esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing a Conceptual Digital Twin Architecture for High-Temperature Heat Upgrade Systems
## 4 High-Temperature Heat Upgrade Use Case
### Mathematical Modeling  
To be able to simulate the concept described in the previous section, we modeled the system as shown in Figure 4. The waste energy source from the cold loop and the solar collectors from the hot loop are represented by electric heaters, while the energy demand is considered to be a heat sink element. To control the flow of HTL, the model contains electric valves coupled with pressure, temperature, and flow sensors. To ensure the safety of the system, we added pressure safety valves and pressure vessels.  
The heat pump transfers the energy from the cold loop (\(Q_{CL}\)) to the hot loop (\(Q_{HL}\)) using electrical power (\(W_{e}\)). Each loop's energy is dependent on the mass flow rate of the HTL (\(\dot{\mathrm{m}}\)), the specific heat capacity of the HTL (\(C_{p,HTF}\)), and the temperature difference between the inlet and outlet (\(\Delta T=T_{out}-T_{in}\)). With these variables defined in Equations (1)-(3), we can define the coefficient of performance (\(COP\)) of the heat pump as in Equation (4).  
\[Q_{CL}=\dot{\mathrm{m}}_{CL}\;C_{p,HTF}\;\Delta T_{CL} \tag{1}\]  
\[Q_{HL}=\dot{\mathrm{m}}_{HL}C_{p,HTF}\Delta T_{HL} \tag{2}\]  
\[Q_{HL}=Q_{CL}+W_{e} \tag{3}\]  
\[COP=\frac{Q_{HL}}{W_{e}} \tag{4}\]  
The thermal energy storage elements undergo charge and discharge cycles depending on the heat pump output and the energy demand. Similarly to the heat pump, TES energy can be computed using Equation (5). A more insightful characteristic would be the mean power of the TES (\(\overline{Q}_{TES}\)) during an entire cycle, defined by Equation (6) [41], where \(\Delta T_{TES}\) is the temperature difference between the inlet and outlet, \(\Delta t\) is the time interval between two readings, and \(t\) is the time taken by the PCM to vary from the initial \(T_{PCM,i}\) temperature to the final \(T_{PCM,f}\) temperature.  
\[Q_{TES}=\dot{\mathrm{m}}\;C_{p,HTF}\;\Delta T_{TES} \tag{5}\]  
Figure 4: Simplified model used to simulate the high-temperature heat upgrade system.  
\[\overline{\mathbb{Q}}_{TES}=\frac{\sum_{T_{PCM}}^{T_{PCM}}\hat{m}\ C_{p}|\Delta T_ {TES}|\Delta t}{t} \tag{6}\]  
For the electric heaters of the cold and hot loops and the heat exchanger, we use Equation (7).  
\[Q_{EH}=\hat{m}\ C_{p,HTF}\ \Delta T_{EH} \tag{7}\]  
For the pressure measuring and pressure vessel simulation, we use Equation (8), where \(\alpha_{HTF}\) represents the volumetric coefficient of thermal expansion for HTF and \(\alpha_{v}\) for the vessel, \(\Delta T\) is the temperature change, \(\beta\) is the relative change in volume of HTF per unit pressure, with \(D,\ L\) and \(E\) being the diameter, thickness and elastic modulus of the pressure vessel, respectively.  
\[\Delta P=(\alpha_{HTF}-\alpha_{v})\ \Delta T/\left(\beta+\frac{D}{L\,E}\right) \tag{8}\]  
To conclude the system modeling, we list all the important input/output simulation variables with their measurement unit in Table 3, grouped by system component.
## 2 APF-D3 QNPER Algorithm Design  
### Design Ideas  
DRL algorithms typically employ a random experience replay mechanism, which can result in the repeated sampling of low-value experiences, slowing down the convergence speed. To address this issue, the priority experience replay mechanism can be used. However, the traditional prioritized experience replay mechanism tends to prioritize experiences with larger TD errors, which can lead to situations where experiences with smaller TD errors in the replay pool remain unchanged under the new network parameter structure and cannot be updated. Additionally, the traditional prioritized experience replay mechanism does not consider the return of experience samples, overlooking valuable information that could aid network updates and lead to slow convergence and potential local optima. To overcome these limitations, we propose an experience replay mechanism based on sample experience reward value and resampling. This approach ensures that each sample is included in the replay process while reducing the number of training iterations in non-essential states.  
