esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 4 Optimization Algorithms
### The Proposed Improved Equilibrium Optimizer Algorithm
#### 4.3.4 Binomial Crossover and Selection Operation  
To further increase the population's potential diversity of EO, this study adopts the binomial crossover operation, in which the dimensional components of the mutation concentration \(V_{i}\) and the current concentration \(C_{i}\) are randomly reorganized by a crossover operation. The solution individual after the mutation and crossover operation is defined as the trial vector \(U_{i}\), and the scheme may be outlined as:  
\[\mathbf{U_{ij}}=\left\{\begin{array}{ll}\mathbf{V_{ij}}&\text{if }(CR\geq rand\text{ or }j=j_{rand})\\ \mathbf{C_{ij}}&\text{otherwise}\end{array}\right. \tag{41}\]  
where \(CR\) is the crossover probability between [0, 1], and \(j_{rand}\) is the dimension identity randomly generated within [1, D]. For each dimension \(j\), a random number \(rand\) is generated between [0, 1), and if its value is less than \(\mathbf{CR}\), then the \(j\)th dimension of \(U_{i}\) comes from the \(j\)th dimension of \(Vi\); otherwise, from the \(j\)th dimension of \(C_{i}\).  
In this case, the number of parameters inherited from the mutation vector will have an approximate binomial distribution. The crossover must occur if the current dimension \(j\) is equal to \(j_{rand}\). This rule ensures that the trial vector \(U_{i}\) obtains at least one component from the mutation vector \(V_{i}\), thereby preventing them from being identical. The scheme ensures the generation of new individuals and mitigates the risk of evolutionary stagnation within the population.  
Finally, the selection operation will apply the principle of \"survival of the fittest\" to the trial vector \(U_{i}\) and the current vector \(C_{i}\). The individuals with better fitness values in the current generation \(G\) will be saved in the next generation of the population, as shown in Equation (42).  
\[\mathbf{C}_{i,G+1}=\left\{\begin{array}{ll}\mathbf{U}_{i,G}&\text{if }f(\mathbf{U}_{i,G})\leq f(\mathbf{C}_{i,G})\\ \mathbf{C}_{i,G}&\text{otherwise}\end{array}\right. \tag{42}\]
# Characterization of GNSS Signals Tracked by the iGMAS Network Considering Recent BDS-3 Satellites
## 1 Introduction
The satellites were aboard a Long March-3B carrier rocket which launched from Xichang Satellite Launch Center, China. Up to June 2018, eight BDS-3 MEO satellites (excluding experimental satellites) have been successfully launched followed by extensive testing and evaluation. These satellites are manufactured by the China Academy of Space Technology (CAST) and the China Academy of Science (CAS). Some details of these eight BDS-3 satellites are listed in Table 1. It is noted that the PRN numbers of some satellites may be changed in the future. The ranging performance of a GNSS mainly depends on the measurement quality of the transmitted satellite navigation signals. Analysis of signal quality, measurement noise and multipath is critical for navigation systems [14]. Up to the present, only BDS-3e satellites have been analyzed. There has been a lack of reports conducted on the analysis of authentic BDS-3 satellite signals. In this study, we focus on the assessment and characterization of GNSS signals tracked by the iGMAS network, especially for the signals from the eight recently deployed BDS-3 satellites. The paper is organized as follows. First, an overview of the iGMAS network with BDS-3 tracking capability is described. Then, the tracking characteristics of BDS-3, BDS-2, GPS, and Galileo signals are compared and discussed, including the carrier-to-noise density ratio and the pseudo-range multipath and errors. In the following section, we analyze the satellite-induced code biases for all the available signals of BDS-3. Finally, we present the results of triple-frequency carrier-phase combinations to investigate the inter-frequency clock biases.
## 5 Treatment
### Surgical Interventions  
Surgical management of MRONJ often involves the resection of necrotic bone followed by reconstruction. Precision in resecting the affected area is crucial and necessitates advanced visualization techniques. A study conducted a rigorous evaluation of 17 articles, selected from a comprehensive pool of 320, to assess the clinical efficacy of autofluorescence-guided laser therapy in this context [63]. This innovative technology leverages autofluorescence to enhance the visibility of bone structures impacted by MRONJ, significantly improving the accuracy and safety of surgical interventions [63].  
