esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 DT Workshop Planning Model-Based Decision Support Methodology
### Work Hours Forecasting  
The colossal assembly and welding workloads are determined by the intricate design of OE platforms. The operators' estimation of their working hours is inaccurate due to the twin effects of the learning effect, including operation proficiency, and the deterioration effect, including fatigue degree. The errors' cumulative effect severely hampers the execution rate of the workshop plan. Hence, it is essential to anticipate labor hours using DT.  
Based on the previous study [42], the measured data access and the forecasted data output are additional elements in the work hours forecasting framework, as shown in Figure 8. The measured data from the data acquisition layer of the DT framework is input to the work hours quota database and the process route database, and K-means-based clustering analysis of welding processes is performed according to the operational characteristics to evaluate the work hours quotas according to the classification of different types of welding processes with various difficulties. The evaluation method uses a slack-based measure model in data envelopment analysis to update the time quota \(OP_{i}\) for the process type \(i\) according to the planned data and execution data and to evaluate the validity. Finally, according to the same type of process over time iteration of work time rules, the formation of dynamic standard work time allows MPS to achieve highly reliable data input. Equation (34) shows the prediction model \(WHFM(POT_{i},ft_{i})\) for work hours of welding operations with welding characteristics as the independent variable.  
\[WHFM(POT_{i},ft_{i})=f(OP_{i}) \tag{34}\]  
subject to:  
\[\min(ft_{i})\ \forall i\in[1,N] \tag{35}\]  
\[\min(POT_{i}+ft_{i}-AOT_{i})\ \forall i\in[1,N] \tag{36}\]  
\[\min\sum_{i=1}^{N}(POT_{i}+ft_{i}-AOT_{i}) \tag{37}\]  
where \(POT_{i}\) is planned operating time, \(AOT_{i}\) represents actual operating time, and \(ft_{i}\) stands for floating time. \(f(OP_{i})\) is a recursive equation based on Graph Long Short-Term Memory (Graph LSTM):  
\[f(OP_{i})=\sigma\left(\mathbf{W}^{\prime}op_{i}^{(t)}+\mathbf{U}_{m(i,k)}^{\prime}\bm {h}_{ik}^{(t-1)}+\mathbf{b}^{\prime}\right) \tag{38}\]  
where \(\mathbf{W}\) stands for the weights, \(\mathbf{U}\) for the weight matrix to be trained, \(m(i,k)\) indicates the relationship type label between nodes, \(\mathbf{h}\) for the hidden vector, and \(\mathbf{b}\) for the training bias.
