esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision Support Model for Allocating Maintenance Budgets for Bridges
###### Abstract  
This paper introduces a newly developed decision support model for allocating a budget for bridge maintenance. The model is built based on capturing the experts' knowledge for the prioritization of criteria for selecting a bridge maintenance plan. A literature review and interviews with a group of nine local experts were carried out to identify the criteria for bridge prioritization for maintenance. A questionnaire survey was then designed and administered, utilizing multi-criteria decision-making techniques through face-to-face meetings with local bridge maintenance managers to determine the weights of the identified criteria and develop the utility curves for each criterion. The developed model consists of four major modules: (1) bridge ranking module; (2) prioritization module of bridge structural elements; (3) optimization-based simulation module; and (4) the outputs module. The model was validated in a hypothetical network of four bridges. The validation reveals that the model can assist bridge maintenance managers in setting a bridge maintenance plan, with a high level of confidence using efficient frontier analysis technique. Utilizing frontier analysis enables comparisons of mean costs of different bridge maintenance plans against different levels of risk to enable decision-makers to make informed decisions. The result of this study indicated that bridge structural condition was the most important criterion amongst all the criteria influencing the decision of bridge prioritization for maintenance, followed by traffic and bridge location. Moreover, bridge deck and parquet, followed by bearing pads, were found to be the most important of the bridge's structural elements.  
budget allocation; bridge maintenance; simulation optimization; budget; uncertainty Article
## 3 Methods
### KDDPG: Local Path Planning Method Based on Kinematic Analysis
#### 3.2.2 Reward Function Setup  
Combining the kinematic model and environmental perception of the robot in Section 3.2.1, this paper upgrades DDPG's reward function \(r\) as follows; its composition is shown in Figure 3.  
\[r=\omega_{goal}\cdot r_{goal}+\omega_{obs}\cdot r_{obs}+\omega_{smooth}\cdot r _{smooth}+\omega_{lv}\cdot r_{lv} \tag{11}\]  
where \(r_{goal}\), \(r_{obs}\), \(r_{smooth}\), and \(r_{lv}\) denote reward consideration on the robot's goal approach, obstacle avoidance, path smoothness, and navigation efficiency, respectively, and \(\omega_{goal}\), \(\omega_{obs}\), \(\omega_{smooth}\), and \(\omega_{lv}\) are the corresponding weights.  
Reward \(r_{goal}\) is defined based on \(F_{goal,t}\) (Equation (10)), such as,  
\[r_{goal}=\left\{\begin{array}{ll}r_{\text{arrival}}&\text{if }d_{goal}<d_{gmin}\\ F_{goal,t}&\text{otherwise}\end{array}\right. \tag{12}\]  
where \(d_{goal}\) is the distance from the robot to the goal, and \(d_{gmin}\) is the distance threshold. When \(d_{goal}\) is less than \(d_{gmin}\), a significant positive reward \(r_{arrival}\) is provided.  
Rewards \(r_{obs}\) are defined based on \(F_{obs,t}\) (Equation (10)), such as,  
\[r_{obs}=\left\{\begin{array}{ll}r_{collision}&\text{if }d_{ro}<d_{collision}\\ F_{obs,t}&\text{otherwise}\end{array}\right. \tag{13}\]  
where \(d_{collision}\) is the collision threshold determined by the robot's actual dimensions. A collision occurs if the minimum scanning distance \(d_{ro}\) is less than \(d_{collision}\), for which \(r_{obs}\) retrieves a significant negative reward \(r_{collision}\).  
Reward \(r_{smooth}\) encourages the robot to pursue a smooth path, avoiding excessive sharp turns. It denotes the changes in the robot's linear velocity \(\Delta v\) and angular velocity \(\Delta\omega\), such as,  
\[r_{smooth}=-\left(\alpha\cdot|\Delta v|+\beta\cdot|\Delta\omega|\right) \tag{14}\]  
Figure 3: KDDPG Reward Function.  
where \(\alpha\) and \(\beta\) are weight factors that balance the influence of changes in linear and angular velocities, respectively. This reward penalizes significant velocity changes and hence rewards smoother paths.  