In complex dynamic environments, traditional DQN networks face challenges in making optimal obstacle avoidance decisions for obstacles that dynamically appear in unknown locations. To enhance the algorithm's generalization and obstacle avoidance capabilities, we propose an action output method based on the APF algorithm. In addition to utilizing deep vision and LiDAR as network inputs, we incorporate the APF algorithm as an additional network input to provide prior information for path planning robustness.  
The APF algorithm, as a traditional path planning algorithm, can have excellent performance in obstacle avoidance ability without training; meanwhile, the D3 QNPER algorithm, as a deep reinforcement learning algorithm, has excellent path planning ability and good obstacle avoidance ability, while it needs to spend time for model training. In the initial stage of D3 QNPER algorithm training, the convergence result can be accelerated if there is guidance for path planning from the APF algorithm. In the action output stage, combining the action output mechanism of APF and D3 QNPER algorithms makes the unmanned vehicle have excellent obstacle avoidance ability.
# Prescriptive Maintenance: A Systematic Literature Review and Exploratory Meta-Synthesis
### Research Questions and Objectives  
This SLR aims to advance the theoretical clarity of PsM by (i) systematically identifying PsM studies published in the post-I4.0 period; (ii) critically appraising studies dealing specifically with prescriptive aspects and post-prognostic (and diagnostic) recommendations and decision-making; and (iii) identifying research areas and challenges to recommend solution space in PsM practice. To do so, we set the following Research Questions (RQs):  
**RQ1:** What applications, methods, and tools are utilised within the PsM body of knowledge?  
Understanding the diversity of practical applications, methods, and tools, the motivation behind RQ1 is to enable researchers and practitioners to benchmark existing solutions and recognise areas for improvement. By identifying prevalent practices, technological bottlenecks, and knowledge gaps, the question aims to determine the breadth of real-world use cases, analytical methods, and tools deployed in practical PsM scenarios.  
**RQ2**: What activities characterise the prescriptive part of PsM?  
A precise characterisation of PsM activities is crucial for delineating PsM from other maintenance practices. By systematically mapping core activities, their automation levels, and operational context, RQ2 aims to clarify what uniquely constitutes PsM. Consequently, RQ2 supports theoretical differentiation and practical operationalisation, which in turn helps practitioners to make better decisions.  
**RQ3**: What clusters of research can be identified within the existing PsM?  
The motivation behind this approach is to identify thematic areas within PsM research and provide an integrative overview of the field, offering researchers and industry professionals a structured understanding of major research trends, focal areas, and theoretical contributions.  
**RQ4**: What are the current challenges and future research agenda for PsM?  
Lastly, exploring the current challenges and identifying future research directions are essential for bridging the gap between theoretical advancements and practical scenarios. Namely, by systematically extracting evidence from prior research, RQ4 aims to translate findings into actionable research agendas aligned with I5.0 objectives. RQ4 will drive industry leaders and policymakers in prioritising research investments and strategic decision-making for broader PsM adoption.  
The rest of the paper is structured as follows: The Section 2 provides a methodological description of the research protocol and search strategy; the Section 3 analyses metadata, content-based data, and qualitative data. The Section 4 offers an in-depth analytical description of the meta-synthesis results. The Section 5 presents concluding remarks, implications, limitations, and future research directions.
# Improved Particle Swarm Optimization for Sea Surface Temperature Prediction
## 4 Results and Discussion
### Evaluation Indicators and Test Functions  
In this paper, we used three indicators to evaluate our model performance. We used Root Mean Square Error (RMSE) [36] to assess the overall accuracy of the SSTP models. To evaluate the convergence and uniformity of the parameter optimization algorithm, we used Generational Distance (GD) [37] and Spacing (SP) [38] as the indicators, and introduced three test functions. The details of these indicators and test functions are described in the following.  
RMSE is a measure of the deviation between the observed value and the true value. The smaller the value, the more accurate the prediction. The formula of RMSE is as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}{(Y\_real_{i}-Y\_pred_{i})^{2}}}{n}} \tag{16}\]  
where \(Y\_real\) is the true value, \(Y\_pred\) is the predicted value, and n is the number of days to predict.  