## Appendix A Supplementary Information
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Common Name** & **CD \(\pi\)-\(\pi\)** & **W** & **R** & **RS** & **Common Name** & **CD \(\pi\)-\(\pi\)** & **W** & **R** & **RS** \\ \hline Myristic acid & 14.0 & 8.64 \(\pm\) 0.17 & 7.54 \(\pm\) 0.02 & 8.39 \(\pm\) 0.55 & Linoleic acid & 18.2n-6 & 1.41 \(\pm\) 0.25 & 1.51 \(\pm\) 0.13 & 1.62 \(\pm\) 0.21 \\  & 14.1n-9 & 0.01 \(\pm\) 0.01 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & & 18.2n-4 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 \\  & 14.1n-7 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & & 18.3n-6 & 0.18 \(\pm\) 0.11 & 0.23 \(\pm\) 0.02 & 0.21 \(\pm\) 0.08 \\ Myristoleic acid & 14.1n-5 & 0.08 \(\pm\) 0.02 & 0.09 \(\pm\) 0.01 & 0.08 \(\pm\) 0.01 & & 18.3n-4 & 0.01 \(\pm\) 0.01 & 0.05 \(\pm\) 0.02 & 0.06 \(\pm\) 0.03 \\  & i+15.0 & 0.01 \(\pm\) 0.01 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & & \(\pm\)-Linoleic acid & 18.3n-3 & 0.44 \(\pm\) 0.06 & 0.36 \(\pm\) 0.04 & 0.41 \(\pm\) 0.04 \\  & i+15.0 & 0.01 \(\pm\) 0.01 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & & 18.3n-3 & 0.21 \(\pm\) 0.06 & 0.60 \(\pm\) 0.07 & 0.64 \(\pm\) 0.14 \\  & 15.0 & 0.28 \(\pm\) 0.07 & 0.26 \(\pm\) 0.01 & 0.25 \(\pm\) 0.03 & Stearidonic acid & 18.4n-3 & 0.21 \(\pm\) 0.06 & 0.21 \(\pm\) 0.03 & 0.23 \(\pm\) 0.05 \\  & i+16.0 & 0.12 \(\pm\) 0.05 & 0.13 \(\pm\) 0.02 & 0.12 \(\pm\) 0.02 & & 18.4n-1 & 0.01 \(\pm\) 0.01 & 0.02 \(\pm\) 0.01 & 0.02 \(\pm\) 0.01 \\ Palmitic acid & 16.0 & 15.18 \(\pm\) 0.24 & 14.80 \(\pm\) 1.80 & 1.52 \(\pm\) 3.67 & Arachidic acid & 20.0 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.08 & 0.01 \(\pm\) 0.08 \\  & 16.1n-11 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & & 20.1n-11 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 \\ Hypogne acid & 16.1n-19 & 1.79 \(\pm\) 0.24 & 2.99 \(\pm\) 0.07 & 2.78 \(\pm\) 0.51 & Gondolo acid & 20.1n-9 & 0.03 \(\pm\) 0.01 & 0.04 \(\pm\) 0.01 & 0.00 \(\pm\) 0.00 \\ Palmitic acid & 16.1n-7 & 28.20 \(\pm\) 0.95 & 27.17 \(\pm\) 1.59 & 24.39 \(\pm\) 1.07 & Paulline acid & 20.1n-7 & 0.01 \(\pm\) 0.01 & 0.02 \(\pm\) 0.01 & 0.04 \(\pm\) 0.02 \\  & 16.1n-5 & 0.12 \(\pm\) 0.01 & 0.09 \(\pm\) 0.08 & 0.10 \(\pm\) 0.07 & & 20.2n-9 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 \\  & 17.1n-4 & 0.00 \(\pm\) 0.00 & 0.01 \(\pm\) 0.01 & 0.01 \(\pm\) 0.01 & Eicosadiadiec acid & 20.2n-6 & 0.09 \(\pm\) 0.03 & 0.10 \(\pm\) 0.08 & 0.03 \\  & i+17.0 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & 0.00 \(\pm\) 0.00 & & 20.3n-6 & 0.05 \(\pm\) 0.02 & 0.08 \(\pm\) 0.02 & 0.07 \(\pm\) 0.01 \\  & 16.2n-6 & 0.94 \(\pm\) 0.20 & 1.13 \(\pm\) 0.11 & 1.16 \(\pm\) 0.19 & Arachidic acid & 20.4n-6 & 0.33 \(\pm\) 0.05 & 0.62 \(\pm\) 0.05 & 0.46 \(\pm\) 0.08 \\  & i+17.0 & 0.06 \(\pm\) 0.00 & 0.09 \(\pm\) 0.01 & 0.07 \(\pm\) 0.04 & Eicosadi
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.3 EnergyTIC  
ICT tools have also been examined for in-home energy management, as indicates the project \"EnergyTIC\". Some 1700 houses participated in the project. The end users have been informed through useful messages for their consumptions from water and energy companies, thus being able to monitor and adapt their energy usage [83].
