esa-sceva/satcom-chunk-collection huggingface dataset:
Silver nanoparticles (AgNPs) have been widely used in biomedical studies in recent years due to their wide variety of applications in this field. Some recent studies have shown the strong antimicrobial activity of green synthesized AgNPs against Gram-negative, Gram-positive, and/or antibiotic-resistant bacteria [8; 9; 10].  
To enhance the antimicrobial activity and reduce the toxicity of AgNPs, some studies have focused on making conjugates with biological molecules, such as peptides [11], bacteriophages [12; 13], and endolysins [6; 14].  
Endolysins belong to the enzymatic group of peptidoglycan hydrolases, which break down the peptidoglycan structure. They are produced in bacterial cells infected by bacteriophages at the end of their lytic cycle and accumulate in the cytoplasm of the infected cell while the bacteriophage particles mature [15].  
Studies realized by [PERSON] et al. [16] recorded the interactions of PEGyled dendronized AgNPs with _P. aeruginosa_ LPS in the presence of lysozymes and KP27 endolysin. They found that PEGyled dendronized AgNPs with endolysins overcame the LPS barrier and enhanced the antibacterial effect more efficiently than lysozymes.  
BK510 Lys is an endolysin from the _Staphylococcus aureus_ bacteriophage BK510, isolated from cows diagnosed with mastitistia [17], which has shown high effectiveness against Gram-positive bacteria [18]. However, its efficiency against Gram-negative bacteria is very low. Therefore, the present research aims to conjugate BK510 Lys with AgNPs (Synthesized by a green route with _Lepidium virginicum_ extract as a reducing agent) to increase its efficiency in inhibiting Gram-negative bacteria of veterinary interest.
## 4 In Situ Testing
### Evaluation and Assessment
#### 4.2.2 Identification of Natural Frequencies  
To determine the bending and torsional modes, MP1 and MP2 are considered. Derived from the averaged ambient frequency spectra (FFT) shown in Figure 10, the resulting eigenfrequencies are obtained and documented in Table 2. Higher modes were analysed but are not of interest in the following.  
For a deeper understanding, the representation of the eigenmodes using Artemis is also included in Figure 10. In contrast, OMA reveals the uncertainty of the first bending eigenfrequency, denoted by \(f_{1}\), where it manifests as either 9.4 Hz (as identified in the stable mode by stochastic subspace identification) or 9.9 Hz (as indicated in the Singular Value Decomposition spectrum), depending on the methodology applied.  
As a consequence of the averaging process applied to the windowed frequency spectra in the ambient time signal analysis, nearby peaks are smoothed out, revealing only diffuse plots. The indeterminate nature of the initial bending eigenfrequency (9.4 Hz \(<f_{1}<\) 10.1 Hz) remains apparent in the frequency spectrum resulting from pulse excitation (Figure 11). In this case, the effect of averaging is reduced. Furthermore, the uncertainty in the natural frequency is evident in the continuous wavelet transformation (CWT) [50], where the signal characteristics are combined into a representation that is both frequency- and time-dependent (Figure 11b).  
In this scenario, the initial bending eigenfrequency defies definitive assignment to a single frequency, with the abutments appearing to play a predominant role, as indicated by a slight counter-phase of the abutment on the \"Ingolstadt\" side in the OMA. This can lead to divergent structural stiffening due to a phase shift at about \(\approx\)10 Hz. The stiffening phenomenon due to counter-phase vibrating foundations is discussed in [51].
## 3 Results  
### Data Pre-Processing Results  
This study uses UVE to select sensitive variables for the sample of temperature and humidity variables to assess the reliability of each variable. The number of main components is selected as 10 and the number of sensitive variables is 8; the results of the treatment are shown in Figure 5.  
The horizontal coordinates of Figure 5 indicate the variable ordinal numbers, and the vertical coordinates are the \(E_{i}\) calculated according to the UVE method using Formula (2). The vertical line in the middle of the figure divides the temperature and humidity variable data matrix from the random noise data matrix. Two horizontal dashed lines are the upper and lower thresholds for sensitive variable selection, i.e., \(E_{max}\). UVE eliminates the data points within dashed lines and retains those outside, forming a new matrix \(X_{UVE}\).  
Finally, eight groups of sensitive variables are selected: upper outlet temperature, lower outlet temperature, drying section B temperature, drying section C temperature,  
Figure 5: UVE processing results.  
lower outlet humidity, humidity in drying section A, humidity in drying section B, and humidity in drying section D.
