esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### IMD Activated the cAMP/Rac1 Pathway and Mitigated Peritubular Capillary Injury in CIAKI Rats  
Western blot analysis was conducted to examine the levels of Rac1, VEGFR and VE-cadherin, focusing on the cAMP/Rac1 and adhesion pathways in IMD-mediated effects within the CIAKI rat model. IMD enhanced the expression of Rac1 and VE-cadherin; however, these effects were nullified by the specific inhibition of Rac1. This suggests that IMD mitigates iohexol-induced destruction of adherens junctions through activation of the cAMP/Rac1 pathway. Additionally, it was observed that iohexol stimulation elevated VEGFR2 expression, a critical receptor for neovascularization, whereas IMD attenuated this increase, and specific Rac1 inhibition also blocked the IMD-induced effects [29; 30]. This indicates that iohexol inflicts damage on renal microvessels, leading to a reactive increase in VEGFR2 expression, which promotes neovascularization. Our findings demonstrate that IMD protects renal microvessels from iohexol-induced damage, and this protective effect is inhibited by Rac1 blockade (Figure 4A,B), further supporting the involvement of the cAMP/Rac1 pathway in the action of IMD. Relative expressions of CD34 and ICAM1 were evaluated using immunohistochemistry and immunofluorescence to assess peritubular capillary damage. As a marker of microvascular endothelium, CD34 expression inversely correlates with the extent of microvascular damage [31]. Results revealed that CD34 expression was significantly lower in the CIAKI group compared to the control group, while the IMD + iohexol group exhibited significantly higher CD34 levels compared to the CIAKI group, with NSC23766 reversing effects of IMD (Figure 4C-E). ICAM-1, also known as CD54, is part of the immunoglobulin superfamily of adhesion molecules, and its increased expression indicates vascular endothelial injury [32]. In CIAKI rat kidneys, ICAM1 expression was elevated, but IMD intervention significantly reduced ICAM1 levels, an effect that was abolished by Rac1 inhibition. These findings suggest that IMD mitigates renal microvascular injury in CIAKI rats by activating the cAMP/Rac1 pathway.
# Classical and Quantum Gases on a Semiregular Mesh
## 3 Hard-Core Bosons on a Spherical Mesh: Extended Bose-Hubbard Model  
The PD mesh is regular enough that we can study the quantum analog of the lattice-gas model in relatively simple terms. The obvious bosonic counterpart of (1) is the hard-core limit of the extended Bose-Hubbard (BH) model  
\[H = -t\sum_{\langle i,j\rangle}\Bigl{(}a_{i}^{\dagger}a_{j}+a_{j}^{ \dagger}a_{i}\Bigr{)}-t\sum_{\langle\langle k,l\rangle\rangle}\Bigl{(}a_{k}^{ \dagger}a_{l}+a_{l}^{\dagger}a_{k}\Bigr{)} \tag{11}\] \[+\frac{U}{2}\sum_{i}n_{i}(n_{i}-1)+V_{\langle i,j\rangle}\,n_{i}n _{j}+V_{2}\sum_{\langle\langle k,l\rangle\rangle}\,n_{k}n_{l}-\mu\sum_{i}n_{i}\,,\]  
where \(a_{i},a_{i}^{\dagger}\) are bosonic field operators and \(n_{i}=a_{i}^{\dagger}a_{i}\) is a number operator. Moreover, \(t\geq 0\) is the hopping amplitude, taken for simplicity to be the same for first- and second-neighbor pairs, whereas \(U>0\) is the on-site repulsion. In the hard-core limit \(U\to+\infty\), the site occupancies are effectively restricted to zero or one and the \(U\) term can thus be discarded. For hard-core bosons, creation and annihilation operators at different sites commute, whereas \(a_{i}\) and \(a_{i}^{\dagger}\) are _anticommuting_ operators as a result of the dynamical suppression of Fock states with two or more particles in the same site [27].  
In the original BH model [28; 29; 30], where the second, fourth, and fifth terms in \(H\) are absent, the tunneling term (kinetic energy) is minimized by a condensed state spread over the entire volume of the system, whereas the potential energy favors particle localization. As a result, the \(T=0\) system exists as either a superfluid (large \(t/U\)) or a Mott insulating ground state (small \(t/U\)), separated by a quantum transition. The Bose-Hubbard Hamiltonian can be derived starting from the second-quantized Hamiltonian describing a gas of ultracold bosonic atoms subject to an optical-lattice potential [18].  