# A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
## 2 Related Work and Preliminary Technology
### Related Work
[29] introduced an auction mechanism to encourage ESs to offer services for tasks with dependencies, as well as designed an algorithm based on multiple rounds of truthful combinatorial reverse auctions to solve the problem of maximizing social welfare. However, the multi-round auction process in this algorithm may lead to high waiting latency. The algorithmic designs in the aforementioned literature are all centralized. However, in reality, much of the information from users and ESs is difficult to control globally. Over the recent years, certain literature has also explored the design of incentive mechanisms under distributed systems. [PERSON] et al. [PERSON] et al. (2018) proposed a learning-based distributed resource coordination framework which transforms the computation offloading and resource allocation problems into dual timescale problems, and it then solves them using game theory and distributed reinforcement learning algorithms. [PERSON] et al. [PERSON] et al. (2019) described the computational offloading mechanism with resource allocation in EC networks as a stochastic game, and they designed a Q-learning algorithm to achieve NE. [PERSON] et al. [PERSON] et al. (2019) jointly optimized offloading decisions and resource pricing, as well as designed a learning game method to obtain optimal decisions. Despite these methods being based on a distributed approach, the aforementioned studies did not consider the dependencies of tasks. [PERSON] et al. [PERSON] et al. (2019) proposed a multi-objective offloading optimization algorithm based on reinforcement learning, which was aimed at minimizing execution time, energy consumption, and the cost of dependent task offloading. However, that work did not dive into the cost aspect. This paper investigates the problem of incentive mechanisms in dependent task scenarios, as well as proposes two distributed reinforcement learning algorithms to find optimal solutions.
## 1 Introduction
However, most of the studies were focused on the demonstration of the suitability of the formulated paste for the fabrication of porous structures representing different final porosities and pores sizes [35, 36, 38, 41]. While attempts have also been conducted to increase the mechanical properties of the printed scaffolds through soaking the produced structures in the simulated body fluid (SBF) [36, 42] or polymer infiltration [37, 39, 43], no studies have been reported on the production of dense or FGM CaP structures through this technique. The last concern relates to probable inflammation and infection in the surgery site, due to bacterial adhesion to the implant surface. Generally, bacterial adhesion originates from the surgery and escapes from immunological surveillance [44]. It is shielded from the host immune system by theformation of bacterial biofilms making any antibiotic therapy ineffective. This circumstance leads to implant loss and inevitable revised surgery [44; 45]. The most reliable way to avoid bacterial adhesion is the incorporation of antibacterial agents such as silver, copper, zinc, mercury, tin, etc. into/onto implants [44; 46]. Among these elements, silver has been exploited since ancient times as an effective antimicrobial agent that inhibits bacterial replications by damaging their RNA and DNA [47]. In comparison with other heavy metals, silver also shows remarkably low toxicity [45]. This paper is divided into two parts. In the first (main) part, the production of porous, dense and bi-layer scaffolds using the robocasting technique from a commercially available hydroxyapatite (HA) powder is reported. The fabrication of a bi-layer robocast sample in a single printing step is a key innovation, as no previous examples--to the best of the authors' knowledge--are currently available in the literature. The bi-layer scaffolds were made of a dense core part designed to provide load-bearing function, and a surrounding porous part to provide biological performances. Physical, mechanical and bioactivity properties of the new scaffolds were characterized. The biodegradation properties of the scaffolds were already verified in the authors' previous work by in vitro dissolution tests, by immersion of the samples in a 0.1M tris(hydroxymethyl) aminomethane solution, at physiological pH and 37 \({}^{\circ}\)C, up to 17 days [21]. The second part is dealing with the hybridization of the robocasting technique with conventional routes for the processing of polymer parts, with the aim to provide further functionalities to the scaffolds. Such an original approach implies the joining of the bi-layer robocast scaffold with an antibacterial porous polymer outer layer fabricated by solvent casting/salt leaching techniques. The antibacterial functionality was achieved through the addition of silver ions to the polymer layer and validated through antimicrobial tests. The effective release of silver ions proved the ability of the polymer layer to provide the desired functionality; however, in the future different drugs could be added as well, to respond to specific requirements.
# Performance Investigation of Active, Semi-Active and Passive Suspension Using Quarter Car Model
## 2 Methodology
### Road Profile Modelling  
Road profile inputs were chosen based on common disturbances a vehicle might undergo in real-world usage. As such, road profile 1 models a simple sinusoidal bump, and road profile 2 models a vehicle mounting a typical curb height at a speed which qualifies it as an extreme circumstance. Road profile 3 models repeated sinusoidal bumps in order to test the response of the suspension systems under multiple, consecutive disturbance inputs.  
