esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Permeability of Microbial-Solidified Calcareous Sand Based on MICP
## 1 Introduction
This process will inevitably cause changes in the permeability of the calcareous sand foundations. Some scholars have conducted related studies on the permeability properties of calcareous sands [12; 43]; in addition, some MICP-based permeability characteristics were investigated [44], but there are few studies on the effects of particle gradation on the permeability properties of MICP-improved calcareous sands. Altogether, there exists the need for an understanding of the permeability variation in MICP-improved calcareous sand with various gradations. Possibly, different particle gradations could change the pore space structure between the sand particles, thus affecting the reinforcement effect of the MICP treatment. The novelty of our work lies in testing this hypothesis through a series of penetration tests, CT scan tests and microstructure tests. Based on a self-made grouting and seepage device, cemented sand columns were formed. The effects of grain gradation, with indicators including the curvature coefficient (\(C_{u}\)) and the uniformity coefficient (\(C_{c}\)), on permeability were studied. A CT scan was conducted to investigate the porosity of the cemented sand. The content of the induced calcium carbonate was measured using a weighting method. A scanning electron microscopy (SEM) technique was adopted to further study the micromechanism of the MICP treatment in sands of different gradations. Finally, the correlation of the permeability coefficient with \(C_{u}\) and \(C_{c}\) was semiquantitively analyzed.
## 2 System Model
### The General Framework
#### 2.1.3 Target Vehicle Switching  
Within the \(n\)th time slot, vehicle \(k\) uses V2V communication to share sensing domains between vehicles. At the end of the time slot, vehicle \(k\) switches to a new target vehicle \(k^{\prime}\) using the sensing information from other vehicles, effectively reducing path loss caused by communication distance and obstructions. At the start of the next time slot, return to step (a) and continue.
# Gene Networks and Pathways Involved in _Escherichia coli_ Response to Multiple Stressors
## 3 Results
### Network Analysis of DEGs and Identification of Hub Genes  
Based on the functional categories outlined in the previous section, we proceeded to pinpoint \"hub genes\" that might play key roles within each functional category as a part of _E. coli_ response to multiple stressors. Accordingly, we computationally generated a protein-protein interaction network (PPINs) using the STRING database to highlight important nodes with numerous interaction partners and a few major sub-clusters. The network of upregulated genes in at least four stressors showed fewer connections (Supplementary Figure S1) and its main sub-clusters are represented in Supplementary Figure S2; consequently, we generated PPIN of upregulated genes common in at least three stressors (Figure 5A). PPIN of the downregulated genes common in at least four stressors are presented in Figure 5B.  
Figure 5: Network of (**A**) 461 upregulated genes in at least three stressors, and (**B**) 127 downregulated in at least four stressors with medium confidence (cut-off score: 0.4).  
Our network analysis highlighted the following stress response hubs: for upregulated genes, the following hubs were identified: _elaB_, _dps_, _fbaB_, _fic_, _myyB_, _ostA_, _ostB_, _osmY_, _sra_, _uspB_, _wrbA_, _ybaY_, _ybgS_, _ycaC_, _yccl_, _ycgB_, _yeaG_, _yeaH_, _yegP_, _yhfG_, _yiaG_, _yodD_, _yqiC_ (Figure 6A); _entF_, _entS_, _fepB_, _fepC_, _fepD_, _fepG_, _fes_, _ybdZ_ (Figure 6B); _hoffM_, _opgC_, _yaiY_, _ydcY_, _ydhl_, _ydiH_, _ydgK_, _ygbc_, _yifN_, _ypeC_ (Figure 6C); _arAC_, _bluf_, _csiE_, _dgcZ_, _dosC_, _elfA_, _glgS_, _lexA_, _mqsA_, _mqsR_, _mutM_, _nrdD_, _nrdE_, _nrdF_, _nrdG_, _nrdH_, _nrdI_, _polB_, _rcnB_, _recC_, _recN_, _recQ_, _rpoS_, _umuD_, _urrA_, _yahA_, _ydiV_, _ygaC_, _yhN_, _yhhA_, _yile_, _yodC_, _ypfG_, _ytfK_ (Figure 6D).  
