esa-sceva/satcom-chunk-collection huggingface dataset:
# A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
## 2 Dynamic Weighted Protein Network Construction
### Construction of the Adjacency Matrix
#### 2.1.1 Active Period Calculation  
Proteins have their active periods in the cell, and gene expression data can reflect the dynamic information of proteins at time points [21; 22]. Generally, the expression level of a protein will decrease after the protein has completed its function. Therefore, the protein is active at time points when the related gene expression data are at a high level. Gene expression data always contain unavoidable noise. Even though an active threshold is used for each gene, active proteins with low expression values will likely be filtered out. To address this problem, we calculate the active probability of each protein at different time points based on the 3-sigma [23] criterion. Suppose the gene expression value of protein \(u\) at time \(t\) is \(G_{t}(u)\). The mean and standard deviation of gene expression for protein \(u\) are \(\alpha(u)\) and \(\sigma(u)\). The activity threshold for protein \(u\) is calculated through the following expression:  
\[\alpha(u)=\frac{1}{n}\sum_{t=1}^{n}G_{t}(u), \tag{1}\]  
\[\sigma(u)=\sqrt{\frac{\sum_{t=1}^{n}(G_{t}(u)-\alpha(u))^{2}}{n-1}}. \tag{2}\]  
Based on Equations (1) and (2), the function \(F(u)\) is defined to reflect the fluctuation of the gene expression curve for protein \(u\),  
\[F(u)=\frac{1}{1+\sigma^{2}(u)}, \tag{3}\]  
where \(0\leq F(u)\leq 1\). Proteins play different roles and perform multiple functions in biological processes, so their expression value varies across different periods and organizations [24]. Generally, a threshold is used to determine whether a protein is expressed at a time point or condition. A protein is considered to be expressed when its expression value exceeds the given threshold. However, proteins with lower expression values might be filtered out. Therefore, the active probability of a protein is calculated based on the mean and standard deviation of the corresponding gene. The threshold function is defined as follows:  
\[th=\alpha(u)+k\sigma(u)\times(1-F(u)). \tag{4}\]  
Three standard deviations include about 99% of all observations. Since using a threshold of \(k\sigma\), where \(k\sigma\) is less than \(3\sigma\), results in a lower threshold, more gene expression profiles will be retained. The calculation of \(th\) applies to all possible values of \(k\) (\(0\leq k\leq 3\)).  
This paper selects 2.5 as the value of coefficient \(k\)[25]. Multiple protein subnetworks with different time points can be divided by calculating the active period of all proteins.
## 2 Numerical Methods and Target Model
### Target Model and Details on Simulation  
Figure 2 shows the entire computational domain with the related dimensions and the applied boundary conditions. The speed of the train was set as a velocity inlet boundary condition. The atmospheric pressure was used as a pressure outlet boundary condition. The non-reflective boundary condition was also applied for the inlet and outlet regions to capture the aerodynamic noise without non-physical reflection off the boundaries. Pressure far-field boundary conditions were applied on the upper and two side boundary surfaces. The non-slip wall boundary conditions were  
Figure 1: Illustrative slanted Dirac cone separating incompressible and compressible parts in the three-dimensional periodogram: \(U_{0}=300\) km/h and \(c_{0}=343\) m/s.  
applied on the surface of HEMU. Moving wall boundary conditions were applied on the ground and the rails. The detailed boundary conditions with their specified values are listed in Table 2.  
