01 M KNO\({}_{3}\), pH 6.0; PIM: M2; receiving phase: 5 mL 0.01 M HNO\({}_{3}\) (\(n=3\)).