The review notes that surgeries guided by autofluorescence offer success rates like those achieved by tetracycline fluorescence-guided surgeries, especially in ensuring mucosal integrity, reducing infection, and alleviating pain [63]. However, it also points out a notable gap in the literature concerning the standardization and broader clinical adoption of autofluorescence technology for MRONJ treatment. This indicates a pressing need for more research and development to fully integrate and standardize autofluorescence-guided procedures in clinical practice, ensuring a consistent and reliable approach to MRONJ surgical management [63].  
In the realm of surgical intervention for MRONJ, the use of human amniotic membrane (hAM) is emerging as a promising reconstructive material. While one study highlighted hAM's potential in reducing post-surgical pain [25], a review encompassing five studies with 91 patients revealed a high success rate (91.2%) for hAM in surgical therapy [64]. Although these findings indicate hAM's efficacy and potential for improving patient comfort, they also underline the need for more extensive research to fully grasp its long-term benefits and recurrence rates after MRONJ treatment [25; 64].
# Efficient Multi-Objective Simulation Metamodeling for Researchers
## 3 Experimentation
### Procedural Steps  
We compare the performance of MO pairs on each simulation model separately, as each such case study corresponds to a different scenario. There are four scenarios in total. Each case study used four time limits, so from an optimization standpoint, there are four problem instances per case study, for sixteen total across all case studies. Each individual experiment specification is run fifty times with the same setup, as is required for stochastic experimentation.  
More specifically, each case study covers all possible combinations of the following:  
* Two metamodel options (Gaussian regression, Neural network);
* Two optimizer options (Basic, Genetic algorithm);
* Four time limits (as determined for each simulation problem).  
The selection of time limits is different for each simulation model. The lowest time limit corresponds to the time required to carry out the FEs corresponding to the initial sample. For all experiments, initial sample sizes are 5% of the simulation model's input space, although this can be user-defined. The initial sample is uniformly and randomly distributed via Latin hypercube sampling.  
The experiments are run sequentially, from the initial sampling until the time limit is reached or, for the Basic optimizer only, if the stopping condition is reached before the time limit. As the test environment uses FE records instead of obtaining objective values from running the simulation model directly for each required set of inputs, the real-world time taken for an experiment will be shorter than the time limit, as the time limit accounts for the full FE time costs. For each completed experiment, the final Pareto front is stored. All Pareto fronts for a given case study are evaluated as a set, as the metrics used to evaluate MO pair performance are all normalized.  
Three metrics, normalized hypervolume, normalized Hausdorff distance and normalized crowding distance, are used to evaluate the obtained Pareto fronts, which, for consistency, use as a reference the Pareto front generated from the exhaustive FE record. This optimal Pareto front, corresponding to the specific simulation model problem, remains the same whenever the simulation model is referenced, ensuring that all metrics have a common benchmark across different sets of experiments.
## 2 Materials and Methods
### Data Collection
#### 2.2.3 Satellite Imagery  
Sentinel-2 imagery was acquired from the Copernicus Open Access Hub [76]. The Sentinel-2 constellation provides 13-band multispectral images at a 5-day interval. The experimental dataset contained 25 cloud-free Sentinel-2 images representing each month of the year from December 2017 to December 2019. Sen2 Cor [77] was used to convert top-of-atmosphere radiance to surface reflectance values. The surface reflectance images were then used to generate eight spectral (vegetation condition and soil moisture) indices (Table 1), utilizing spectral bands 2, 3, 4, 8 (10 m spatial resolution), 9 (60 m spatial resolution) and 11 (20 m spatial resolution).