The Pareto optimal solution set obtained by the multi-objective optimization algorithm should maintain the convergence of the solution and the uniformity of the distribution. In order to evaluate the convergence and uniformity of the Pareto frontier obtained by the multi-objective optimization algorithm, to obtain as many Pareto optimal solutions as possible, and to approach the true Paretofrontier as best as possible, GD was used as the solution convergence performance evaluation. The smaller the value of GD, the better the convergence of the solution set. Secondly, the Pareto optimal solution should be evenly distributed along the Pareto frontier as much as possible, and SP was used as the index of uniform distribution performance evaluation. The smaller the SP, the more uniform the solution set distribution. GD is defined as:  
\[GD=\sqrt{\sum_{i=1}^{n}\frac{d_{i}^{2}}{n}} \tag{17}\]  
where n is the optimal number of Pareto solutions, and \(d_{i}\) is the distance of the i-th Pareto optimal solution in the objective space from the nearest individual of the Pareto frontier. SP is defined as:  
\[SP=\sqrt{\frac{1}{n{-}1}{\sum_{i=1}^{n}{(\overline{d}-d_{i})}^{2}}} \tag{18}\]  
where n is the optimal solution number of Pareto, \(d_{i}\) is the distance of the i-th Pareto optimal solution from other individuals in the objective space, and \(\overline{d}\) is the average value of \(d_{i}\).  
LSPSO was applied to the bi-objective optimization of accuracy and efficiency of the SSTP. In order to verify the feasibility of LSPSO in the bi-objective optimization problem, three commonly used bi-objective test functions were selected for testing: BNH [39], SRN [40] and TNK [41]. We compared the GD and SP indicators of the optimal frontier obtained by MODE, NSGA-II, and OMOPSO, respectively. Table 1 lists the characteristics of these three test functions.
# Analytical Investigation of Vertical Force Control in In-Wheel Motors for Enhanced Ride Comfort
###### Abstract  
This study explores the effectiveness of vertical force control in in-wheel motors (IWMs) to enhance ride comfort in electric vehicles (EVs). A dynamic vehicle model and a proportional ride-blending controller were used to reduce vertical vibrations of the sprung mass. By converting the state-space model into a transfer function, the system's frequency response was evaluated using road profiles generated according to ISO 8608 standards and converted into Power Spectral Density (PSD) inputs. The frequency-weighted acceleration (\(a_{w}\)) was calculated based on ISO 2631 standards to measure ride comfort improvements. The results showed that increasing the proportional gain (\(K_{p}\)) effectively reduced the frequency-weighted acceleration and the RMS of the vertical acceleration of the sprung mass. However, the proportional gain could not be increased indefinitely due to the torque limitations of the IWMs. Optimal proportional gains for various road profiles demonstrated significant improvements in ride comfort. This study concludes that advanced suspension technologies, including the proportional ride-blending controller, can effectively mitigate the challenges of increased unsprung mass in IWM vehicles, thereby enhancing ride quality and vehicle dynamics.  
in-wheel motor; ride comfort; vehicle control; vehicle dynamics; frequency-weighted acceleration; power spectral density; road profile +
Footnote †: journal: Applied
# Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments
## 2 Background
### Problem Statement  
In this paper, we investigate the problem of multi-robot cooperative exploration in unknown environments, aiming to achieve maximal coverage rapidly. We consider a system of \(N\) agents in a two-dimensional, bounded, and unknown environment \(\mathcal{E}\subset\mathbb{R}^{2}\). The state of each agent \(i\) is denoted as \(\mathcal{S}_{i}=(\mathbf{r}_{i},\mathbf{p}_{i},\alpha_{i})\), where \(\mathbf{r}_{i}\) indicates agent \(i\)'s LiDAR scan information, \(\mathbf{p}_{i}=\{(d_{ij},\beta_{ij})\}_{j\
eq i}\) represents the relative distance \(d_{ij}\) and relative angle \(\beta_{ij}\) to agent \(j\), and \(\alpha_{i}\in[-\pi,\pi)\) is agent \(i\)'s own heading angle. Therefore, the system state can be defined as the Cartesian product of all agents' states: \(\mathcal{S}=\mathcal{S}_{1}\times\mathcal{S}_{2}\times\ldots\times\mathcal{S} _{N}\). Likewise, the action space of agent \(i\) is denoted as \(\mathcal{A}_{i}=(\Delta\alpha_{i},\Delta d_{i})\), where \(\Delta\alpha_{i}\in[-\alpha_{\max},\alpha_{\max}]\) represents the deviation angle relative to the current heading, and \(\Delta d_{i}\in[0,d_{\max}]\) denotes the travel distance along that direction with the radar detection range \(d\). Therefore, the joint action space of the system is \(\mathcal{A}=\mathcal{A}_{1}\times\mathcal{A}_{2}\times\ldots\times\mathcal{A} _{N}\). In addition, the central server is aware of the positions of all agents, and the terms 'robot' and 'agent' are used interchangeably in this context.  
In order to make a reasonable assessment, we define three key metrics:  
1. Exploration Time Steps (\(T\)): The total number of navigation operations (each considered a step), thus indicating the efficiency of the reinforcement learning strategies.