Reward \(r_{lp}\) encourages the robot to maintain a stable speed, thereby improving navigation efficiency, such as,  
\[r_{lp}=\left\{\begin{array}{ll}r_{lp}&\text{if }l_{p}<l_{vmin}\\ 0&\text{otherwise}\end{array}\right. \tag{15}\]  
where \(l_{vmin}\) is a predefined speed threshold. DDPG will receive a negative reward \(r_{lp}\) when the linear velocity \(v\) is smaller than \(l_{vmin}\).
# Silver Nanowires: Synthesis, Antibacterialativity and Biomedical Applications
## 1 Introduction  
For several decades, silver has been used for its demonstrated antibacterial activity [1] and low toxicity to human cells [2], with successful applications in prophylactic burn treatment and water disinfection [3; 4]. With the rising presence of antibiotic-resistant bacteria [5; 6], and the increased development of nanoscale technology, scientific focus has shifted toward silver nanospecies, due to their unique electrical, optical, chemical and physical properties and their potential in a variety of fields from toothpastes to electrodes [7], including potential future biomedical applications [8; 9]. Silver nanospecies have been synthesized via a variety of different methods, resulting in the production of different configurations, each possessing a discrete extent of antibacterial activity. Commonly synthesized silver nanospecies include: silver nanowires, silver nanorods, silver nanocubes and silver nanospheres [10; 11; 12], as well as more recent silver nanocoils [13]. Due to their greater surface area to volume ratio, silver nanospecies offer enhanced characteristics compared to bulk silver [14; 15; 16], and may therefore serve as an additional weapon in our current antibacterial arsenal. To date, these silver nanospecies have been shown to be effective against bacteria such as _Escherichia coli_[17], _Staphylococcus aureus_[17], _Klebsiella pneumoniae_[18] and methicillin-resistant _Staphylococcus aureus_[19], among others.  
Of these silver nanospecies, silver nanowires (AgNWs) are currently garnering major research interest. Due to their one-dimensional structure, they have a higher aspect ratio compared to other silver nanospecies, and therefore very different physicochemical properties. This review will consider the different synthesis methods used to generate silver nanowires of different lengths and diameters; address the proposed mechanisms of how AgNPs elicit powerful biocidal effects and how their toxicity potential is mitigated compared to other silver species; and finally, provide an up-to-date overview of AgNW-based applications in biomedicine.
# Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
## 3 Results
### PPP AR with the Augmentation of Multi-Frequency LEO Observation
#### 3.2.2 PPP AR with the Triple-Frequency LEO Observation  
To evaluate the benefits of triple-frequency LEO observations for PPP rapid ambiguity resolution, triple-frequency PPP fixed solutions with the augmentation of 288 LEO-constellation were conducted. Figure 9 shows the PPP fixed solutions with the augmentation of the dual- and triple-frequency LEO observations within 5 min at stations CEBR, ASCG, and DYNG. As expected, the ambiguities were fixed within 1 min for dual-frequency PPP AR and the positioning accuracy was significantly improved once the ambiguities were fixed to the correct integers. The triple-frequency PPP AR can converge to a sub-meter level instantaneously, which presents a faster convergence and higher positioning accuracy compared to the dual-frequency fixed solutions.  
The average TTFF values of dual-frequency and triple-frequency fixed solutions were calculated and plotted in Figure 10. It can be seen that, with the augmentation of 288-LEO constellation, the time to first fix of each user station is less than 60 s. After the inclusion of the third frequency of LEO, it only requires 33 s to achieve the first fix.  
Figure 8: Positioning accuracy of static GREC PPP fixed solutions with different session lengths (1 min, 5 min, 10 min, and 30 min) with the augmentation of 0, 60, 192, and 288 LEO satellites.  
Figure 9: PPP fixed solutions with the augmentation of the dual- and triple-frequency observations of the 288-LEO constellation.