The phase scenario becomes richer when the range of interaction is increased: depending on the lattice, other Mott insulating ground states (density waves) may appear; moreover, crystalline order may coexist with superfluidity (supersolids) [31; 32; 33; 34; 35; 36]. Supersolidity is a fascinating property of quantum matter, which has only recently been experimentally detected in a gas of dipolar atoms [37; 38; 39]. In a supersolid, atoms can simultaneously support frictionless flow and form a crystal. As suggested by [PERSON], a rotating supersolid should have a moment of inertia that is reduced with respect to its classical value [40]. This phenomenon is called \"nonclassical rotational inertia\" and its first observation is reported in a paper published this year [41].  
Due to the semiregular character of the PD mesh, in our system the superfluid phase would be discouraged in favor of less-symmetric condensed phases, and a supersolid region will then occur at low temperature. To check this expectation, we employ a mean-field theory, an approach known to give accurate results in the continuum [42; 43; 44].
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Datasets
#### 2.2.1 Corn Yield Data  
The corn yield data were collected from the National Agricultural Statistics Service Information (NASS), United States Department of Agriculture (USDA) [53]. The yield data were aggregated at the county level and are available to the public on their website ([[https://quickstats.nass.usda.gov](https://quickstats.nass.usda.gov)]([https://quickstats.nass.usda.gov](https://quickstats.nass.usda.gov)), accessed on 18 November 2021). The county-level yield data have been used by many studies as the government provides most of the datasets at the county level [25; 31; 34; 40]. County-level data from 2006 to 2020 were collected and mapped for each year using the geographic boundaries of the counties obtained from the US Census Topologically Integrated Geographic Encoding and Referencing (TIGER) project.
# Fibers Effects on Contract Turbulence Using a Coupling Euler Model
## 2 Coupling Models and Methods
### Mathematical Description of Fibers Orientation Distribution  
There are two premises to assure that the suspension flow can be regarded as a single continuum medium: (1) The size of the added particles would be much smaller than the  
Figure 1: Two-dimension model of contraction.  
characteristic scale of the flow field and the suspension is quasi-uniform; (2) The Reynolds number of the particles, characterized by the relative velocity of the particles in the flow field, should be small enough to satisfy the quasi-Newtonian fluid.  
Therefore, the flow behavior of the fiber suspension can be described by using Euler method. The orientation state of abundant suspended fibers in the dilute flow is described by a Fokker-Plank type function \(f(t,x,\phi)\), which is the probability of a fiber orienting \(p\) occurring at time \(t\) and placement \(x\). Considering the turbulent effect according to the Reynolds average idea, Equation (1) is given for a 2D steady incompressible flow and its detailed derivation is shown in the literature [18].  
\[\frac{\partial^{2}\overline{f}}{\partial^{2}\phi}=H\frac{\partial\overline{f} }{\partial\phi}+R\overline{f}+B\frac{\partial\overline{f}}{\partial x}+G\frac {\partial\overline{f}}{\partial y} \tag{1}\]  
where the coefficients in the Equation (1) can see Equations (2)-(5):  
\[B=\frac{\sin^{2}(2\phi)}{4D_{r}}\overline{u}, \tag{2}\]  
\[G=\frac{\sin^{2}(2\phi)}{4D_{r}}\overline{v} \tag{3}\]  
\[H=-\frac{\sin^{2}(2\phi)}{2D_{r}}\bigg{(}\frac{\partial\overline{u}}{ \partial x}\sin(2\phi)+\frac{\partial\overline{v}}{\partial x}\sin^{2}\phi- \frac{\partial\overline{u}}{\partial y}\cos^{2}\phi\bigg{)} \tag{4}\]  
\[R=-\frac{\sin^{2}(2\phi)}{4D_{r}}\bigg{(}\frac{\partial\overline{u}}{ \partial x}\cos(2\phi)+\frac{1}{2}(\frac{\partial\overline{u}}{\partial y}+ \frac{\partial\overline{v}}{\partial x})\sin(2\phi)\bigg{)} \tag{5}\]  
Here \(p\) is a fiber's unit orientation vector along the coordinate axes (see Figure 1), i.e., \(p=(\cos\phi,\sin\phi)^{T}\). \(\phi\) is an orientation angle of fibers; \(D_{r}=\left[a(v/\varepsilon)^{1/2}+bL(v\varepsilon)^{-1/4}\right]^{-1}\), is the rotational diffusion rate of fibers; \(v\) is the kinematic viscosity; \(\varepsilon\) is the turbulent dissipation rate. \(u\), \(v\) are the \(x\)-component and \(y\)-component of flow velocity \(V\) respectively.