2. Coverage Rate (\(\mathcal{C}(t)\)): The proportion of environment \(\mathcal{E}\) explored at time step \(t\), calculated as \[\mathcal{C}(t)=\frac{\left|\bigcup_{i=1}^{N}\mathcal{A}_{i}(1:t)\right|}{| \mathcal{E}|},\] (3) where \(\mathcal{A}_{i}(1:t)\) represents the cumulative area explored by agent \(i\) from \(A_{1}\) to \(A_{t}\). An unknown area is considered fully explored when it is entirely enclosed by detected obstacles.  
Exploration Time (\(\tau\)): The total time spent completing the exploration task, distinct from the number of time steps (\(T\)), thus reflecting the overall efficiency of the process.
## 3 Results  
The hydraulic modeling presented in this study was conducted using specialized software tools to ensure accuracy and reliability in network simulations. The water distribution modeling was performed using OpenFlows WaterGEMS (version 10.02.03.06, Bentley Systems, Exton, PA, USA), allowing for detailed hydraulic assessments and EPS. Google Earth Pro (version 7.1.8.3036, Google LLC, Mountain View, CA, USA) was utilized for elevation data extraction, supporting topographical assessments for hydraulic gradient estimations. The digitization and network layout development were executed in AutoCAD 2019 (Autodesk Inc., San Rafael, CA, USA), while ArcMap 10.7.1 (Esri, Redlands, CA, USA) facilitated spatial analysis and GIS integration, refining geographic attributes and pressure zone delineations. These tools collectively contributed to the high-resolution digital representation of the water distribution network. The results of the application are presented in the following paragraphs, highlighting key findings from hydraulic modeling.
## 2 Design of Proposed Array
### An Array Extension  
The single element is first extended to 2 \(\times\) 1 for observing the mutual coupling characteristics when using the ASIWC. Figure 3a,b show the E-field distributions according to the presence and absence of the ASIWC structure. If we draw a specific E-field contour line of 60 dB, the separated E-fields can be observed obviously when employing the ASIWC. Moreover, we simulate S-parameters of the proposed antenna with and without the ASIWC. The mutual coupling value with the ASIWC is decreased by 3 dB less than that without the cavity at the operating frequency of 35 GHz because the multiple vias in the ASIWC can minimize the EM wave propagation from the element to the element through the substrate.  
Figure 2: Simulated bore-sight gain, HPBW, and reflection coefficient according to the parameters: (a) \(h_{2}\), (b) \(l_{4}\), and (c) \(w_{5}\).  
The cavity can additionally miniaturize the antenna size because it can increase the effective permittivity of the substrate [26], that is, the resonant frequency can be changed from 35.9 GHz (without the ASIWC) to 35 GHz (with the ASIWC).  
Figure 4 presents the geometry of the proposed 4 \(\times\) 1 linear array antenna from the extension of the single element. All elements are printed on the TLY-5 substrate, having a width of \(w_{7}\) (=27.9 mm) and a length of \(l_{7}\) (=8.9 mm), and the array distance between the adjacent elements is \(a\) of 6 mm. To steer the array beam pattern, the power-dividing feeding network is designed for the steering angles of \(\theta_{0}\) = 0\({}^{\circ}\), 15\({}^{\circ}\), and 30\({}^{\circ}\). The microstrip line stub length \(l_{8}\) and width \(w_{8}\) are determined by 2 mm and 0.366 mm for maintaining the wide impedance-matching bandwidth that the single element has. The results confirm that the proposed array antenna using the ASIWC can effectively improve the isolation and reduce the antenna size with the high directivity in Ka-band.