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 6 Conclusions  
In this paper, we have proposed a privacy-preserving protocol for utility-based routing PPUR in DTNs. PPUR can protect node privacy information and collect encounter record information to calculate the real utility value at the same time. In addition, the confidentiality and integrity of messages are ensured through hashing and digital signature. We have also verified the effectiveness of PPUR through extensive simulations. Now, this work just focuses on the privacy-preserving problem for utility-based routing without considering the selfish problem. So, our future direction is to explore a privacy-preserving incentive-aware DTN routing protocol that can stimulate nodes to cooperatively forward messages and protect node privacy at the same time.  
Conceptualization, methodology, Q.J.; writing--original draft preparation, K.D.; writing--review and editing, L.Z.; software, C.L.  
This research was funded by the Humanity and Social Science Youth Foundation of Ministry of Education of China (grant numbers 18 YJCZH068 and 17 YJCZH033), The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (grant number 18 KJB520002), Research Start-up Fund Project of Changshu Institute of Technology (grant number KYZ2018005Q), and the Zhejiang Provincial Education Department Research Foundation of China (grant number Y201636127).  
The authors declare no conflict of interest regarding the publication of this paper.
## 4 Results and Analysis
### Numerical Example
After analysis, we found that the different improvements in the detection rate of faults was related to the characteristics of the actual occurrence of the incipient faults. When the occurrence of the incipient fault is in the detection sensitive region of a fixed PV obtained by the PCA method, the method of PCA + KLD2 can obtain good results. For example, the incipient fault \(f_{1}\) is both located in the sensitive region of the second and the third PV. Consequently, the detection rate of the PCA + KLD2 method was more than 98% for the incipient fault \(f_{1}\). However, when the incipient fault is located in regions where the existing PVs are insensitive (e.g., the incipient fault \(f_{3}\)), the detection rate of the proposed method is improved by 60% compared with the traditional PCA + KLD2 method which uses fixed PVs. Combining the results of detecting the above three incipient faults, with the given significance level \(\alpha\), the PCA + KLD2 method is only sensitive to a part of the fault space. In contrast, the method proposed in this paper covers more of the fault space by finding the optimum PV for detecting incipient faults detection and is more adaptable to various possible incipient faults than the fixed PV fault detection method because it takes into account the characteristics of the actual incipient faults. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Faults**} & \multicolumn{2}{c}{**PCA+\(T^{2}\)**} & \multicolumn{2}{c}{**PCA + SPE**} & \multicolumn{2}{c}{**PCA + KLD1**} & \multicolumn{2}{c}{**PCA + KLD2**} & \multicolumn{2}{c}{**Proposed Method**} \\ \cline{2-11}  & **FDR** & **FAR** & **FDR** & **FAR** & **FDR** & **FAR** & **FDR** & **FAR** & **FDR** & **FAR** \\ \hline \(f_{1}\) & 1.09\% & 1.01\% & 1.20\% & 1.01\% & 79.63\% & 4.87\% & 98.04\% & 9.50\% & **98.11\%** & 8.10\% \\ \(f_{2}\) & 1.45\% & 0.98\% & 2.13\% & 1.02\% & 13.83\% & 4.63\% & 62.92\% & 9.01\% & **84.77\%** & 7.48\% \\ \(f_{3}\) & 1.04\% & 0.99\% & 1.02\% & 1.01\% & 4.38\% & 3.70\% & 35.17\% & 9.20\% & **98.33\%** & 8.05\% \\ Average value & 1.19\% & 1.00\% & 1.45\% & 1.01\% & 32.61\% & 4.73\% & 65.38\% & 9.24\% & **93.74\%** & 7.88\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the performance of five different methods at detecting the three faults. Figure 9: The detection result of the proposed method for the incipient fault \(f_{3}\).