Figure 3 shows the CAD model of the targeted train and its main parts. The targeted train was HEMU-430X (High-speed Electric Multiple Unit 430 km/h experimental), which is under development as a next-generation Korean high-speed train, for which the maximum speed has been recorded as 430 km/h. It consists of eight coaches including double-deck carriages, as shown in Figure 2. However, for simplicity but without losing the main aerodynamic source mechanisms, only four coaches which are a trailer coach (TC), first motor coach (M1), fourth motor coach (M4), and the last motor coach (MC) were considered in the current study. The detailed, full-scale geometry including eight bogies, two pantographs, one wiper, and three intercoach spaces was modeled to capture the essential aerodynamic noise source mechanisms. Figure 4 shows the grid description for the computational domain. The computational mesh was composed of about 310 million cells using fluent meshing. Tetrahedral and hexacore cells were used together with five layers of prismatic elements near the wall surface.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Boundary** & **Setting** & **Remarks** \\ \hline Inlet boundary & Velocity inlet & 83.33 m/s (300 km/h), non-reflecting \\ Outlet boundary & Pressure outlet & 101,325 Pa, non-reflecting \\ Side and upper boundary & Pressure far field & 101,325 Pa, Ma = 0.24 \\ Ground boundary & Moving wall & 83.33 m/s \\ HEMU wall & No-slip wall & \\ Rail wall & Moving wall & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details on boundary setting for the open field case.  
Figure 2: Computational domain for open field case (1H: height of train).
## 6 Results and Discussion
### Comparison with Related Work  
As seen in the comparative table (Table 1), our work offers the smallest dimensional configuration. Also, the proposed work has a four-port MIMO configuration, which is the highest among other mentioned studies. Despite the smaller size and the four-port configuration of our work, an acceptable value isolation of >23 dB is achieved. Refs. [40; 41], which describe a two-port and a three-port configuration, respectively, offer good isolation, but the size is large and the bandwidth is relatively small. Ref. [42] offers the highest gain; however, it uses two dielectric layers, which not only increases the dimensions but is also comparatively difficult to fabricate. The proposed antenna is a simple planar configuration, and it avoids vias or SIWs, which makes it easy to fabricate.  
Figure 15: Surface current for Antenna-1 at 27 GHz. (**a**) Feed-plane (top view). (**b**) Ground plane (top view).
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Loop-Mediated Isothermal Amplification (LAMP)  
Amongst all of the currently available isothermal amplification techniques, only Nucleic Acid Sequence-based Amplification (NASBA) [103], Rolling Circle Amplification (RCA) [104], Transcription Mediated Amplification (TMA) [105], and LAMP have been used in the identification of _Candida_ spp. Nonetheless, to the best of our knowledge, LAMP is the only technique that has been applied to oral samples.  
LAMP is an isothermal one-step amplification method that uses two inner primers (FIP: Forward Inner Primers and BIP: Backward Inner Primer), and two loop primers creating a continuous loop structure during DNA amplification. LAMP uses a Bst DNA polymerase with increased activity, which can produce a high molecular weight DNA fragment within a short time. LAMP's exceptional specificity is due to a set of four primers with six binding sites that must hybridize correctly to the target sequence before DNA biosynthesis occurs. The detection methods include real-time turbidity, fluorescence probes, and others [106]. The use of LAMP to identify relevant fungi and yeasts has been reviewed by [PERSON] and colleagues [107]. LAMP has shown very good results in the identification of _Candida_ spp. in clinical samples [108], dairy products [109], and oral samples [110]. When using oral samples, LAMP was executed by [PERSON] and colleagues but only for the detection of _C. albicans_ and not for non-C. _ albicans_ species.  
The key elements for a good LAMP assay are primer design and concentration. A higher concentration of the loop primers, FIP and BIP, provides a faster amplification and therefore a quicker result.  
Monitoring LAMP amplification can be performed with a water bath/heating block instead of an (expensive) thermocycler. It is real-time, fast, and has a higher amplification efficiency and sensitivity. Naked eye visual amplification monitoring is possible through the turbidity of magnesium pyrophosphate, a by-product of the reaction, color changes by fluorescent intercalating dyes using a UV lamp, and agarose gel analysis revealing patterns that are characterized by a ladder pattern [111]. A list of primers used for the identification of _Candida_ spp. is available in Supplementary material--Table S5.
# Advances in Random Fiber Lasers and Their Sensing Application
## 3 Control of Output Characteristics  
Having experienced vigorous development, RFLs mainly concentrate on a few hot aspects: Output power, wavelength, and line-width. Many researchers have made contributions to optimize the output performance of RFLs.
