esa-sceva/satcom-chunk-collection huggingface dataset:
# A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
## 3 Experiments and Results
### Performance Comparison
#### 3.3.2 Robustness Analysis  
PPI data in the real world often include numerous false positives (interactions that are detected but not present) and false negatives (actual interactions but are undetected). These inaccuracies challenge the effectiveness of algorithms meant to recognize protein complexes. To evaluate our network construction method's reliability, we conducted further analysis by comparing the performance of different complex recognition algorithms when confronted with these incorrect data. Taking the BioGRID dataset as an example, we choose two complex identification algorithms, ClusterONE and Core, and simulate false-positive and false-negative situations in protein networks by randomly adding or removing a certain percentage of edges.  
Figure 5 shows that based on the initial protein network, multiple networks with different degrees of false positives are constructed by randomly increasing the proportion of edges, and the performance of the complex recognition algorithms in ClusterONE and MCL on these networks is examined. No matter which algorithm is used for complex recognition, the performance of our method tends to be stable after the proportion of increasing edges reaches 30%. Specifically, the F1-measure values of the combined ClusterONE and MCL algorithms are 0.6038 and 0.4410 when the proportion of added edges reaches 30%, while the corresponding F1-measure values are 0.5901 and 0.4223 when the proportion of added edges reaches 50%. Figure 6 presents the experimental results obtained by randomly deleting the proportion of edges in the network, thus obtaining multiple protein networks with varying degrees of false negatives. The performance of all the recognition algorithms decreases dramatically as the false negatives increase in the network, mainly because the more edges are removed from the network, the more real interactions between the proteins may be disrupted, leading to the loss of complexes. Overall, the F1-measure values of DWPNMLE+ClusterONE and DWPNMLE+MCL are consistently better than those of the other methods, even when the false-negative data increase by up to 50%, fully reflecting the reliability of the protein networks constructed by DWPNMLE.  
Figure 4: F1-measure of different methods on the BioGRID dataset.  
Figure 5: False positive analysis of different recognition methods.  
By simulating these uncertainties, one can assess the robustness and accuracy of the protein network construction method, even when the input data are noisy or incomplete. This provides valuable insights into the practical applicability of the method and its resilience to errors in real experimental data.
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
## 3 Design Framework
### Diode Selection  
Diodes are a vital component in a rectifier circuit. For this circuit, we used the HSMS-286C diodes [45]. This group of diodes has been designed and optimized to work at frequencies between 915 MHz and 5.8 GHz. According to the manufacturer, these diodes are ideal for RF-to-DC conversion or voltage doubling. We achieved a high conversion efficiency thanks to their characteristics, which are the following: barrier capacitance \(C_{\text{J0}}=0.18\) pF, series resistance \(R_{S}=6\,\Omega\), breakdown voltage \(B_{V}=7\) V, typical capacitance \(C_{\text{T}}=0.25\) pF, and \(V_{F}=250\) mV\(-350\) mV [58].
# Understanding How Image Quality Affects Transformer Neural Networks
## 3 Experimental Setup  
In this section, we describe the experimental setup used to evaluate the noise sensitivity of notable transformer models trained on the ImageNet dataset. First, we provide an overview of the transformer architectures considered in our study. Second, we outline the details of the used database sampled from the ImageNet dataset, including its size, content, and class distribution. Third, we discuss the types of image distortions that were applied to assess the robustness of the transformer models under various noise conditions. This comprehensive setup enables us to systematically evaluate the performance of transformer models across different noise types and levels, providing valuable insights into their noise sensitivity and robustness.  
To evaluate the performance of the considered models under different noise conditions, we systematically apply common artificial distortions, i.e., additive Gaussian noise, multiplicative Gaussian noise, blur, salt-and-pepper noise, and JPEG compression, to the images and measure both top-1 and top-5 accuracies.