## 4 Discussion
### Contributions and Limitations of ISC Data Construction  
Our ISC construction methodology offers several significant contributions to urban monitoring and analysis. First, the spatial aggregation approach from 30 m binary GAIA data to 1 km continuous coverage data addresses a critical gap in urbanization monitoring. Traditional binary classification approaches, while effective for identifying the presence or absence of impervious surfaces, fail to capture the subtle gradations of urban development intensity, particularly in peri-urban areas where sparse development patterns are common [25]. Our ISC method provides sub-pixel continuity that better reflects the heterogeneous nature of urbanization processes, especially valuable for characterizing low-density development and gradual urban expansion patterns. Also, the temporal continuity maintained throughout the 35-year period (1985-2020) provides unprecedented insights into urbanization dynamics. In contrast to many existing products that rely on discrete time points or have temporal gaps, our annual ISC dataset enables the detection of subtle  
Figure 13: Comparison of ISC with other global urban products over the past years. **(a)** Time series comparison of annual impervious surface area trends [24]. **(b)** Correlation analysis of ISC with GISA from 700 random sampling points (1985–2019).  
year-to-year variations in urban expansion rates and intensity changes. This temporal granularity is particularly important for understanding policy impacts, economic cycles, and environmental responses to urbanization.  
Despite these contributions, several limitations must be acknowledged. The spatial aggregation process, while providing continuity, inevitably results in some information loss compared to the original 30 m resolution. Small, isolated impervious patches may be averaged out within the 1 km grid, potentially underestimating urban fragmentation in some areas. This smoothing effect may be particularly pronounced in regions with highly dispersed settlement patterns or complex urban morphologies [26].  
The dependency on GAIA source data introduces inherited uncertainties. While GAIA demonstrates high overall accuracy (>90%), regional variations in classification performance may propagate through our aggregation process. Cloud contamination, seasonal variations in satellite imagery, and the challenges of distinguishing between different types of impervious surfaces in the source Landsat imagery can introduce localized errors. Additionally, the assumption of irreversible impervious surface growth, while generally valid, may not capture all real-world scenarios. Urban renewal projects, building demolition, or conversion of paved areas back to vegetation can result in decreases in impervious surface coverage that our methodology may not fully represent. This limitation is particularly relevant for mature urban areas undergoing redevelopment or cities implementing green infrastructure initiatives [27].
## 4 Results and Discussions
### Evaluation Metrics
#### 4.1.7 Root Mean Square Error  
The root mean square error is used to measure error value. It is defined as:  
\[Root\;\text{Mean Square Error}=\sqrt{(1/n)\sum_{k=0}^{n}(Y_{i}-y_{i})^{2}} \tag{12}\]  
\(Y_{i}\) and \(y_{i}\) indicate two different entities, with one representing the actual ranking, whereas the other represents the outcomes of the ranking by experiments. In the above equation, the actual ranking is basically the experts' recommendation which is denoted by '\(Y_{i}\)' whereas '\(y_{i}\)' denotes the system's prediction.
## 4 Proposed Protocol Procedure
### Authentication and Key-Exchange Phase
\(\mathrm{S3}=\mathrm{H}(\mathrm{A}\ |\ \mathrm{S2}\ |\ \mathrm{ACi})\)  
In the authentication mechanism, the value A serves as a primary parameter and is determined through calculations involving the private parameters \(\mathrm{N_{i}}\) and \(\mathrm{AC_{i}}\), along with \(\mathrm{S_{1}}\). Through the utilization of ECDH protocol features, the protocol's mechanism defines circumstances in which the node is not required to include the value of n in its message to the server. Furthermore, the secure calculation of this value on the server side is guaranteed according to the ECDH protocol. During the mutual authentication process conducted by both the node and the server, this mechanism serves as an effective factor in implementing security policies for the security of the protocol process. Furthermore, besides computing the value of \(\mathrm{N_{i}}\), the server needs to recalculate the values of \(\mathrm{S_{2}}\) and \(\mathrm{S_{3}}\) with the correct data available to obtain the parameters \(\mathrm{A}\), \(\mathrm{N_{i(X)}}\), and \(\mathrm{S_{1}}\). The parameters \(\mathrm{S_{2}}\) and \(\mathrm{S_{3}}\) are directly linked to the validation and confirmation of the node's identity authorization, and the correctness of recalculating these parameters proves the validation of the node's authorization. The noteworthy point is that only parameters \(\mathrm{S_{1}}\) and \(\mathrm{S_{3}}\) are encrypted by the node using the dynamically generated symmetric key (\(\mathrm{N_{i}(x)}\)). Finally, the node sends parameters \(\mathrm{S_{1}}\), \(\mathrm{S_{3}}\), \(\mathrm{PU}_{\mathrm{NODE}}\), and K (The K parameter is not included in the encrypted message) to the server in an encrypted form. The public key of the node is used to identify the identity of the message sender.