Road profile 1 is a single sinusoidal bump, as indicated by Equation (2):  
\[w_{1}(t)=\left\{\begin{array}{ccc}-0.05\,\sin(10\pi t)&&2.5\,\text{s}\leq t \leq 2.6\,\text{s}\\ 0\,\text{m}&&\textit{otherwise}\end{array}\right. \tag{2}\]  
where \(w_{1}(t)\) represents a road bump profile for a vehicle speed of 18 km/h, assuming a bump size (length) of 0.5 m and duration of 0.1 s, as shown in Figure 1.  
Road profile 2 is a 0.15 m sudden step, as indicated by Equation (3):  
\[w_{2}(t)=\left\{\begin{array}{ccc}0&0\,\text{s}\leq t\leq 2\,\text{s}\\ 0.15\,\text{m}&&\textit{t}\geq 2\,\text{s}\end{array}\right. \tag{3}\]  
where \(w_{2}(t)\) is a sudden step input of a height of 0.15 m, simulating the vehicle mounting a typical curb height, as shown in Figure 2.  
Road Profile 3 is consecutive bumps of increasing amplitude, as indicated by Equation (4):  
\[w_{2}(t)=\left\{\begin{array}{ccc}-0.05\,\sin(10\pi t)&&2.6\,\text{s}\leq t \leq 2.7\,\text{s}\\ -0.07\,\sin(10\pi t)&&4.7\,\text{s}\leq t\leq 4.8\,\text{s}\\ -0.09\,\sin(10\pi t)&&6.7\,\text{s}\leq t\leq 6.8\,\text{s}\\ 0\,\text{m}&&\textit{otherwise}\end{array}\right. \tag{4}\]  
where \(w_{3}(t)\) simulates repeated speed bumps for a vehicle speed of 18 km/h, assuming a bump size (length) of 0.5 m and duration of 0.1 s, as shown in Figure 3.  
Figure 1: Road Profile 1: Single sinusoidal bump.
# Parameter Optimization Based BPNN of Atmosphere Continuous-Variable Quantum Key Distribution
## Appendix A The Atmospheric Transmittance Analysis
\begin{split}\langle x_{0}^{2}\rangle=\langle y_{0}^{2}\rangle& =0.33W_{0}^{2}\sigma_{R}^{2}\Omega^{-\frac{7}{6}},\\ \langle\Theta_{1}^{2}\rangle=\langle\Theta_{2}^{2}\rangle& =\text{ln}\Big{[}1+\frac{1.2\sigma_{R}^{2}\Omega^{\frac{5}{6}}}{(1+2.96\sigma_{ R}^{2}\Omega^{\frac{5}{6}})^{2}}\Big{]},\\ \langle\Theta_{1}\Theta_{2}\rangle&=\text{ln}\Big{[}1-\frac{0.8 \sigma_{R}^{2}\Omega^{\frac{5}{6}}}{(1+2.96\sigma_{R}^{2}\Omega^{\frac{5}{6}}) ^{2}}\Big{]},\end{split}  \tag{15}\]with  
\[\Omega =\frac{kW_{0}^{2}}{2L}, \tag{10}\] \[\langle\Theta_{1,2}\rangle =\ln\Bigl{[}\frac{(1+2.96\sigma_{R}^{2}\Omega^{\frac{5}{6}})^{2}}{ \Omega^{2}\sqrt{(1+2.96\sigma_{R}^{2}\Omega^{\frac{5}{6}})^{2}+1.2\sigma_{R}^{ 2}\Omega^{\frac{5}{6}}}}\Bigr{]}.\]  
In the case of strong turbulence, these elements are expressed by [24]  
\[\langle x_{0}^{2}\rangle =\langle y_{0}^{2}\rangle=0.75W_{0}^{2}\sigma_{R}^{\frac{8}{6}} \Omega^{-1}, \tag{11}\] \[\langle\Theta_{1}^{2}\rangle =\langle\Theta_{2}^{2}\rangle=\ln\Bigl{[}1+\frac{13.14\gamma \sigma_{R}^{\frac{12}{6}}\Omega^{-1}}{(\gamma+1.71\sigma_{R}^{\frac{12}{6}} \Omega^{-1}-2.99\sigma_{R}^{\frac{8}{6}}\Omega^{-1})^{2}}\Bigr{]},\] \[\langle\Theta_{1}\Theta_{2}\rangle =\ln\Bigl{[}1+\frac{0.65\gamma\sigma_{R}^{\frac{12}{6}}\Omega^{-1 }}{(\gamma+1.71\sigma_{R}^{\frac{12}{6}}\Omega^{-1}-2.99\sigma_{R}^{\frac{8}{ 6}}\Omega^{-1})^{2}}\Bigr{]},\]  
with  
\[\gamma =\frac{1+\Omega^{2}}{\Omega^{2}}, \tag{12}\] \[\langle\Theta_{1,2}\rangle =\ln\Bigl{[}\frac{(\gamma+1.71\sigma_{R}^{\frac{12}{6}}\Omega^{- 1}-2.99\sigma_{R}^{\frac{8}{6}}\Omega^{-1})^{2}}{\sqrt{(\gamma+1.71\sigma_{R} ^{\frac{12}{6}}\Omega^{-1}-2.99\sigma_{R}^{\frac{8}{6}}\Omega^{-1})^{2}+3.24 \gamma\sigma_{R}^{\frac{8}{6}}\Omega^{-1}}}\Bigr{]}.\]  