## 3 Results and Discussion
### Preliminary Material Characterization: Effects of Processing Parameters
#### 3.1.2 Macro- and Microstructure Phase Investigations
At the highest applied pressure of 540 MPa, for example, the high-intensity zirconia contaminants were redispersed--without disappearing--into a cascade of minor traces traversing the wider 2\(\theta\) range. The effect of this on the ionic conductivity of NASICON pellets will determine whether a balancing strategy is viable regarding the applied pressure levels for countering the negative repercussions of elevated temperature (i.e., zirconia contamination).
## 3 Architecture of DT Workshop System
### Intelligent Workshop Integrated Manufacturing Execution Mode  
As the basic unit of offshore platform construction, the assembly and welding workshop is based on the principle of grouping technology, with the intermediate product as the guide, and production is organized by area, with the platform deck as the base and outfitting as the core, and the \"structure-outfitting-painting\" operations are separated in space and ordered in time to realize the integration of design, production, and management [37]. The integrated operation mode is balanced and continuous, avoiding the traditional mode of independent and uncoordinated production links, reducing the simultaneous cross-work of multi-disciplinary construction, and simplifying the work packages interface and the interference between different types of work.  
The integration manufacturing execution mode has extremely strict requirements on the production data chain. As shown in Figure 2, the five major elements surrounding the operation of the workshop are associated with dozens of forms. They are intertwined with design, planning, materials, equipment, storage, quality control, and other professions. In the intelligent workshop, in addition to cranes, welding torches, material pallets, and other manually operated equipment, there are automatic plate-cutting machines, gantry-type welding robots, CNC pipe-cutting machines, automatic plate winders, material conveyors, uncrewed transportation vehicles, and other automated uncrewed equipment. In general, more than 60% of the equipment is networked. In the construction process, the operators adopt the workshop-level plan as the main line. Each workstation completes the processes of material collection, plate assembly, welding, and inspection according to the work order and provides progress and quality information in real time. Structural and quality expertssimultaneously verify the weld quality and global strength properties of completed assemblies across multiple stations, in hours, to form production master data under a time series. With the addition of digital devices, data-aware collection devices, and simulation models in the smart shop, the conversion and integration of multidimensional heterogeneous data demand to be realized under a unified and customized architecture. A standardized production database relying on the model proposed in Section 4 enables optimizing production quantities, balancing operational tasks, improving equipment utilization, reducing energy consumption, etc.
###### Abstract  
We estimated source-based aerosol optical properties for polydisperse aerosols according to a chemical-species-resolved mass contribution method based on source apportionment. We investigated the sensitivity of aerosol optical properties based on PM2.5 (particulate matter that have a diameter of less than 2.5 micrometers) monitoring results. These aerosols were composed of ions, metals, elemental carbon, and water-soluble organic carbon which includes humic-like carbon substances and water-soluble organic carbon. We calculated aerosols' extinction coefficients based on the PM2.5 composition data and the results of a multivariate receptor model (Solver for Mixture Problem model, SMP). Based on the mass concentration of chemical composition and nine sources calculated with the SMP receptor model, we estimated the size-resolved mass extinction efficiencies for each aerosol source using a multilinear regression model. Consequently, this study quantitatively determined the size resolved sources contributing to the apportionment-based aerosol optical properties and calculated their respective contributions. The results show that source-resolved mass concentrations and extinction coefficients had varying contributions. This discrepancy between the source-based mass concentration and extinction coefficient was mainly due to differences between the source-dependent aerosol size distribution and the aerosol optical properties from different sources.  
Keywords:mass extinction efficiency; SMP receptor model; size- and source-resolved aerosol optical properties; polydispersity +
Footnote †: journal: Applied Physics Letters
## 3 Federated Kalman Filter Design
### Kalman Filter Algorithm for Inertial/Odometer Integrated Navigation
#### 3.6.2. State Vector Fusion Process  
State vector fusion means that the local state estimation of the Kalman filter of each subsystem is fused to obtain the global state estimation. After adopting the \"upper bound of variance\" technique, the measurement update and time update of each subfilter were performed independently, that is, the local estimation of each subfilter was irrelevant, so there was a state vector fusion algorithm.  