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 4 KF-Based Two-Stage Carrier Tracking Loop  
The conventional two-stage tracking architecture is not robust under harsh environments, especially under weak signal conditions. To improve the tracking sensitivity and achieve fast convergence, a KF-based coarse-to-fine carrier tracking strategy is proposed. The proposed two-stage KF-based carrier tracking loop is analyzed in this section.
## 6 Simulation Results
### Algorithm Comparison Experiment
In relatively straightforward scenarios (such as scenarios 1, 3, 5, and 7), ASDR values are nearly equal to 0, signifying nearly identical channel capacity among devices. Thus, MOEA/D-MFAR demonstrates superior performance in minimizing channel rate differences among devices within similar IoT devices. In summary, the superiority and robustness of MOEA/D-MFAR have been unequivocally demonstrated, surpassing the comparison algorithms MOEA/D, NSGA-II, and MOPSO. This achievement can be attributed to several key factors that set MOEA/D-MFAR apart from the comparison algorithms. MOEA/D-MFAR is distinguished by its incorporation of equitable resource allocation knowledge, a feature not present in conventional multi-objective optimization methodologies. This algorithmic approach allocates resources across different classes, followed by a nuanced distribution within each class, thereby enhancing the directness of the search process. Consequently, MOEA/D-MFAR achieves a higher efficiency in identifying feasible solutions compared to general multi-objective optimization algorithms. Furthermore, the decay rate mechanism within MOEA/D-MFAR is pivotal in mitigating oscillations around the optimal solution during the latter stages of the optimization process. This mechanism accelerates the convergence rate and facilitates a more refined resource allocation. As a result, it minimizes the variance in channel capacity among devices of the same category, thereby promoting a more nuanced and equitable distribution of resources across the system. While MOEA/D-MFAR demonstrates significant advantages, it does have limitations. It necessitates the specification of numerous parameters, including mutation probabilities \((PB_{num},PB_{bm},PB_{cm})\) and scaling coefficients \((c_{1}\sim c_{4})\), in addition to those required by the MOEA/D framework. Moreover, its application is primarily tailored to scenarios that emphasize fairness in resource allocation, which may restrict its generalizability to other optimization contexts.
## 4 Conclusions  
This study calculated the IFT between injected gas and crude oil using an MD simulation. The calculated IFT of the binary system of CH\({}_{4}\) + n-decane was in good agreement with the experimental data. The results indicate that MD simulation has good feasibility and accuracy in predicting IFT and is an alternative to experimental methods. The effects of pressure, temperature, gas composition, and crude oil composition on IFT are investigated, and the results show that:  
1. With rising pressure and falling temperature, there will be more CH\({}_{4}\) enrichment at the interface. It is worth noting that after adding CO\({}_{2}\) to CH\({}_{4}\), the accumulation of CO\({}_{2}\) at the interface is greater than that of CH\({}_{4}\).
2. Under low pressure, the IFT of the CH\({}_{4}\)+n-decane system decreases dramatically as temperature rises. The IFT remains unchanged as the temperature rises when pressure is high.
3. The addition of CO\({}_{2}\) can lower the IFT of the CH\({}_{4}\)+n-decane system. The IFT is affected in the opposite way by the addition of n-hexane and n-nonadecane, with the former having a lower IFT and the latter having a greater IFT.
4. By studying the diffusion properties, it was discovered that CH\({}_{4}\) can weak the interaction between n-decane, thereby increasing its diffusion ability. One significant finding is that the interaction between n-decane and CO\({}_{2}\) is significantly more potent than that of CH\({}_{4}\). Simultaneously, systems containing lighter components are more easily aggregated around CH\({}_{4}\) and dissolved in CH\({}_{4}\).  
Conceptualization, Z.D.; Methodology, X.M.; Software, X.M.; Validation, H.X.; Investigation, S.Q.; Data curation, X.M.; Writing--original draft, X.M.; Writing--review & editing, Z.D.; Visualization, Z.L.; Supervision, W.L.; Project administration, Z.W. and G.L. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the Xi'an Shiyou University Postgraduate Innovation and Practice Training Program (Project No. YCS21213159).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 13: Radial distribution function of different component systems at 313.3 K, 5.2 MPa. (**a**) CH\({}_{4}\)-n-hexane, CH\({}_{4}\)-n-decane, CH\({}_{4}\)-n-nonadecane; (**b**) n-decane-n-hexane, n-decane-n-nonadecane.