The weak turbulence results can be applied, e.g., for short propagation distances with \(\sigma_{R}^{2}<1\). In near-to-ground propagation, the latter condition is fulfilled for optical frequencies for night-time communication. The strong turbulence results are applied for short distance communication, \(\sigma_{R}^{2}\gg 1\). For a near-to-ground communication scenario, this corresponds to the day-time operation on clear sunny days.
## 3 Biochar Adsorbents
### Biochar Usage in Faecal Sludge Management
#### 3.2.2 Heavy Metal Immobilization  
Biochar can also act as a barrier to prevent heavy metals from percolating into groundwater aquifers and surface water resources [15; 24; 25]. Biochar has a high ability of filtering of heavy metals in contaminated soil and faecal sludge due to the potential of adsorbing metals on its surface [16]. Sequestration of Pb, Cd, Cu, and Ni has been reported from cottonseed hull biochar because of functional groups on the biochar surfaces [24]. As pH,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Nutrient in Wastewater** & **Biochar Retention** \\ \hline NH\({}_{4}\)\({}^{+}\) removal rate (g/m\({}^{3}\)/d) & 5.4 \(\pm\) 0.51 \\ NH\({}_{4}\)\({}^{+}\) removal (\%) & 90\% \(\pm\) 4\% \\ PO\({}_{4}\)\({}^{3-}\) removal rate (g/m\({}^{3}\)/d) & 3.8 \(\pm\) 0.01 \\ PO\({}_{4}\)\({}^{3-}\) removal & 87\% \(\pm\) 2\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Wastewater treatment and the retention ability of biochar [109].  
volatile matter, O:C, and N:C ratios in the biochar increase, biochar's capacity to adsorb heavy metals also increases [16; 24]. In a study using poultry litter biochar and green waste biochar, it was found that Cd and Pb elements in soil water decreased [94]. Conversely, Cu increased in the soil water because of more mobility through increased dissolved organic C [94]. Other studies observed no effect on Cu when the soil was mixed with a hardwood biochar produced at 750 \({}^{\circ}\)C [20]. Decreases in Cd, Cr, Cu, Pb, Ni, and Zn in excess faecal effluent leachates from a soil column amended with a wood-based biochar were also found [25]. Cu, Cd, and Pb were removed from aqueous solution after amending the soil with bamboo, sugarcane, hickory, and peanut hull biochars, with the bamboo biochar being most effective [62]. The high adsorption of heavy metals in the study by [PERSON] et al. [62] was associated with pH increases in the solution. An increase in Cu and Zn removal as pH increased has also been seen using hardwood and corn straw biochar produced at temperatures of 450 \({}^{\circ}\)C and 600 \({}^{\circ}\)C [110]. These findings are similar to [PERSON] et al. [111], showing the immobilization occurring when faecal sludge is treated with biochar (Figure 3). The long-term impacts of biochar on heavy metal elements sorption are limited and require more research due to the recalcitrance of biochar [16].