## 4 Results and Discussion
### Semi-Physical Simulation Based on Telemetered Missile Trajectory
Since there is no loss of lock in dynamic simulation, the independent GPS receiver equipped with the new preprocessing model can implement navigation. However, in navigation domain, if KF is applied, the large error contained in the estimated Doppler shift may lead to inaccurate positioning results. Hence the independent GPS receiver still uses LS to obtain navigation results. Figures 14 and 15 respectively show its position and velocity estimation errors in ECEF coordinate system. And Figures 16 and 17 show the INS navigation errors in the same coordinate system. Meanwhile,  
Figure 12: The absolute acceleration of the missile. Figure 13: Doppler shift tracking error for PRN5 and the estimation of \(\delta v_{G,1}\). navigation errors of the ultra-tight GPS/INS integration, namely the difference between the corrected inertial navigation information and reference values are shown in Figures 18 and 19. Obviously, the velocity components estimated by the independent GPS receiver have large errors, which are on the order of 3 m/s, during the missile ascent phase. This is consistent with the carrier tracking result. For the case of INS, there is no sudden change in navigation errors. That is because INS is robust to vehicle dynamics. However, INS has the problem of error accumulation. At 180 s, the position errors are 76.3, \(-\)21.1 and \(-\)60.4 m, while the velocity errors are 0.6, 0.2 and \(-\)0.5 m/s. On the other hand, employing the navigation filter with expanded dimension, the ultra-tight GPS/INS integration can provide more accurate navigation results. The absolute maximum values of position errors and velocity errors are smaller than 0.5 m and 0.1 m/s, respectively. Figure 16: INS position estimation errors. Figure 17: INS velocity estimation errors. As mentioned before, the traditional navigation filter cannot maintain the estimation accuracy of INS errors when the GPS receiver operates under harsh conditions. Once the incorrect navigation information is utilized to generate local carrier frequency, the Doppler shift tracking error will further increase, so it can be concluded that the proposed navigation filter elegantly avoids this vicious circle. Here, a tight GPS/INS integration based on the traditional navigation filter is realized to show the harmful effect of carrier tracking errors in detail. The corresponding navigation errors are shown in Figures 20 and 21. It can be observed that the tight integration gradually achieves acceptable navigation accuracy as time goes on, but during the missile ascent phase, the velocity errors of this integration are on the order of 2 m/s, and are much larger than those of INS. Figure 19: GPS/INS velocity estimation errors. Figure 18: GPS/INS position estimation errors.
## 2 Problem Statement
### Methodology
#### 2.1.2 Modeling Load Demand, Price, and Wind Speed Uncertainties  
The uncertainties in the load demand, electricity price, and wind speed are simulated by using a Monte-Carlo simulation which produces 1000 scenarios of possible probabilities [24]. The normal and Weibull distribution functions are suitable solutions for modeling system objective uncertainties [25].  
Figure 4: PDF of travel distance.  
Figure 3: PDF of departure time.  
Figure 2: Probability distribution function (PDF) of arrival time.  
The input data for the Monte-Carlo simulation is extracted from the PDF created by normal distribution function for the load demand and electricity price. The PDF created by the Weibull distribution function for the wind speed is also extracted for the Monte-Carlo simulation [24]. Normal and Weibull distribution functions are presented in Equations (19) and (20).  
\[f(x|\mu,\sigma^{2})=\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{-\frac{(x-y)^{2}}{2\omega^ {2}}} \tag{19}\]  
\[f(x|\lambda,\kappa)=\left\{\begin{array}{ll}\frac{K}{\lambda}\left(\frac{K}{ \lambda}\right)^{\kappa-1}e^{-(\frac{K}{\lambda})^{\kappa}}&x\geq 0\\ 0&x<0\end{array}\right. \tag{20}\]  
The generated scenarios for load demand, price, and wind are shown in Figures 5-7, respectively. The relationship between wind speed and wind power is derived from [26] which is shown in Equation (21). This function transforms the wind speed to electricity power for a wind turbine.  
\[P_{n_{w},t}^{ind}=\left\{\begin{array}{ll}0&v_{t}<v_{ci},v_{t}>v_{\infty}\\ P_{W\max}&v_{r}<v_{t}<v_{\infty}\\ \frac{P_{W\max}(v_{t}-v_{ci})}{v_{r}-v_{ci}}&v_{ci}<v_{t}<v_{r}\end{array}\right. \tag{21}\]  
where \(P_{n_{w},t}^{ind}\) is the power generated by the wind turbine. In general, generated power equals to zero when the wind speed is lower than the cut-in speed (\(v_{ci}\)) or is greater than the cut-out speed (\(v_{\infty}\)). When the wind speed is between rated and cut-out speed, the turbine produces its nominal power; also if it is between a cut-in and rated speed (\(v_{r}\)), the generated power is calculated by related function that is shown in Equation (21). Table 1 shows the parameters of the distribution functions for the arrival and departure time and travel distance.  