For downregulated genes, the following hubs were identified: _purF_, _purD_, _purN_, _purT_, _purL_, _purM_, _purK_, _purE_, _purC_, _purB_, _purH_, _pryB_, _pyrC_, _pyrD_, _carA_, _carB_, _cppA_ (Figure 7A); _flgH_, _flgG_, _flik_, _fliM_, _flip_, _flhA_, _flhB_, _egS_ (Figure 7B); _muoC_, _muoE_, _muoF_, _muoH_, _muol_, _nujD_, _pfo_, _prs_, _codA_, _codB_, _glyA_, _pryI_, _upp_, _urA_, _cyoB_ (Figure 7C).  
Figure 6: Four main sub-clusters (numbered **A–D**) were identified by the MCODE clustering algorithm from the network of the upregulated genes in at least three stressors. Examples of the hub genes are mentioned in the Discussion section.
## 3 Proposed System Model
### Simple Handover Protocol  
The usefulness of the generated data can be illustrated by a simple simulation of three legitimate BS units with physical positions to emulate a short stretch of a 5G urban motorway, as illustrated in Figure 2(a). The frequency of communication used is 3.8 GHz, part of the 5G New Radio spectrum. The RSRP for each BS is determined for each second that the mobile UE (platoon leader) is in the BS coverage area, as shown in Figure 2(b). The handover decision in 5G RAN is based primarily on the details found in the UE MR [41]. The mobile UE (the platoon leader) calculates the signal strength of the BS units from which it can receive a signal. The UE handover from one BS to another occurs when the signal strength from the 2 nd BS exceeds a given threshold. As shown in Figure 2(c), in this example, the threshold for handover is 5 dB [42].
# Effects of \"S\"-Type Bowed Guide Vanes on Unsteady Flow in 1.5-Stage Axial Compressors
###### Abstract  
In axial compressors, the unsteady flow caused by the interaction between dynamic and static cascades will make the moving vanes subject to periodic forces and increase the risk of high-cycle fatigue fractures. In this study, an \"S\"-type bowed guide vane was designed and a 1.5-stage axial compressor model was established. For five guide vanes with different bending coefficients, unsteady numerical simulation was carried out under design conditions and near-blockage conditions. The influence of the guide vane bending coefficient on the pressure ratio and efficiency is analyzed, and the aerodynamic exciting force on moving vanes is analyzed by using the fast Fourier transform. The study shows that the model with an \"S\"-type bowed guide vane can greatly reduce the amplitude of aerodynamic exciting force on moving vanes. The model with a guide vane bending coefficient of \(-10\) mm can reduce the tangential and axial aerodynamic exciting force amplitudes at the first-order blade-passing frequency by 90.82% and 90.39% under the design conditions, respectively. Under the near-blockage condition, the tangential and axial aerodynamic exciting force amplitudes can be reduced by 85.84% and 86.58%, respectively. This can greatly improve the vibration safety of the moving vane.  
axial compressor; unsteady flow; aerodynamic exciting force; \"S\"-type bowed; guide vane
## 7 Experimental Evaluation
### Results for the Full Request Satisfaction Mode
#### 7.2.2 Sensitivity to Constellation Size  
The comparison between the algorithms for utility, computation time, and leximin profiles does not change with respect to the number of orbital planes. In other words, the relative performance of the algorithms is the same whatever the size of the constellation. Figure 10(a) shows that the normalized global utility obtained by the agents does not increase a lot with the constellation's size. However, the allocation's global reward increases with the growing number of orbital planes. In fact, as shown in Figure 10(b), the global reward obtained for 2 orbital planes (i.e., a constellation with 4 satellites) is around 1000 for the util algorithm. When considering 16 orbital planes (i.e., a constellation with 32 satellites), such a reward almost reaches 1500, at best. The fact that the normalized utility does not increase with the constellation size but the global reward does, comes from the normalization factor. In fact, with 32 satellites, the global utility that the agents can obtain individually is higher than with 4 satellites. However, the global utility obtained by the agents is relatively the same compared with their best paths and results in a similar normalized global utility.  
The time required for computing the allocations also increases with the size of the constellation. More precisely, Figure 10(c) shows that computation time is multiplied by 10 when the number of satellites increases from 4 to 32. This comes from the higher number of orbit slots and consequently much larger graphs (see Table 2) with more paths to explore and more constraints to check.  