When the state vector of each subfilter is the same, the global state estimate of the federated filter is the weighted average of the local estimates of the state vectors of all subfilters. The inverse of the positive definite symmetric weighting matrix is the sum of the inverse matrices of the covariance matrix, \(\mathrm{P}_{1}(\mathrm{k})(\mathrm{i}=1,2)\) of each subfilter state estimation error. The globe state estimation equation is as follows:  
\[\hat{\mathrm{X}}(\mathrm{k})=\left[\sum_{\mathrm{i}=1}^{2}\mathrm{P}_{\mathrm{ i}}^{-1}(\mathrm{k})\right]^{-1}\left[\sum_{\mathrm{i}=1}^{2}\mathrm{P}_{ \mathrm{i}}^{-1}(\mathrm{k})\hat{\mathrm{X}}_{\mathrm{i}}(\mathrm{k})\right] \tag{26}\]  
The global state estimation error covariance is as follows:  
\[\mathrm{P}(\mathrm{k})=\left[\sum_{\mathrm{i}=1}^{2}\mathrm{P}_{\mathrm{i}}^{ -1}(\mathrm{k})\right]^{-1} \tag{27}\]
## 3 Model Construction  
### Objective Functions  
#### 3.1.1 Objective Extraction  
The Huangjinvia Reservoir undertakes multi-objective such as water diversion, power generation, flood control and ecology, among which the flood control objective can be met by the water level constraints of the reservoir in flood season. This paper mainly extracted the water diversion objective, power generation objective and ecology objective and analyzed the correlation between these three objectives before constructing the multi-objective optimal operation model.  
1. Water diversion objective: maximum water diversion (Equation (1))  
\[MaxW=\sum_{t=1}^{T}Q_{g}(t)\Delta t,t=1,\ 2,\ldots,T \tag{1}\]  
where \(W\) is the total water diversion; \(t\) is the index of operational periods; \(\Delta t\) is the duration of each period; \(Q_{g}(t)\) is the water diversion flow during each period \(\Delta t;T\) is the total number of operational periods.  
2. Power generation objective: maximum power generation (Equation (2))  
\[MaxE=\sum_{t=1}^{T}KQ_{f}(t)H(t)\Delta t,t=1,\ 2,\ldots,T \tag{2}\]  
where \(E\) is the total power generation; \(K\) is the power output coefficient; \(Q_{f}(t)\) is the flow passing through the hydraulic turbine during each period; \(H\) (\(t\)) is the power generation head during each period.  
3. Ecology objective: minimum ecological AAPFD value (Equation (3))  
It is generally believed that the river ecosystem is in the best state under the condition of natural inflow. For the river affected by reservoir operation, the degree of ecological damage downstream of the river is usually measured by the difference between outflow of reservoir and natural inflow [41]. At present, the most used method is to calculate the amended annual proportion flow deviation (ecological AAPFD value). The smaller  
Figure 2: Natural runoff processes in different typical years.  
the AAPFD value, the better the river ecological environment [42]. The AAPFD value is calculated as follows in Equation (3):  
\[MinR=\left[\sum_{t=1}^{T}\left(\frac{Q_{t}-Q_{t}^{n}}{\overline{Q_{t}^{n}}} \right)^{2}\right]^{0.5},\;t=1,\;2,\ldots,T \tag{3}\]  
where \(R\) is the ecological AAPFD value; \(Q_{t}\) is the outflow of the reservoir after operation during each period; \(Q_{t}^{n}\) is the natural inflow during each period; \(\overline{Q_{t}^{n}}\) is the average of natural inflow during the operational period.  
The constraints mainly include water balance constraint, pumping flow constraint of pump station, reservoir water level constraint, power output constraint and variable non-negative constraint.
## 2 Materials and Methods
### SpectraViT Model
#### 2.2.2 Transformer Module  
The Transformer model is a sequence modeling method based on self-attention mechanisms [21], initially used for natural language processing tasks. Compared with traditional convolutional neural network (CNN) models, the Transformer module employs a global attention mechanism, which better captures long-distance dependencies within sequences. The Transformer module consists of an encoder and a decoder. The encoder is responsible for transforming the input sequence into a series of high-level feature representations, while the decoder generates the target sequence based on the output of the encoder. This study  
Figure 3: Structure of MV2 module. (**a**) MV2, (**b**) MV2\(\downarrow\).  