## 5 Discussion
### Risk Analysis  
A robust optimization method can be effectively used to determine the associated risk from stochastic and fuzzy uncertainties. In this study, two risk recourse actions were adopted to make the model robust, which were used to capture the risks from stochastic and fuzzy uncertainties, respectively. Figure 11 presents the weighted values of the expected deviations from the stochastic uncertainties (\(V\widetilde{T}S=\rho\cdot\sum\limits_{i=1}^{I}\sum\limits_{t=1}^{T}\sum\limits_ {h=1}^{H}p_{th}(V_{ith}+VC_{ij\_th}]\)) and fuzzy uncertainties (\(V\widetilde{T}F=\rho\cdot\sum\limits_{t=1}^{3}VF_{t}\)) under different robustness levels of 0.2, 0.6, 0.8, and 1.0, respectively. \(\rho\) is a goal programming weight, through varying the \(\rho\) level, the decision makers can then control the variability of the recourse cost. Generally, a lower \(\rho\) corresponds to a lower weight value of the expected deviations and system costs, indicating an aggressive attitude of the manager regarding the system costs. However, this might be associated with a higher risk level because of the expected deviations from the uncertainties. On the contrary, a plan with a higher\(\rho\)would better resist a deviation from the uncertainties of the EES. A decision with a higher robust level would thus correspond to a lower risk of system failure and higher system reliability. As results, the weighted values of the expected deviations from the stochastic and fuzzy uncertainties increased with increasing robustness level \(\rho\). For instance, \(V\widetilde{T}S\) would be RMB 5.17 \(\times\) 10\({}^{9}\) when \(\rho\) = 0.2, RMB 15.54 \(\times\) 10\({}^{9}\) when \(\rho\) = 0.6, RMB 20.71 \(\times\) 10\({}^{9}\) when \(\rho\) = 0.8, and 25.89 \(\times\) 10\({}^{9}\) when \(\rho\) = 1. This analysis demonstrates the trade-offs between system costs and reliability. The results enable the manager to plan with a reasonable consideration of both system costs and reliability.  
Figure 11: _Cont._
## 4 Conclusions  
The accurate modeling of the non-uniform deformation of materials is essential to the description and analysis of their complex mechanical behaviors, which can be exploited in digital twins, for example. In this study, a PINN-based method involving the tension testing of an open-holed square plate is proposed to solve solid mechanics problems via the displacement method, importing both forward problems and inverse problems. The following are the main conclusions of this study:  
1. A PINN framework comprising a simple FNN structure and a conventional physics-informed loss function can perform well in non-uniform deformation predictions and capture strain concentration well within the whole computational domain;
2. The two case studies conducted to verify the effectiveness of this approach in identifying unknown physics laws show its promise and power in addressing inverse problems related to computational mechanics;
3. Using the PINN method, the distribution of the unknown parameters of material can be identified via a single uniaxial test though one training session, thus avoiding the complexity of conventional uniaxial tests performed on several specimens.  
Conceptualization, methodology, and writing--original draft, Y.D.; project administration, resources, and writing--original draft, C.C.; investigation and formal analysis, Q.W.; data curation and software, X.L.; funding acquisition and supervision, Z.F.; validation and visualization, Y.L.; writing--review and editing, Y.D., X.L. and Z.F. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Example ID** & **FEM** & **PINN** \\ \hline
1 & 17 s & 14 s \\
2 & 625 s & 63 s \\
3 & 674 s & 66 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inversion times for different examples using the FEM and PINN methods.  