The average utility profiles given in Figure 12 show that the utility profiles obtained by algorithms do not change much with the constellation size. Even with more satellites, at the most two agents have a utility strictly greater than 0. This confirms the high number of conflicts of the requests in the considered instances. This illustrates the low utility and reward obtained in this setting: few requests are fulfilled in the end.  
These results show that, in the case of V-DPAP with full request satisfaction mode, algorithm util is quite interesting in terms of global utility versus required time. Moreover,as the instances do not allow the utility profiles to be balanced between the agents, this algorithm provides as fair allocations as algorithm lex.
###### Abstract  
Remote sensing reflectance (Rrs), which is currently measured mainly using the above-water approach, is the most crucial parameter in the remote sensing inversion of plateau inland water colors. It is very difficult to measure the Rrs of plateau inland unmanned areas; thus, we provide a measurement solution using a micro-spectrometer. Currently, commercial micro-spectrometers are not factory calibrated for radiation, and thus, a radiometric calibration of the micro-spectrometer is an essential step. This article uses an Ocean Optics micro-spectrometer (STS-VIS) and a traditional water spectrometer (Trios) to simultaneously measure the irradiance and radiance of diffuse reflectance plates with different reflectance values for field calibration. The results show the following: (1) different fiber types have different calibration coefficients, and the integration time is determined according to the diameter of the fiber and the type of fiber, and (2) by comparing the simultaneous measurement results of STS-VIS with Trios, the mean absolute percentage difference (MAPD) of both reached 18.64% and 5.11% for Qinghai Lake and Golmud River, respectively, which are accurate Rrs measurements of water bodies. The Rrs of the Hoh Xil and Qarhan Salt Lake water bodies in unmanned areas of China was measured, and this was the first collection of in situ spectral information with a micro-spectrometer. This article shows that the micro-spectrometer can perform the in situ measurement of water Rrs in unmanned inland areas. With this breakthrough in the radiometric performance of the micro-spectrometer, we are able to obtain more accurate remote sensing reflectance results of unmanned water bodies.  
Keywords:field radiometric calibration; micro-spectrometer; remote sensing reflectance; plateau inland waters +
Footnote †: journal: Applied Sciences
###### Abstract  
The study of deep shale gas (\(>\)3500 m) has become a new research hotspot in the field of shale gas research in China. In this study, 16 representative deep shale samples were selected from different layers of the Wufeng-Longmaxi Formation in the Z-3 well in the western Chongqing area to conduct low-field nuclear magnetic resonance (NMR) tests, field-emission scanning electron microscopy (FE-SEM) observation, and fractal modeling. By comparing the differences in pore structure and their influencing factors in representative samples from different layers, the particularities of high-quality reservoirs have been revealed. The results show that the Z-3 well shales mainly develop micropores and mesopores, with pore sizes of 1 nm-200 nm. The fractal dimensions of bound fluid pores D\({}_{1}\) (1.6895-2.3821) and fractal dimension of movable fluid pores D\({}_{2}\) (2.9914-2.9996) were obtained from \(T_{2}\) spectra and linear fitting, and the pores were divided into three sections based on the NMR fractal characteristics. TOC content was one of the major factors affecting the gas content in the study area. The shale samples in the bottom S\({}_{1}\)[1]-[1] sub-layer with a higher TOC content have larger porosity and permeability, leading to enhanced homogeneity of the pore structure and favorable conditions for shale gas adsorption. A comparative understanding of the particularities of pore structure and influencing factors in high-quality reservoirs with higher gas content will provide the scientific basis for further exploration and exploitation of the Wufeng-Longmaxi Formation deep shale reservoirs in the western Chongqing area.  
deep shale; pore structure; fractal analysis; high-quality reservoirs; Wufeng-Longmaxi Formation; heterogeneity Article
# Magnetoelectric Nanoparticles for Wireless Peripheral Nerve Stimulation: A Computational Study
## 2 Materials and Methods
### Electromagnetic Simulation  
#### 2.1.1 MENPs Model  
Leveraging the results of our previous multiphysics studies [18; 19], the electric model of the core-shell CoFe\({}_{2}\)O\({}_{4}\)-BaTiO\({}_{3}\) composites consisted of a dipolar distribution aligned along the hypothesized direction of the external low-frequency magnetic field used for eliciting the ME effect. The dipole was modeled as a positive hemisphere of 2.5 mV and a negative hemisphere of \(-2.5\) mV separated by an insulating layer of techotane material (V = 0 mV, \(\sigma\) = 0 S/m, \(\varepsilon\) = 3.4, and \(\upmu_{\text{r}}\) = 1). The electric potential was assigned by using Dirichlet boundary conditions. Specifically, the model of the dipole with potential equal to \(\pm 2.5\) mV on each hemisphere corresponded to the maximum amplitude reached by the MENPs when placed into a uniform external magnetic field. The corresponding ME coefficient was equal to 0.28 V/cm\(\cdot\)Oe, which was in the range of the ME coefficients reported in the literature [22; 23].  