# Nanostructured Biosilica of Diatoms: From Water World to Biomedical Applications
## 4 Diatoms as Surface-Enhanced Raman Scattering (SERS) Platforms for Label-Free Diagnosis
The high Langmuir adsorption properties of diatom frustules can effectively enhance analyte mass transport into the sensing area [78]. [PERSON] et al. [79] demonstrated the efficiency of a microfluidic channel with photonic crystal-enhanced plasmonic mesocapsules. Mesocapsules were produced by isolating diatom frustules from _Pinnularia_ sp and synthesizing AgNPs in situ. The authors presented ultrasensitive optofluidic sensing of rhodamine-6G (R6G) with a detection limit of \(10^{-13}\) M inside a microfluidic channel. Porous diatom frustules decorated with AgNPs generated periodically distributed hotspots and, at the same time, created a large surface-to-volume ratio for effective analyte capture. For the detection of analytes, the fluids containing various concentrations of target molecules were injected together with the mesocapsules through the microfluidic channel. Photonic crystal-enhanced plasmonic mesocapsules showed an enhancement factor 100-fold higher than regular colloidal AgNPs and a 1000-fold reduced detection limit. The reported results show how the combination of diatoms frustule and metal nanoparticles can be a valid strategy for the fabrication of new SERS substrates. However, it is still unclear how the species-specific variability of diatoms (in particular their morphology and size) can influence the properties of the substrates, such as the enhancement factor or sensitivity. Therefore, to promote the employment of diatoms in the fabrication of SERS platforms, it could be useful to investigate how the diatom species-specific pattern affects the sensing surface.
## 4 Conclusions  
This study focused on developing a new approach using CT analysis data to determine the functional diameter and internal microscale surface features of cylindrical channels fabricated by L-PBF to link both properties to the flow functionality of the component. The methods developed to analyse the CT data using fitted Gaussian and cubic spline algorithms were compared. They were found to agree in assessing the \(d_{eq}\), \(d_{mean}\) and \(msth\) of internal channels with or without inclination angles.  
The functional equivalent diameter obtained from the CT data was further verified by a comparison with the calculated hydraulic diameter (\(d_{h}\)) from experimental flow tests, showing deviations of less than 4% and 2.5% for specimens N\({}_{1}\) and N\({}_{2}\), respectively. The CT data analysis also verified that \(msth\) could be introduced as a surface irregularity parameter for channels and is dependent on the manufacturing process and the material used for fabrication rather than the geometrical dimensions.  
The method of substituting \(msth\) for hydrodynamic sand grain roughness (\(k_{s}\)) in circular channels to determine the experimental \(d_{h}\) was verified with the aid of the developed mathematical model and was proven to be consistent at all mass flow rates in the turbulent flow regime for incompressible flow, thereby avoiding the necessity to develop fluid flow functional models at different \(\dot{m}\).  
The results presented in this work are the first proof of concept to correlate the geometrical characterisation to its functional characteristics based on incompressible fluid flow experiments. Future work will include the CT analysis of an entire component with internal channels and study the effect of internal channel surface irregularities by considering the compressibility effects.  
Conceptualization, L.C., M.B. and S.C.; Methodology, L.C. and M.B.; Software, L.C. and M.B.; Validation, L.C. and M.B.; Formal analysis, L.C. and M.B.; Investigation, L.C. and M.B.; Resources, R.M., M.P. and S.C.; Data curation, L.C. and M.B.; Writing--original draft preparation, L.C.; Writing--review and editing, M.B., R.M., M.P., L.A., E.D.P. and S.C.; Supervision, L.A., E.D.P. and S.C.; Project administration, L.A. and E.D.P. All authors have read and agreed to the published version of the manuscript.  