**Funding:** This research was funded by the Science and Technology Committee of the Military Commission (CX2022050201) and the Chinese Academy of Sciences (CXJ)[21S032).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Systematic Mapping Study on Remote Sensing in Agriculture
## 2 Research Methodology
### Study Selection  
The following task after defining the search string is to establish the inclusion and exclusion criteria. Inclusion criteria are the conditions that should be met by the selected papers, whereas the exclusion criteria indicate what candidate papers should be removed from the review. Inclusion criteria (IC) were limited to the search string (IC1), and the papers should be written in English (IC2). On the other hand, the papers that meet one or more of these exclusion criteria (EC) were discarded:  
* **EC1**. Editorial papers, papers about colloquium and international meetings, and summer school papers.
* **EC2**. Papers that have a citation ratio of less than 6 citations per year.  
In EC1, editorial papers, papers about colloquium, international meetings, and summer school papers were not considered as the material provided in these manuscripts may not be of sufficient relevance and novelty. EC2 was based on the idea of selecting the most highly-cited publications. In addition, the impact of the literature on RSA was also kept in mind. For this purpose, a citation ratio with the number of citations divided by the numbers of years was employed. This ratio defines an objective criterion that allows to order the papers according to their relevance in the literature, taking into account that recent papers can have less citations than older papers.  
The PRISMA methodology [87] was followed in the selection of the papers, providing a formal protocol for the accuracy and impartiality in the search of the titles in Scopus. Figure 1 shows the steps occurred during the study selection.
## 2 Materials and Methods
### Microtissue Formation Assay  
Fibronectin-null mouse embryonic fibroblasts (FN-null MEFs) were cultured on tissue culture flasks pre-coated with type I rat tail collagen, using a 1:1 mixture of AimV and SF Medium [28]. These media do not contain fibronectin and do require serum-supplementation. FN-null MEFs do not produce fibronectin endogenously, but can assemble fibronectin and collagen fibrils when provided with an exogenous source of fibronectin, allowing for precise control over the matrix assembly process [31]. Cells were seeded at a density of \(4\times 10^{4}\) cells/cm\({}^{2}\) onto the surface of collagen-fibronectin gels polymerized under ultrasound- or sham-exposure conditions. Cell-seeded collagen gels were incubated at 37 \({}^{\circ}\)C, 8% CO2 for up to 6 days. The formation of microtissues was monitored using phase-contrast microscopy (BX-60 microscope, Olympus, Tokyo, Japan) at 24 h, 3 d, and 6 d post-seeding. Images were obtained using a digital camera (Teledyne Imaging, Tucson, AZ, USA). At each time point, images were collected beginning at the region of the gel corresponding to the center of the ultrasound exposure and moving off-center in non-overlapping steps (1.6 mm).  
Microtissue formation was analyzed within each region of interest (ROI, 2.2 x 1.6 mm) using FIJI software. The number of microtissues was quantified as the number of independent cell clusters consisting of 3 or more cells. If cell clusters were connected to one or more nearby cell clusters via a cellular network, only the largest cluster was counted. As a second measure of cellular self-assembly,the surface area occupied by cells within each ROI was measured by manually tracing the edges of both adherent cell monolayers and 3D microtissues. Individual cells and groups containing fewer than 3 cells were excluded from the measurement.
## 5 Discussion and Conclusions
### Conclusions  
This paper leveraged the trust region reflective algorithm (TRF) to transform the parameter inversion problem of individual time function models into a nonlinear least squares optimization trust region subproblem. In the process of parameter optimization, compared to differential evolution algorithm (DE), genetic algorithm (GA), and particle swarm optimization algorithm (PSO), this algorithm demonstrated robust local search and global optimization capabilities, enabling rapid overall convergence for parameter optimization. Additionally, the time function models obtained using this algorithm exhibited predictive accuracy suitable for general engineering requirements.  
Building upon the conventional practice of fixed-weight combinations, this paper introduced a novel approach by incorporating a sliding window model to convert fixed weights into variable weights, thereby achieving dynamic weighting for combined models. This introduced a new method and perspective for achieving model weighting adaptation.  
Compared to individual time function models, the combined model can compensate for the shortcomings of individual models, resulting in reduced errors and improved accuracy. Through practical engineering applications, the feasibility of implementing the variable-weight combined model based on sliding windows has been validated. This method can provide guidance for dynamic subsidence prediction in other complex situations to some extent.  