# Fault-Tolerant Topology of Agricultural Wireless Sensor Networks Based on a Double Price Function
## 4 Hybrid WSN Topology
### Topology Initialization between Clusters Based on Potential Game
\begin{array}{rl}\Delta u_{2i}&=u_{2i}(p_{i},p_{-i})-u_{2i}(q_{i},p_{-i})\\ &=f(p_{i},p_{-i})(\alpha p_{i}^{\max}\frac{T_{i}^{\max}}{T_{i}^{\min}})-\alpha p _{i}\frac{T_{i}^{\max}}{T_{i}^{\min}}\\ &-f(q_{i},p_{-i})(\alpha p_{i}^{\max}\frac{T_{i}^{\max}}{T_{i}^{\min}})-\alpha q _{i}\frac{T_{i}^{\max}}{T_{i}^{\min}}\\ \Delta V_{2}&=V_{2}(p_{i},p_{-i})-V_{2}(q_{i},p_{-i})\\ &=\Delta u_{2i}+\sum\limits_{j\in V_{j}\
eq i}\left(f(p_{i},p_{-i})(\alpha p_ {j}^{\max}\frac{T_{j}^{\max}}{T_{j}^{\min}})-\alpha p_{j}\frac{T_{j}^{\max}}{T _{j}^{\min}}\right)\\ &-\sum\limits_{j\in V_{j}\
eq i}\left(f(q_{i},p_{-i})(\alpha p_{j}^{\max}\frac {T_{j}^{\max}}{T_{j}^{\min}})-\alpha p_{j}\frac{T_{j}^{\max}}{T_{j}^{\min}}\right) \\ \Delta V_{2}&=V_{2}(p_{i},p_{-i})-V_{2}(q_{i},p_{-i})\\ &=\left\{\begin{array}{cc}\Delta u_{2i}&f_{i}(p_{i},p_{-i})=f_{i}(q_{i},p_{- i})\\ \Delta u_{2}+\sum\limits_{j\in V_{j}\
eq i}1&f_{i}(p_{i},p_{-i})\
eq f_{i}(q_{i},p_{- i})\end{array}\right.\end{array}  \tag{15}\]  
For Equation (15), obviously \(\Delta V_{2}>0\Leftrightarrow\Delta u_{2i}>0\). The strategy game \(<M\), \(S\), \(u(S)>\) is an ordinal potential game. Therefore, the problem of network topology optimization can be transformed into the problem of maximizing price function. It can be seen that if the power policy \(P^{*}\) is a Nash equilibrium of the topology control game and the corresponding network topology is connected. Then \(P^{*}\) is Pareto optimal. The inter cluster topology initialization algorithm based on potential game is as follows. In the Algorithm 1, node \(i\) initializes its transmit power to \(p_{i}^{\max}\), and then determines its transmit power according to its current residual energy \(Er_{i}\) and the collected local topology information. The adjustable transmit power set was \(P^{*}\). The transmit power in set \(P^{*}\) was arranged in descending order. ```
Input: Graph \(G\) (\(V\),\(E\),\(P\)) of WSN, clusters of network Output: Initial transmit power of CHs
```  
1. for loop = 1 to \(N\)
2. if node loop receives Hello message from neighbor nodes at time \(t\)
3. Update the adjacency matrix
4. end
5. end
6. for loop = 1 to \(N\)
7. if node loop is the CH
8. if the residual energy is below the threshold \(Er_{\text{th}}\)
9. Select the transmit power that maximizes price function \(p0_{loop}^{*}=\operatorname*{argmax}\
olimits_{1 loop}(p_{loop},p_{- loop})\) (\(p0_{loop}^{*}\in P^{*}\))
10. else
11. Select the transmit power that maximizes price function \(p0_{loop}^{*}=\operatorname*{argmax}\
olimits_{2 loop}(p_{loop},p_{- loop})\) (\(p0_{loop}^{*}\in P^{*}\))
12. end
13. end
14. Node loop broadcasts a HELLO message containing \(p0_{loop}^{*}\)
15. end ```
**Algorithm 1**Inter-cluster topology initialization algorithm
## 3 Non-Steady-State Heat Transfer Theory of Linear Heat Sources in Water-Soil Media  
When the heating source of an active heating and temperature measurement system is placed in water, its heat transfer model is idealized as a slender linear heat source situated in an infinitely large liquid environment [14]. At any given moment, the temperature within the linear heat source is uniformly distributed. The analytical solution for temperature variation in a linear heat source at a measurement point in a water medium with respect to time is derived as follows [12]:  
\[T(t)-T_{\infty}=\left\{\begin{array}{l}\frac{Q}{AR}(1-\frac{|Q-Ah(T_{0}-T_{ \infty})|}{Ah}e^{-\frac{Ah}{F^{\
u}}t}),0<t\leq t_{1}\\ (T(t_{1})-T_{\infty})e^{-\frac{hA}{F^{\
u}}(t-t_{1})},t>t_{1}\end{array}\right. \tag{1}\]  
In Equation (1), \(Q\) represents the heating power; \(A\) is the surface area of a linear heat source per unit length; \(h\) is the convection heat transfer coefficient; \(T_{0}\) is the initial temperature of the linear heat source surface; \(T_{\infty}\) is the ambient temperature; \(\rho\) is the density of linear heat sources; \(c\) is the specific heat capacity of linear heat sources; \(V\) is the volume of the linear heat source in contact with sea; \(t\) is the heating time; and \(t_{1}\) is the moment to stop heating.  