Figure 5: The boxplot of electric vehicles load demand.  
Figure 6: The boxplot of hourly electricity price.
## 3 Analysis of Aging Building Remodeling and Object Recognition Technology
### Analysis of Deep Learning-Based Point Cloud Object Recognition Technology
PointNet, based on the principles shown in Figure 5, was first introduced by [PERSON] et al. in 2017. Subsequently, an improved version called PointNet++ was developed [21]. While the initial version focused on processing single-point clouds, PointNet++ was designed to handle high-resolution point clouds through a hierarchical structure, resulting  
Figure 4: Fully Convolutional Neural Network (FCNN). Adapted with permission from Ref. [15]. 2020, [PERSON] et al. in enhanced performance in capturing spatial hierarchical information. These models are effectively utilized in the classification of 3D objects and the recognition of 3D objects and boundaries [20]. The lack of digital representations impedes the efficient operation and renovation of existing buildings. Digital information in the form of geometric information can be extracted from the shape of buildings and a high degree of surface detail in triangular meshes and point clouds. In this study, we developed a technology that can efficiently be utilized in the remodeling process for numerous buildings without architectural drawings. Specifically, we employed point cloud data obtained through 3D scanning to classify architectural objects using the FCAF3D model, making it applicable for utilization. The bottom-up approach refers to recognizing individual points or small parts first and then combining these small parts to recognize the entire object. It involves extracting features for small parts of 3D points and clustering or grouping points with similar features. By combining these grouped points, it recognizes and segments the entire object. While this approach performs well in finding details, it is challenging to recognize the overall structure, and it has a high computational complexity. On the other hand, the top-down approach involves first recognizing the overall structure or large objects and then decomposing them into detailed parts for recognition. In the case of FCA3D, it classifies objects first and then proceeds with instance segmentation through learning. This method is effective in quickly recognizing large objects, but it may miss detailed details. This research aims at the classification and segmentation of point cloud datasets to increase automation in the accuracy of the classification of architectural objects in the context of the renovation of aging buildings. The scope of architectural objects in this research encompasses point cloud-built objects scanned for renovation.
## 5 Experiment Conclusions
### Experimental Results and Analysis
#### 5.3.2 Connectivity Test  
Figure 3 displays the robustness testing process of the real networks, where the collapse is simulated by successively removing the nodes of utmost importance within the network using the DNC method as well as the eight other node importance methods. In the experimental procedure, node importance values are computed using the DNC method and several other node importance identification methods; nodes are ranked based on these  
Figure 2: Subfigures (**a–d**) represent the correlation matrices between DNC and the baseline methods in the Adjnoun, Celegans, Yeast, and Hamsterster networks. The correlation coefficients between the two methods are mathematically expressed by the corresponding rows and columns. The first row represents the correlation coefficients between DNC and the baseline methods, while the second row represents the correlation coefficients between CI and the remaining methods. The black numbers in the figure indicate that the two methods have low correlation.  
values. After that, the nodes are deleted in declining order of importance. Figure 3 depicts the trends in \(\sigma(i/N)\) for the Dmela, Kohonen, Vote, and Yeast networks, while the trends for the remaining networks are incorporated into Appendix B.  
From the results in Figure 3 and Appendix B, it is obvious that the robustness curve for DNC, with nodes removed in the order determined by the DNC algorithm, is positioned at the bottom, resulting in the smallest area with the x-axis. This suggests that removing nodes via the DNC method hastens network breakdown. Table 4 provides specific robustness values for DNC and the comparative algorithms. In summary, DNC outperforms the baseline methods in terms of accuracy.  
Figure 3: Subfigures (**a**), (**b**), (**c**), and (**d**) respectively represent the performance of DNC and the baseline methods in terms of accuracy on the Dmela, Kohonen, Yeast, and Vote networks. The x-axis of each subfigure represents the sequential removal of nodes according to DNC or the baseline methods. The y-axis measures the degree of network collapse, with a faster descent indicating greater accuracy for the method.
# Role of Bimodal Water Retention Curve on the Unsaturated Shear Strength
## 2 Investigated Soil and Methodology  
### Investigated Soils  
In this experiment, there were two soil mixtures produced by mixing coarse kaolin and Ottawa sand, specifically 70%Sand-30%Kaolin (70S30K) and 50%Sand-50%Kaolin (50S50K). For each composition, specimens were prepared for WRC tests, and for saturated and unsaturated triaxial tests. The soil mixtures were selected to avoid non-homogeneity of the soil specimen in order to ensure the results from different specimens were comparable and could be used to establish the characteristics of soil [33]. The specimens in this study were prepared with water content corresponding to 90% of maximum dry density on the dry side of optimum of the compaction curve. The specimens were then statically compacted at 1 mm/min loading rate with 10 mm thickness per layer as described in [34], to produce homogenous specimens. This procedure was selected to ensure the specimens used in each laboratory testing have similar soil properties. Specifically, specimens of 100 mm in height and 50 mm in diameter were used for multistage consolidated undrained and consolidated drained saturated and unsaturated triaxial tests, correspondingly. Moreover, specimens of 20 mm in height and 50 mm in diameter were used for WRC measurements.