Conceptualization, H.C.; methodology, H.C. and H.X.; software, H.X. and J.H.; validation, S.G., P.G. and H.X.; formal analysis, H.X., J.H., Y.D. and Y.Z.; investigation, H.X. and M.X.; resources, H.X. and L.C.; data curation, H.X., Y.Z. and M.X.; writing--original draft preparation, H.X.; writing--review and editing, H.C. and H.X.; visualization, H.X. and L.C.; supervision, S.G., P.G. and Y.D.; project administration, H.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant U22A20620.  
Not applicable.  
Dataset available on request from the authors.  
**Acknowledgments:** We thank all the authors for their support. The authors are also very grateful for the kind suggestions and valuable comments by the anonymous reviewers.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Spiking Neural P Systems for Basic Arithmetic Operations
## 3 Arithmetic Operation in Spiking Neural P Systems  
This part discusses in detail the four arithmetic SNPS designed in this paper. To perform arithmetical operations on SNPS, we input the natural numbers into the system which to be computed and output the computation results. In this paper, we use binary strings to represent natural numbers, and we make the following assumptions:  
1. A unified clock is used to manage and maintain the operation process, with the unit of time being the time slice. The execution of each rule in the SNPS only requires one time slice.
2. The binary strings involved in the operations have k digits. If a string has less than k digits, it is padded with leading zeros to reach k digits.
3. The SNPS can accept one binary digit per time slice. When a binary digit of 1 is received, this means the system has received a spike; otherwise, no spike is received. The system receives the input binary string from the least significant bit to the most significant bit.
4. In the addition, subtraction, and multiplication SNPS the system operation result is outputted by the output neurons from low to high bits in the form of a binary string. In the division SNPS the operation result is stored in a set of result neurons.
5. In our designed SNPS we set parameter \(d\) in the rule description of Formula (1) to 0. This means that both the spiking rule and the forgetting rule will be executed immediately once the conditions are satisfied.  
The four SNP operational models proposed in this paper have all been proven to function correctly. Our proof strategy involves tracking the changes in the number of spikes in key neurons over time slices and providing spike count diagrams for the critical patterns of neurons when necessary. For example, Section 3.1 illustrates the pattern of the adder at \(\mathrm{t}=\mathrm{k}+2\), where the augend has already been input into the augend cache neuron group. Finally, the system provides the computation result for each digit over the time slice.
## 5 Conclusions  
Frost heaving and pressure tests were performed for gravel-sand mixtures with different mixing ratios to examine the frost action at the interfacial zone between the gravel ballast and subgrade. Frost heaving and induced pressure of the mixtures were measured over time in a cold chamber. Pure gravel ballast is considered to have exceptional frost resistance. However, the results of this study revealed that gravel ballast becomes susceptible to frost when finer soils are mixed in it. The specific findings are as follows:  
* For pure gravel ballast, no frost heaving occurs when \(S\) is less than 55%. However, even pure gravel ballast experiences frost heaving as \(S\) increases. The induced pressure is dissipated within the soil network under fully confined conditions.
* The level of frost heaving increases as \(S\) and \(M\) increase. Depending on the mixture matrix, frost shrinkage is sometimes observed during the initial freezing phase. However, the mixture gradually frost-heaved. The initial frost shrinkage disappears as \(S\) increases. The level of frost heaving pressure under fully confined conditions also increases as \(S\) and \(M\) increase.
* The observed level of gravelly soils was lower than that of fine soils in this study compared to data reported previously in the literature. However, the frost potential increased as the proportion of frost-susceptible soil increased when there was sufficient water. Repeated freezing cycles can amplify the frost potential of gravelly soils. Therefore, a proper drainage system and prevention of fine intrusion are required to reduce frost damage in gravel ballast, particularly at the interfacial zone.  
Figure 9: Frost heaving ratio for diverse soil types with respect to degree of saturation.