Geometrically, the modeling distinguished a total of four different MENPs dimensions related to variable MENPs' concentrations injected into a single fascicle of neural fibers. Initially, we hypothesized the injection of a single MENP to investigate the effects of a highly localized stimulation. The MENP was modeled as a sphere with a diameter of 80 nm (Figure 1a,b) in accordance with computational and experimental studies [15; 19; 24; 25]. Additionally, considering that experimental studies [7] indicate that MENPs tend to agglomerate when injected into biological means, with the size of the agglomerates depending on the injected MENPs' concentration, we also included larger MENP agglomerates of varying size (referred to as 'clusters') in our simulations. The clusters were geometrically modeled as spherical structures, and diameters equal to 80 \(\upmu\)m, 250 \(\upmu\)m and 500 \(\upmu\)m (Figure 1c,d,e).  
Figure 1: Schematic representation of MENP and MENPs clusters configurations localized in proximity of a Ranvier’s node of a neural fiber when the potential distribution over the MENPs’ surface reach its maximum amplitude. The MENP–fiber distance is 1R. The background represents the fascicle’stissue, and the nodal and non-nodal segments of the neural fiber's dynamic model are explicated. The latter consist of paranodal myelin attachments (MYSA), paranodal main segments (FLUT), and internodal segments (STIN). (**a**) 80 nm single MCNP parallelly oriented to the neural fiber; (**b**) 80 nm single MCNP perpendicularly oriented to the neural fiber; (**c**) 80-\(\upmu\)m, (**d**) 250-\(\upmu\)m, and (**e**) 500-\(\upmu\)m clusters of MENPs with perpendicular orientation to the neural fiber.
## 3 Results and Discussion
### Volatile Emissions from Reproductive-Stage, Field Grown Soybean  
Comparison of total ion chromatograms (TICs) and single ion (120) chromatograms characteristic of MeSA for the headspace of leaves from non-infested and _A. glycines_-infested soybean plants grown in the field is shown in Figure 1B. The TICs were similar to those obtained from the leaves grown in the greenhouse. No thrips were observed on soybean leaves collected from the field, on either aphid infested or non-infested leaves. Infested soybeans leaves had an average of 31.3 _A. glycines_ (adults and nymphs) with a standard deviation of 7.5. We detected several compounds from these field grown soybeans that varied significantly between leaves infested and non-infested with _A. glycines_ (Figure 2). There were four compounds that were found in greater abundance from non-infested leaves; acetone (\(t=2.69\), df = 6, \(P=0.04\)), 2-methylfuran (\(t=5.39\), df = 6, \(P=0.002\)), _trans_-caryophyllene (\(t=3.38\), df = 6, \(P=0.02\)) and _alpha_-humulene (\(t=4.31\), df = 6, \(P=0.005\)). Three compounds were found in greater abundance from leaves infested with _A. glycines_, cis-3-hexane-1-ol acetate (t = 2.95, df = 6, \(P=0.03\)), MeSA (\(t=12.33\), df = 6, \(P=0.0001\)) and _trans-beta_ farnesene (\(t=25.14\), df = 6, \(P=0.0001\)). Analysis of leaf area and MeSA revealed no significant relationship (analysis not shown).  
Figure 2: Mean (+SEM) abundance of several selected compounds per leaf on reproductive stage soybeans grown in the field with (gray bar) and without _A. glycines_ (white bar). The concentration of each compound was significantly different between the two treatments (statistics reported in the text).