# A Review of Prediction Methods for Ultra-Low Cycle Fatigue Damage of Steel Piers Under Earthquakes
## 1 Introduction
Its fracture mechanism differs from that of low cycle fatigue failure [24; 25; 26; 27]. Currently, research on predicting ultra-low cycle fatigue damage remains exploratory. The primary methods for predicting ultra-low cycle fatigue damage in steel include the empirical formula method [28; 29], the cyclic void growth model (CVGM) [30], and the continuous damage mechanics (CDM) model [31]. The traditional fracture mechanics method is mainly suited for studying brittle or pseudo-brittle fractures with minimal local plastic damage [32]. It is not applicable to ductile fracture under cyclic loading [33]. Although the empirical formula method offers simplicity and ease of integration with finite element software, it requires extensive parameter calibration and cannot effectively predict the fatigue life of welded joints under complex  
Figure 1: Ultra-low cycle fatigue failure of steel bridge piers. stress states [34]. The CVGM can forecast the ultra-low cycle fatigue life of steel or steel structures under complex stress conditions; however, it demands numerous calibration tests, features significant parameter dispersion, and entails a complicated prediction process [35; 36]. Although the CDM model has fewer calibration requirements, offers multiple prediction functions, and integrates easily with finite element software, it does not account for the influence of equivalent plastic strain on damage increment, indicating a need for improved prediction accuracy [37]. These prediction methods primarily focus on estimating the crack initiation life of ultra-low cycle fatigue in steel piers [38]. However, it has been observed that crack initiation typically does not impact the horizontal bearing capacity or displacement ductility of steel piers. Only once a crack reaches a certain length does the bearing capacity and ductility significantly decline. Thus, predicting ultra-low cycle fatigue in steel bridge piers should not rely solely on crack initiation limits [39]. Due to the lack of consensus among scholars on predicting ultra-low cycle fatigue life in steel, relevant standards domestically and internationally, such as China's \"General Specifications for Design of Highway Bridges and Culverts\" [40] and Japan's \"Specifications for highway bridges-PartII Steel Bridges\" [41], do not provide methods for predicting ultra-low cycle fatigue damage in steel piers during strong earthquakes. Consequently, these standards are insufficient for evaluating the ability of steel piers to withstand ultra-low cycle fatigue damage.
# Characteristic Length and Time Scales of the Highly Forward Scattering of Photons in Random Media
## 2 Materials and Methods
### Analytical Solutions (AS) of the Time-Domain Photon Transport Models  
#### 2.2.1 3D Infinite Homogeneous Media  
In this paper, we mainly employed the analytical solutions of the time-dependent RTE, dEM, and PDE for 3D infinite homogeneous media to calculate the temporal profiles of the fluence rate \(\Phi(\mathbf{r},t)\) when an isotropic point source is incident at the initial time and at the origin of the \(xyz\)-coordinate as shown in Figure 3a. Then, the source-detector (SD) distance \(r_{sd}\) is given by \(r=|\mathbf{r}|\). The analytical form of the fluence rate for the RTE \(\Phi_{RTE}(r,t)\) with an arbitrary \(g\)-value is derived by [PERSON] and [PERSON] [31]:  
\[\Phi_{RTE}(r,t)=\Phi_{LK}(r,t;\mu_{a},\mu_{s},\mathbf{\sigma_{HG}},n), \tag{10}\]  
where \(\Phi_{LK}\) represents the analytical form by [PERSON] and [PERSON]; \(\mathbf{\sigma_{HG}}\) the vector of the expansion coefficients for the HG phase function up to a maximum order of \(N\): \([1,g;g^{2},g^{3},\cdots,g^{N}]\); the \(N\)-value is determined so as to attain sufficient convergence of the results; and \(n\) is the refractive index of the medium. The verifications of \(\Phi_{RTE}(r,t)\) were confirmed by several independent works [23; 32]. Using \(\Phi_{LK}\), we can obtain the analytical form for the dEM \(\Phi_{dEM}(r,t)\):  
\[\Phi_{dEM}(r,t)=\Phi_{LK}(r,t;\mu_{a},\mu_{s}^{M},\mathbf{\sigma_{M}},n), \tag{11}\]  
where \(\mathbf{\sigma_{M}}\) represents the vector of the expansion coefficients for \(p_{\ell 2}^{M}\) (Equation (6)) up to \(N(>M)\): \([1,\sigma_{dE1},\sigma_{dE2},\cdots\sigma_{dEM},\sigma_{dEM+1}=0,\cdots, \sigma_{dEN}=0]\). We confirmed the verification of \(\Phi_{dEM}(r,t)\) by comparing with the numerical solutions in Appendix A. The analytical solution for the dEO (\(M=0\)) by [PERSON] and [PERSON] is largely oscillated and unstable for several cases such as short SD distances, short times after light incidence, and the low scattering coefficient. In those cases, we employed the analytical solution for the dE0 derived by [PERSON] [33] instead. It is noted that [PERSON] and coworkers employed a heuristic approach to obtain the approximate solution of the RTE for highly forward scattering [34] from the exact solution of the RTE for isotropic scattering by [PERSON] [33]. Their approach is identical to the zeroth order dEA, so that the \(\Phi(r,t)\)-result using their approach coincides with the result of \(\Phi_{dE0}(r,t)\). The analytical form for the PDE \(\Phi_{PDE}(r,t)\) is given as [35]  
\[\Phi_{PDE}(r,t)=\frac{1}{\left(4\pi Dt\right)^{3/2}}\exp\left[-\mu_{a}vt-\frac {r^{2}}{4 Dvt}\right]. \tag{12}\]