This work was conducted within PAM\({}^{2}\), 'Precision Additive Metal Manufacturing', a research project funded by The EU Framework Programme for Research and Innovation--Horizon 2020--Grant Agreement No. 721383.  
The authors declare no conflict of interest.
## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Model for Prediction
#### 4.4.2 SVR  
SVR is a versatile supervised learning approach for regression. Its decision boundary is manifested as the maximal margin hyperplane derived from the learning samples. By incorporating kernel functions, SVR maps the input space into a higher-dimensional feature space, where it seeks an optimal hyperplane for classification or regression prediction. Given a dataset \(x,y\), where \(x\in\{x_{1},x_{2},\cdots,x_{n}\},y\in\{y_{1},y_{2},\cdots,y_{n}\}\), \(x\) and \(y\) represent the input and output spaces, respectively, with n being the number of training data points, SVR formulates the best-fit function as follows:  
\[f(x)=w^{T}\varphi(x)+b \tag{3}\]  
where \(x\) represents the transformed matrix. \(W\) and \(b\) are estimated by solving an optimization problem, which is defined as follows:  
\[\underset{W,b,\xi_{i}^{*},\xi_{i}}{min}\left\{\frac{1}{2}\|x\|^{2}+C\sum_{i=1 }^{n}\xi_{i}^{*}+\xi_{i}\right\} \tag{4}\]  
Subject to  
\[\left\{\begin{array}{l}\left(y_{i}-W^{T}\varphi(x_{i})-b\right)\leq\varepsilon +\xi_{i}\\ \left(W^{T}\varphi(x_{i})+b-y_{i}\right)\leq\varepsilon+\xi_{i}^{*}\\ \xi_{i}\geq 0,\xi_{i}^{**}\geq 0,i=1,\ldots,n\end{array}\right. \tag{5}\]  
where, \(C\) is the regularization parameter, \(\xi_{i}^{**}\) and \(\xi_{i}\) are slack variables that allow flexibility in the constraints.  
SVR excels in handling nonlinear, high-dimensional, and small sample data, making it well-suited for complex and data-driven tasks such as building energy consumption predictions. The performance of SVR is largely determined by the choice of kernel function. Commonly used kernel functions include the linear kernel, polynomial kernel, and radial basis function (RBF) kernel, among others. In building energy consumption prediction, the RBF kernel is frequently selected due to its excellent nonlinear mapping capabilities [158; 159]. SVR is capable of addressing the nonlinear relationships between input and output variables, rendering it suitable for complex problems like building energy consumption prediction [160]. By selecting an appropriate kernel function and tuning parameters, SVR models can achieve high prediction accuracy. During the training process, SVR models seek the optimal hyperplane by maximizing the margin, a strategy that not only enhances the model's fitting ability on training data but also imparts robust generalization performance on unseen data, ensuring the stability and consistency of prediction results in practical applications.
## 7 Wave Propagation Sideway from a Tunnel Line by 2D-FEBEM Calculation  
The excitation of waves by a tunnel line has been further analysed for different stiffnesses of the tunnel and the soil by the two-dimensional finite-element boundary-element method. A 9 m wide and 6 m high tunnel and the amplitudes on a horizontal propagation path in the interior of the soil are calculated for different thicknesses of the tunnel invert and walls and for different wave velocities of the soil. The Figure 14a,b shows the frequency-dependent reduction ratio of the tunnel excitation compared to a line load. As before, the reduction becomes stronger with increasing frequency, and minimum ratios of 0.1 are reached for stiff tunnels and soft soils. The reduction depends on the number of waves that are averaged over the effective width of the tunnel. This effective width can be somewhat increased by a stiffer tunnel invert. The stronger effect stems from the shorter wavelengths of the softer soil. The high-frequency reduction ratios can be 0.1 for a soft soil of 150 m/s, but almost no reduction can be found for a stiff soil of 500 m/s. (Note that the ratios greater than 1 are due to the interference pattern of the reference system. There is no soil-structure resonance effect for the tunnel.).  