Figure 2: Structure of SpectraViT.  
In the Transformer module, the global attention mechanism is a key component. The multi-head attention layer features multiple parallel attention heads, each capable of learning different attention points. This allows the model to simultaneously focus on various aspects of the spectral data, enabling it to pay attention to all positions in the sequence and to better capture contextual information. As shown in Figure 5, the input sequence is first mapped to the spaces of queries (\(Q\)), keys (\(K\)), and values (\(V\)) through linear transformations. Then, attention weights for multiple attention heads are calculated using Formula (1), resulting in weighted values for each head. These weighted values are combined to form the final output representation. Here, Qrepresents the query vector, \(K\) represents the key vector, \(V\) represents the value vector, and \(d_{k}\) represents the dimension of the key vector.  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{1}\]  
Feed-Forward Neural Network  
Another important component of the model is the Feed-Forward Network (FFN), which applies nonlinear transformations to the output of the attention mechanism at each position. FFN maps and transforms the input according to Formula (2), thereby enhancing the model's nonlinear expressiveness. Here, \(W\) and \(W\) are learnable weight matrices, \(b_{1}\) and \(b_{2}\) are bias terms, and SiLU is the nonlinear activation function.  
\[\text{FFN}(x)=\text{SiLU}(xW+b_{1})W^{\prime}+b_{2} \tag{2}\]  
Figure 4: Transformer module.
## 4 DL Models
### Autoencoders (AEs)  
[PERSON] et al. [46] were the first to propose Autoencoders (AEs), a DL technique that is specialized in dimensionality reduction and denoising of data. The key player in an autoencoder operation is the latent (hidden) representation, h, as shown in Figure 4. It plays the role of a bottleneck, which retains only those features that are necessary to reconstruct the input data on the AE output [47]. This latent representation (features) is often used in data classification tasks. This implies that AEs and CNNs have common characteristics and attempt to extract and learn only important features. While CNNs make use of convolutional layers to concentrate the extracted features and improve recognition ability, AEs make use of latent representation (h) to compress the data received from the encoder unit. This step retains salient features and removes irrelevant data. Thus, the operation of AEs includes denoising and reducing the data dimension while extracting features simultaneously. This reduces the computational complexity which in certain classification tasks makes AE models easier to train [48]. However, AEs are also associated with disadvantages, such as poor data compressibility and the inability to train a model effectively for certain tasks. For instance, the latent representation (h) will fail to capture salient features if errors are present in the encoder unit [47]. This is because h cannot get rid of errors; instead, it will compute the average of the input data rather than retain salient features for the decoder unit. The goal of AEs is to reconstruct the input data as shown in Figure 4, hence, the encoder units of AEs have to ensure that there are minimum errors before feeding the input data into the latent representation, h.  
Figure 3: Basic Long Short-Term Memory (LSTM) model architecture.
###### Abstract  
In a megacity, structure response during an earthquake could be increased or decreased due to effects from neighboring structures, through structure-soil-structure interaction (SSSI). In the present study, a series of dynamic geotechnical centrifuge tests are carried out to investigate SSSI effects on responses of structure with various characteristics of mass, height, and natural frequency. Experimental observations are focused on the effects of the distance between two structures, type, and peak acceleration of input excitation. A period lengthening is observed in the soil-foundation-structure interaction (SFSI) effects of all structures. It is monitored that an increment in response of smaller structure and a decrement in response of larger structure, compared to isolated structure, due to SSSI effects. Unfavorable distance reveals that the most significant increment in response of S2 structure occurred at approximately one-fourth of wavelength transmitted from the vibrating adjacent structure. More severe SSSI effects are found under a lower input earthquake acceleration. It is found that both height and mass ratios, between two adjacent structures, are particular parameters on SSSI, resulting in increment or reduction of structure response.  
Keywords:soil-foundation-structure interaction; structure-soil-structure interaction; centrifuge modal; fixed-base frequency ratio; mass ratio +
Footnote †: journal: Applied Sciences
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 5 Validation of the Models and Power Consumption Analysis  
The proposed power consumption models from Section 4 and measured energy consumption for all the separate activity events from Section 3 are used for the validation of models and power consumption analysis for different connection settings.