###### Abstract  
The impact of tropical cyclones on the operation of bridges and railways are mostly dependent on the forecast wind speed trend (upward, downward or staying steady) at present in Hong Kong. There are requests to forecast the exceedance of wind speed thresholds for such operations with a lead time of many hours ahead. This study considers the technical feasibility of forecasting the wind speed along a recently built bridge in Hong Kong with a coupled mesoscale numerical weather prediction (NWP)-computational fluid dynamics (CFD) model. This bridge features numerous anemometers where the coupled model can be verified. It is found that these two tropical cyclone cases are very challenging, especially in representing the wind structure of the cyclone because of its relatively small circulation. As such, the timing of the maximum wind could be 2 to 4 h earlier than the actual observation. The maximum wind speed from CFD modeling could be higher than that from the NWP model alone by 5 to 10 m/s, which shows that CFD modeling could add value in the forecasting of wind speed exceedance, although the maximum simulated value is still lower than the actual observation by as much as 10 m/s. As a result, while the general wind speed trend may be forecast, the exceedance of definite values of wind speed limits is still practically rather challenging, given the present two cases of tropical cyclones.  
numerical weather prediction (NWP); computational fluid dynamics (CFD); large eddy simulation (LES); tropical cyclones; bridge; wind hazards +
Footnote †: journal: Medical Physics
## 2 Indoor Experiment and Establishment of Finite Element Model
### Finite Element Model Verification
#### 2.3.2 Model Verification under Fatigue Loading  
According to the results of static loading, the established finite element model under fatigue load was verified to see whether it matched with the experimental data.  
The relative displacement of rail to track slab (Figure 12a), and the base concrete stress (Figure 12b) were verified after a fatigue load of 2, 4, 6, and 8 million times.  
Figure 11: Diagram of stress of the upper concrete of the base plate. (**a**) Longitudinal stress; (**b**) lateral stress.  
Figure 10: Load loading method.  
After 2 million fatigue loads, the vertical displacement of the rail directly below the loading point relative to the track slab had a maximum error between the calculation and measurement with 6.8%. As the fatigue load increased to 4 million times, the error was 12.5%, reaching a maximum. At a fatigue loading application of 6 and 8 million times, the maximum error increased to 16.2% and 20.0%, respectively.  
With regards to the longitudinal stress of the upper concrete layer of the base plate just below the loading point (Figure 12b), when the fatigue force was loaded for 2 million times, the maximum error between the two situations was 17.2%, for 4 million times the maximum error was reduced to 15.4%, and for 6 and 8 million times, the maximum error was 12.5%, and 15.0%, respectively.  
By comparing the results, the fatigue-loaded numerical model analyzed the mechanical behavior of CRTSIII SBT system under HHT force very well.