When the heating rod of an active heating and temperature measurement system is placed in soils, transient heat transfer theory is used to simplify them into a slender, linear heat source in a uniform and isotropic infinite solid environment [15; 16; 17]. The analytical solution of the non-steady state heat transfer formula for the linear heat source is as follows [12]:  
\[T(t)-T_{0}=\left\{\begin{array}{l}\frac{Q}{4\pi\lambda}(\ln\frac{4 dt}{t^{2}} -\gamma),0\leq t\leq t_{1}\\ \frac{Q}{4\pi\lambda}\ln\frac{t}{t-t_{1}},t>t_{1}\end{array}\right. \tag{2}\]  
In Equation (2), \(Q\) represents the heating power; \(\lambda\) is the thermal conductivity of the soil medium; a is the thermal diffusivity; \(r\) is the radius of a linear heat source; \(\gamma\) is the Euler-Mascheroni constant; \(T_{0}\) is the initial temperature of the linear heat source surface; \(t\) is the heating time; and \(t_{1}\) is the moment that heating is stopped.  
From Equations (1) and (2), it can be inferred that linear heat sources exhibit different rates of temperature change in the two different media of soil and water. Therefore, Formulas (1) and (2) can be used to calculate the variation in the surface temperature of the linear heat source over time in both soil and water media, respectively.  
However, these equations cannot be applied to determine variations in surface temperature over time within the transition region between coupled soil-water media. Further research is required to understand the temperature change pattern within this transitional zone.
## 3 Framework Development  
### Mechanical Model  
Given the significant computational costs, simulations were limited to a small \"cut-off\" section of the wall. A concrete cube measuring 116 \(\times\) 116 \(\times\) 116 mm\({}^{3}\) was extracted for simulation and analysis from the base of the wall, where the sum of the shear forces is the highest.  
For simplicity, the impact of the steel embedded in the concrete (rebar stiffness) was omitted in the model. However, radial pressure resulting from the corrosion of corroded steel was integrated into the model. This pressure, derived from Equation (18), was applied around a \"cylindrical hole\" at the external meshing points (see Figure 3). The pressure simulates the expansion of rust, particularly around the interface layer of the steel, which creates a significant force on the surrounding concrete, leading to internal stresses within the boundary layer. These stresses, represented as radial pressure in the simulation, can induce cracking and spalling in the concrete, ultimately weakening the wall's structural integrity.  