The wave propagation from an interior load has also been investigated by 3-dimensional calculations in wavenumber domain showing an overall reduction ratio of 0.5 compared to a surface load [47].  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Span Length** & \(f_{1}\) & \(f_{2}\) & \(f_{3}\) & \(f_{4}\) \\ \hline
15 m & 6 Hz & 38 Hz & 56 Hz & 100 Hz \\ \hline
10 m & 12 Hz & 50 Hz & 90 Hz & \\ \hline
5 m & 28 Hz & 72 Hz & & \\ \hline \end{tabular}
\end{table}
Table 2: Observed resonance frequencies of the bridge-soil system.  
Figure 14: The frequency-dependent reduction effect of the tunnel excitation compared with a line load: (**a**) with variation in the thickness of the tunnel wall \(\square\) 0.3, \(\Circle\) 0.5, \(\triangle\) 0.7, + 1.0 m; (**b**) and variation in the wave velocity of the soil \(v_{S}\) = \(\square\) 150, \(\Circle\) 200, \(\triangle\) 300, + 500 m/s.
## 1 Introduction
### Normative Documents  
In civil aviation, the requirements and recommendations of international aviation organizations, such as EASA (European Union Aviation Safety Agency), ICAO (International Civil Aviation Organization) and FAA (Federal Aviation Administration), are first applied. In addition, in the national conditions, the regulations established by the Civil Aviation Authority (CAA) in this respect apply. In general, the documents, regulations, recommendations for the safe operation of the airport pavement in civil aviation includes among others:  
* Annex to the Decision of the Executive Director of EASA No. 2017/021/R of December 8, 2017 implementing the fourth edition of Certification Specifications (CS) and Guideline Materials (GM) for Airport Design CS-ADR-DSN [28], 2017.
* ICAO Annex 14 to the Convention on International Civil Aviation, Airports Volume I--Aerodrome Design and Operation [29], 6 th edition, 2013.
* Doc. 9157 ICAO AN/901 Aerodrome Design Manual Part 1--Runways [30], ICAO Third Edition, 2006.  
* Doc. 9137 ICAO AN/898 Airport Service Manual Part 2--Pavement Surface Conditions [31], ICAO, Fourth Edition, 2002.
* Advisory Circular no: 150/5320-12C, Measurement, Construction, and Maintenance of Skid Resistant Airport Pavement Surfaces, US Department of Transportation, Federal Aviation Administration [32], (FAA) 1997 as amended.
* Guidelines No. 2 of the President of the Civil Aviation Authority of 25 January 2016 on the methods of assessing, measuring, and reporting the condition of the runway surface [33], 2016.
* ASTM D5340-12 Standard Test Method for Airport Pavement Condition Index Surveys [14], 2018.
* ASTM D6433-18 Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys [15], 2018.  
Similar to the known methods of assessing the technical condition of artificial airfield pavements, the authors developed an innovative model for assessing the technical condition of natural airfield pavements. The adopted model aims to support airport infrastructure managers by providing up-to-date and reliable data.
## 2 Setting the Stage: Fundamental Mathematics and Knowledge
### Computation of the PM Process with the NAF Algorithm
#### 2.3.2 Scalar Conversion to NAF  
The scalar \(k\) is translated into its NAF representation in this stage. This conversion entails a series of iterations. The algorithm determines, at each transition, whether the value of \(n\), which was previously defined as a scalar \(k\), is divisible by two. If there is a NAF representation, a binary \"0\" is simply appended to it to reduce \(n\) by half. If \(n\) is odd, the algorithm updates \(n\) and adds a residual (\"1\") to the NAF. The algorithm, however, skips this step if \(n\) is even. The scalar \(k\) undergoes an iterative procedure until it hits zero, leading to its NAF representation in the end.