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 2 Concept of Albedo
All these parameters of influence are considered in the methodologies that compute surface albedo through the BRDF, since this is the function that allows us to correct reflectance values and converts reflectance observations to surface albedo values. BRDF describes how the reflectance of a surface depends on the point of view of the observer and on the solar angle, and the characteristics of the surface; that is, surface reflectance depends on the view and illumination geometry, and the structure of the environment. The BRDF correction is applied to correct in the satellite images the effects of the point of view (angle of view) and the illumination angle, in order to calculate albedo from the reflectance values of different wavelengths.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
### Measuring Sea Level  
Methods for measuring sea level can be categorised into three types [6]: surface-following devices, fixed sensors, and remote or mobile sensors. Some established methods are not really suitable for the intertidal zone: surface following devices such as tide poles and float gauges in still wells, although robust, require attachment to significant vertical structures to function. Fixed sensors such as acoustic reflection and radar reflection, although low cost, also require substantial vertical infrastructure. Another fixed sensor method, GPS reflectometry [7], uses the ratio of'signal' (directly received satellite signal) to 'noise' (caused by satellite signals reflecting off the sea surface);this works best where water is present at all states of the tide, and is not as effective in wide intertidal zones (limiting the number of satellites used at low water). A more appropriate method for intertidal beaches is to use a pressure sensor mounted on the sea bed or attached to a structure [8]. In addition to sea-level data, pressure sensors can also record high frequency data for analysis of waves.  
A remote/mobile sensor such as the GNSS buoy has been shown to work well in sheltered, shallow water locations and a number of different GNSS buoys with high-end expensive GPS receivers were tested [9]. We have previously used a GNSS buoy equipped with a low-cost receiver that was capable of measuring accurate sea-level data and wave data within a semi-enclosed harbour [8]. However, analysis of sea-level from a GNSS buoy deployment in the North Sea [10] concluded that in the presence of large waves the received GNSS signal contained significant noise.
# Retrofit Fatigue Cracked Diaphrag Cutouts Using Improved Geometry in Orthotropic Steel Decks
###### Abstract  
Diaphragm cutouts are set to release redundant constraints and hence reduce weld fatigue at the connection of U-ribs to diaphragms in orthotropic steel decks. However, most fatigue cracks which originate from the edge of cutouts are in fact detected in the diaphragms. Therefore, a retrofit technology on cracked cutouts at the diaphragm is proposed and applied to the orthotropic steel box girder of a suspension bridge. Firstly, the stress concentration on the cutout is analyzed through refined finite element analyses. Furthermore, the fatigue cracked cutouts are retrofitted by changing their geometrical parameters. Thereafter, an optimized geometry and the size of diaphragm cutouts were confirmed and applied in the rehabilitation of a suspension bridge. On-site wheel load tests were carried out before and after retrofitting of the diaphragm cutout. The stress distributions along the edges of the cutouts and at the side of a diaphragm were measured under a moving vehicle. The stress spectra at two critical locations on the edge of a cutout was obtained under longitudinally and laterally moving vehicles. Finally, the fatigue life of the cutouts is assessed by the modified nominal stress method. The analytical and test results indicate that the wheel loads on the deck transmit stress to the diaphragms through the U-ribs, during the load transmission process, the stress flow is obstructed by diaphragm cutouts, resulting in local stress concentrations around the cutouts. In addition, the overall size of the cutouts should be small, but the radius of the transition arc should be large, thus the stress flow will not be obviously obstructed. After the retrofitting of the cutouts by improved geometry, the maximum stress decreases by 87.6 MPa, which is about 40% of the original stress. The equivalent constant amplitude stress is reduced by 55.2% when the lateral position of the wheel loads is taken into consideration. Based on the stresses obtained by finite element analysis (FEA) and experimental tests, the fatigue lives of the original cutouts are 1.7 and 4.9 years, respectively, which increase to 78.1 and 155.5 years, respectively, after the cutouts were retrofitted, which indicates that the improved geometry and retrofit technology can enhance the fatigue performance and extend the fatigue life of diaphragm cutouts with fatigue cracks.  
**Keywords:** orthotropic steel deck; fatigue rehabilitation; diaphragm cutout; stress spectrum; fatigue assessment