This model represents the seismic event as shear stress (\(\uptau\)) applied on the shear faces of the cube. Furthermore, the face connected to the back side of the wall is confined in the \(y\)-axis due to the continuum connection to the wall, and acts as a roller in the other directions. Finally, the remaining surface is left free, with no applied forces or continuous conditions, as shown in Figure 1. The shear stresses are applied incrementally until failure by applying velocity conditions in the shear direction (note that the self-weight of a single wall, compared to the shear forces generated by a seismic event, is negligible, and the rotation of the cube section is also relatively small based on [PERSON]'s circle analysis). The simulation process is repeated every ten years, during which the internal pressure is recalculated with increasing values, and degraded mechanical parameters are assigned. It is important to note that we considered the limitation that corrosive products become active after 20 years, as suggested by [57; 58], and therefore, degraded parameters are only applied after this period. Since we modeled the time intervals in years discretely/discontinuously, applying shear forces continuously until failure at each time interval, it is difficult to transfer the material properties degraded by cracks from one-time interval to the next. We relied on the empirical equations presented in Equations (1)-(8) to address this limitation. While this  
Figure 3: Radial pressure applied around a “cylindrical hole” (red).  
approach provides a reasonable approximation, it highlights the potential for future work to enable the continuous transmission of material property information across time steps.
# Dynamic Responses of Asphalt Concrete Waterproofing Layer in Ballastless Track
## 4 Viscoelastic Model of Asphalt Concrete
### Dynamic Modulus Test and Construction of Master Curve  
The samples extracted from the ACWL in the test section (Figure 6) were cored to conduct the dynamic modulus test. The dynamic modulus tests were performed at four different temperatures (10, 20, 30, and 40 \({}^{\circ}\)C) and nine frequencies (0.1, 0.2, 0.5, 1, 2, 5, 10, 20, and 25 Hz) by the UTM. The dynamic modulus and phase angle of asphalt concrete were obtained from the test.  
Based on the time-temperature superposition principle (TTSP), the master curves of dynamic modulus and phase angle can be constructed. The TTSP points out that the temperature effect is equivalent to time effect for viscoelastic materials: the high temperature state corresponds to a longer loading time, and the low temperature state corresponds to a shorter loading time. The time-temperature shift factor is defined as the translation distance between the test data and master curve along the logarithmic time or frequency axis. The shift factor \(a_{T}\) is expressed as:  
\[\log f_{r}=\log f+\log(a_{T}) \tag{1}\]  
where \(f_{r}\) is the reduced frequency and \(f\) is the test frequency.  
The Williams-Landel-Ferry (WLF) equation for the shift factor is widely used and is presented as [20]:  
\[\log(a_{T})=-\frac{C_{1}(T-T_{r})}{C_{2}+(T-T_{r})} \tag{2}\]  
in which \(C_{1}\) and \(C_{2}\) are positive constants dependent on the properties of asphalt mixture, and \(T_{r}\) is the reference temperature.  
The sigmodal function is adopted in this paper to fit the master curve of dynamic modulus and the function is shown as:  
\[\log|E^{*}|=m_{1}+\frac{m_{2}}{1+e^{m_{3}+m_{4}\log(f_{r})}} \tag{3}\]  
where \(|E^{*}|\) is the dynamic modulus, \(m_{1}\) the is minimum dynamic modulus value, \(m_{1}+m_{2}\) is the maximum dynamic modulus value, \(m_{3}\) and \(m_{4}\) are the parameters that control the curve shape.  
Figure 7: Field test results (**a**) Compactness; (**b**) Water permeability coefficient.  
The master curve of the phase angle is fitted by a mathematical function with a bell-shaped curve and the function is presented as [21]:  
\[\varphi=\varphi_{m}\bigg{\{}1+\bigg{[}\frac{\log(f_{m}/f_{r})}{R_{d}}\bigg{]} \bigg{\}}^{-m_{d}/2} \tag{4}\]  
where \(\varphi\) is the phase angle, \(\varphi_{m}\) is the maximum value of phase angle, \(f_{m}\) is the frequency when \(\varphi_{m}\) occurs, \(m_{d}\) and \(R_{d}\) are the fitting parameters.  
The master curves of dynamic modulus and phase angle were constructed following the TTSP at the reference temperature of 20 \({}^{\circ}\)C, as shown in Figure 8. Figure 8 indicates that the Equations (3) and (4) can well predict the dynamic modulus and phase angle in a wide range.