# A Simple Water Retention Model Based on Grain Size Distribution
## 3 Proposed Model
### Primary Pore Filling
#### 3.2.1 Smallest Particle Criterion  
The first pore filling criterion pertains to the smallest particle in the material: if the diameter of the particle needed to create the Apollonian gasket (\(d_{\rm ij}\)), is smaller than the minimum particle size (\(d_{\rm min}\)) in the GSD, then particle \(d_{\rm ij}\) is not placed within the pore \(p_{\rm i}\) as no sufficiently small particles are deemed to be available. This criterion could result in either no filling (\(d_{\rm i1}<d_{\rm min}\)), partial filling (\(d_{\rm i1}>d_{\rm min}>d_{\rm i1}\)), or maximal filling (\(d_{\rm i2}>d_{\rm min}\)). The value for \(d_{\rm min}\) in this study was calculated as per Equation (5).  
\[d_{\rm min}=d_{\rm 1\%,adjusted} \tag{5}\]  
where \(d_{\rm 1\%,adjusted}\) is the smallest adjusted particle diameter (see Equation (2)) where there is at least 1% of the mass fraction finer. The adoption of 1% mass fraction finer is an arbitrary value chosen to prevent extremely small amounts of very fine particles in a soil, overly affecting the filling of pores in the remaining soil.  
Figure 6: Representation of a partial Apollonian gasket created by filling the primary pore \(p_{\rm i}\) with: (**a**) three particles named \(d_{\rm ij}\) (j = 1), (**b**) six particles named \(d_{\rm ij}\) (with j ranging from 1 to 2 to distinguish particle size). In this figure (**a,b**) show two different degrees of filling possible in the proposed model.  
It should be noted here that satisfying the smallest particle criterion does not necessarily ensure that a pore will be filled in any particular way; it simply determines what degree of filling is possible. The criteria that follow determine if a primary pore is filled.
## 4 System Application Example
### Conclusions  
The system can generate the analysis data required for production design and can complete the data analysis of the complete assembly scheme and interchangeability scheme, automatically and consistently. On the premise of ensuring that the design data meet the design accuracy, the system generates a preliminary design scheme reference meeting the production process requirements. In the early stages of part design, the system can use limited data to generate a more comprehensive design scheme, improve the production design efficiency of small batches and multiple changes, reduce the error rate of manual operation, and reduce the cost of purchasing intelligent design software and computer hardware configuration.
# Networking for Cloud Robotics: The DewROS Platform and Its Application
## 6 Experimental Evaluation
### Analysis of Response Time
The results reported in Figure 19 evidence that the video length has a very small effect on the response time, when comparing a video of 3 s with one of 71 s. If we look at the satellite connection, where the distributions are quite similar, the maximum deviation between the medians is about 3 s.
## 4 Test System and Simulation Results
### Simulation Results  
To evaluate the effectiveness of the proposed method, we simulated the cases of no control, of the FFR operating only the BESS, of the FFR operating the BESS and PMSG-type wind turbine, and of the FFR operating with the proposed control. A typical FFR control scheme injects real power when reaching the deadband. The BESS produces its maximum output, and a wind turbine based on PMSG performs torque limit control. For frequency events, SG #2 generating 10 MW tripped out within 20 s in all cases, resulting in a severe frequency drop due to transient power imbalance.  
The NCCS does not set the specified RoCoF constant \(\alpha\). This is because \(\alpha\) may vary depending on the activation and communication delay of the IBRs and the inertia of the system. Figure 9 shows the simulation results for changing \(\alpha\). The \(\alpha\) values close to zero are too late to improve the lowest frequency, and the \(\alpha\) values too far from zero do not effectively prevent secondary frequency dip. In this test system, \(\alpha\) was set to \(-0.45\). Figure 10 shows the RoCoF when only the PMSG responds to the FFR.  
Figure 11 shows the simulation results for the various control methods in FFR. In the case of FFR control in which the BESS- and PMSG-type wind turbines are activated in the deadband, the second frequency dip was lower than the first frequency dip, indicating that resource utilization was inefficient. However, because the proposed control scheme performed the control to improve the lowest frequency after reaching a specific RoCoF, it was confirmed that the frequency improvement was effective with the same resources.  
Figure 12 shows the resulting electrical torque and rotor speed during the wind turbine torque limit control. As shown in Figure 4b, the electrical torque was reduced to 1 pu and kept constant, after which the MPPT operation was performed. It can be seen that the rotor speed decreased when generating additional output, converting the inertia of the drivetrain into electrical energy and recovering normally through the acceleration section.  
Figure 8: Configuration of the test system.