## 2 Design of Base Cells
### Topology Optimization and Sensitivity Analysis
 \tag{12}\]  
where \(\delta(\Phi)\) is the derivative of \(H(\Phi)\)[20], and the conjugate equation can be established:  
\[\int_{V}\varepsilon_{pq}^{*}(u^{(ij)})D_{pqrs}\varepsilon_{rs}^{*}(\dot{v}^{( ij)})H(\Phi)dV=\int_{V}\varepsilon_{pq}^{0(ij)}D_{pqrs}\varepsilon_{rs}^{*}( \dot{v}^{(ij)})H(\Phi)dV \tag{13}\]  
It was known from the equivalent equation that the derivative of \(a(u,v,\Phi)\) and \(\jmath(v,\Phi)\) with respect to time \(t\) are equal. By combining Equations (11)-(13), it yields:  
\[\int_{V}(\varepsilon_{pq}^{0(ij)}-\varepsilon_{pq}^{*}(u^{(ij)}))D_{pqrs} \varepsilon_{rs}^{*}(v^{(ij)}))V_{n}|\
abla\Phi|\delta(\Phi)dV=\int_{V} \varepsilon_{pq}^{*}(\dot{u}^{(ij)})D_{pqrs}\varepsilon_{rs}^{*}(v^{(ij)})H( \Phi)dV \tag{14}\]  
Substituting Equation (14) and the velocity field in Equation (2) into Equation (10), we can have:  
\[\frac{\partial D_{ijkl}^{H}}{\partial t}=-\frac{1}{|V|}\int_{V}(\varepsilon_{ pq}^{0(ij)}-\varepsilon_{pq}^{*}(u^{(ij)}))D_{pqrs}(\varepsilon_{rs}^{0(kl)}- \varepsilon_{rs}^{*}(u^{(kl)}))\varphi(x)\alpha^{\prime}(t)\delta(\Phi)dV \tag{15}\]  
Based on the chain rule, the derivative of elasticity tensor \(D^{H}\) with respect to time \(t\) can be computed:  
\[\frac{\partial D_{ijkl}^{H}}{\partial t}=\frac{\partial D_{ijkl}^{H}}{ \partial\alpha}\cdot\alpha^{\prime}(t) \tag{16}\]  
By comparing the corresponding terms in Equations (15) and (16), the derivative of elasticity tensor \(D^{H}\) with respect to design variables \(\alpha\) can be obtained by:  
\[\frac{\partial D_{ijkl}^{H}}{\partial\alpha}=-\frac{1}{|V|}\int_{V}( \varepsilon_{pq}^{0(ij)}-\varepsilon_{pq}^{*}(u^{(ij)}))D_{pqrs}(\varepsilon_{ rs}^{0(kl)}-\varepsilon_{rs}^{*}(u^{(kl)}))\varphi(x)\delta(\Phi)dV \tag{17}\]  
Similarly, the sensitivity of the volume constraint can be derived by:  
\[\frac{\partial G}{\partial\alpha}=\frac{1}{|V|}\int_{V}\varphi(x)\delta(\Phi)dV \tag{18}\]  
After the sensitivity of the objective function and the volume constraint are obtained, the OC algorithm [47] can be used to iterative optimize the cellular structure according to the sensitivity information.
## 4 Stability Analysis  
### Stress Distribution Characteristics of the Surrounding Rock  
The stress distribution of the surrounding rock in the retrieval area of the Beiyi quarry mined to the 0 m and \(-\)120 m levels is shown in Figure 6. In order to clearly reveal the state of stress distribution, only the stress vector field within 30 m above and below the mined level is given in the figure, and the color of each stress indicates the maximum principal stress value.  
When mining to the 0 m level, the stress concentration area is distributed in a circular pattern along the recovery area, mainly existing in the foot of the slope of the side gang of the open pit, the pit bottom area, and the side gang rock bodies of the north gang and the east gang. Among them, the stress concentration area where the maximum principal stress reaches 20 MPa is mainly concentrated in the north gang and east gang hill, ranging between E2 and E9 profiles, and the level of the stress concentration zone is about 30-40 m wide. The maximum value of the maximum principal stress is 24.7 MPa, and the return void at this location is narrow (the wide range of the return area cannot easily produce a stress concentration phenomenon). The overlying rock layer thickness at this location is large, the side slopes are high, and a large range of hollow surfaces exist in the rock bodies of the north gang and east gang involved. In addition, there is also a certain amount of stress concentration in the bottom plate of the mining area, but the overall amount is less than 20 MPa.  
When mining to a \(-\)120 m level, according to the test results of ground stress, the maximum principal stress at this level is about 18 MPa. The main area of stress concentration is located on the north side of the recovery area, the maximum value is about 40 MPa, and the stress concentration level of the bottom slab is not more than 30 MPa. It can be seen that the overall value is still not high when mining to this level.  
When mining to the \(-\)240 m level, at this time, west of E9 (i.e., Beiyi west area), mining has been completed, and the stress distribution is shown in Figure 7. This level includes two retrieval areas in the south and north, and the stress concentration area is also mainly present in the surrounding area of E9 and E11 profiles in the two retrieval areas. In these areas, the stress concentration level in the southern mining area is higher, and the maximum amount reaches 48 MPa, while there is no obvious stress concentration phenomenon on the north side.