## 4 Conclusions  
This paper introduces a new flexible multi-state adaptive early warning method for mechanical equipment and proposes an adaptive dynamic update model of the equipment alarm threshold based on the similar proportion and state probability model. In the case of early warning of rolling bearings, this method is compared to the traditional fixed threshold method, which verifies the superiority of this method. According to the experimental and analytical results, the following conclusions can be drawn:  
1. Based on the similarity of historical equipment, the initial thresholds of the different health states of online equipment can be determined, which can intuitively reflect the current state of the equipment, provide an early warning of sudden failures, and offer a certain reference guidance for online equipment under the same working conditions.
2. The monitoring data of online equipment change with the interference of external factors such as environment and working conditions, and when the equipment is in the abnormal state, its abnormal degree changes accordingly. In this paper, the equipment status is divided into four categories and analyzed, which can better represent its status and provide more detailed and reasonable guidance.
3. The obtained dynamic alarm lines at all levels can regulate the operation range of the equipment in its different health states, which makes the method proposed in this paper not only attain a high sensitivity by alarming when abnormal or even when a fault occurs in the equipment but can also tolerate the data fluctuation caused by the equipment in the run-in period to a certain extent to prevent false positives.
4. Compared to the traditional method of a fixed threshold, this method can effectively reduce the number of false alarms and realizes a higher prediction accuracy, which demonstrates its effectiveness and superiority.  
The early warning of online equipment from the perspective of the similar probability and proportion model can be applied not only to bearing monitoring but also to the early warning of other online equipment. However, there are still many unsolved problems that need to be addressed in future research, such as how to determine the failure type during maintenance based on early warning of the equipment and how to settle data volatility and intervals in data updating.  
[PERSON], [PERSON], and [PERSON] conceived the methodology. [PERSON] established the model and performed the simulations. [PERSON] and [PERSON] wrote and edited the manuscript. [PERSON], [PERSON], and [PERSON] revised and polished the manuscript. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support of the National Natural Science Foundation of China (No. 51705015), the Fundamental Research Foundation (No. JCKY2018203C005), and the Technical Foundation Program (No. JSZL2019601A003) from the Ministry of Industry and Information Technology of China.  
The authors declare no conflict of interest.
## 3 Numerical Results and Discussion
### 3D-FR-NWT with Radiation Boundary Conditions
The combined radiation boundary condition (SRC and ADZ) was employed to reduce the length of the ADZ, and the corresponding results were compared with those obtained when only the ADZ was applied. When the length of the ADZ was longer than one wavelength, the results of FR-NWT showed good agreement with those of WAMIT, as suggested in previous studies [22, 31, 32]. The heave motion RAOs were compared in Figure 13a by applying the radiation boundary conditions, and their corresponding computational demands were investigated (Figure 13b). The combined boundary condition (both ADZ and SRC) was applied to reduce the length of the damping zone, as described in [23, 24]. When the result using the ADZ alone (\(L_{damp}=0.51\) ) and the results using the combined condition (\(L_{damp}=0.51\) ) were compared, the result using the combined radiation condition was more consistent with WAMIT. As a result, the length of ADZ could be reduced to a half wavelength by applying the combined radiation condition. Figure 3:2 Numerical results of the combined radiation condition: (**a**) nondimensional heave-added mass varying the length of the damping zone (\(w=1.5\) rad/s); (**b**) nondimensional heave radiation damping coefficient varying the length of the damping zone (\(w=1.5\) rad/s). Figure 3:3 Numerical results for various radiation conditions: (**a**) heave RAOs for different radiation conditions; (**b**) calculation time according to the total number of panels. The heavy RAO under the combined radiation condition agreed well with the results of WAMIT, in contrast to the results obtained with only the SRC applied. Figure 13b shows the calculation time for the ADZ only, as well as the combined condition (ADZ and SRC). The computational time was reduced by decreasing the number of meshes in the domain because of the combined boundary condition. Reducing the calculation time by reducing the total number of meshes does not affect the accuracy of the calculation results. It is important to reduce the computational demand to perform the analysis over a wide frequency range. For this, the combined radiation condition was applied to the FR-NWT. Future calculations will be performed using the FR-NWT equipped with the above optimized radiation boundary conditions to simulate a heaving buoy-type WEC-integrated breakwater.