esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Slip Model Inversion  
### Source Parameter Inversion  
Studying the focal mechanism and exploring the types of earthquakes can help deepen understanding of the nature of earthquake disasters and likewise facilitate the government in conducting post-earthquake disaster relief work that can be carried out smoothly [31]. The determination of focal depth is of great significance for evaluating earthquake disasters, determining the cause and dynamic environment of earthquakes, and judging the development trend and danger of aftershocks [32]. Based on the obtained line-of-sight lifting orbitoseismic deformation field (Figure 2), the coseismic deformation field had good coherence and the deformation area was completely covered in the image range. If all the observation data in the deformation area are involved in the inversion, the huge amount of data will reduce the inversion efficiency and may affect the accuracy of the inversion result [33]. Therefore, in order to improve the reliability of the inversion and enhance the spatial correlation of the observation, in this paper, we performed down-sampling processing on the InSAR deformation field, and the down-sampling results are shown in Figure 3.  
In this paper, based on the single fault and Okada elastic half-space dislocation model [34], we used the simplex algorithm for uniform inversion in order to make the simulated deformation value and the observation value best fit. According to the dislocation theory, there is a highly nonlinear relationship between the geometric parameters of the fault and the surface deformation. In the inversion process, the Okada uniform elastic half-space dislocation model was used to simulate the observed interference deformation field, and the fault parameters to be estimated include longitude, latitude, length, depth, strike, dip, and slip angle. The equation to be solved is:  
\[d=Gm+\varepsilon \tag{1}\]  
where \(d\) represents the observed value of surface deformation, \(m\) represents the slip parameters of faults (including strike, dip angle, slip angle, length, width, depth, and slip value), \(G\) is the Green's function matrix, and \(\varepsilon\) indicates observation error.  
We used the Multi-peak Particle Swarm Optimization (MPSO) algorithm [35] to determine the above parameters by searching for the optimal solution of the parameters; the algorithm has the characteristics of fast convergence speed and high computational efficiency. Based on previous studies of motion characteristics and geometric shapes, combined with the focal mechanism solution of the Yunnan Yangbi earthquake given by the Global Centroid Moment Tensor (GCMT) and the United States Geological Survey (USGS)  
Figure 2: The ascending and descending coseismic deformation field of the Yangbi Ms 6.4 earthquake: (**a**) the ascending coseismic deformation field; (**b**) the descending coseismic deformation field.  
and the coseismic deformation obtained from InSAR observations, we established the seismogenic fault of this Yangbi earthquake model.
###### Abstract  
Understanding the interdependencies among production, living, and ecological spaces (PLESs) is critical for sustainable regional development. Urban fringe areas, shaped by rapid urbanization and conflicting land-use demands, are particularly vulnerable to spatial tensions. This study analyzes the spatiotemporal dynamics and drivers of PLES conflicts in Zengcheng District, Guangzhou, a representative urban fringe region. Using land-use data from 2010 to 2020, the study applies the optimal parameter geographic detector, chosen for its ability to untangle complex spatial interactions, to quantify conflict intensity and identify key drivers. This method was chosen over other spatial analysis techniques due to its ability to effectively capture nonlinear relationships and interaction effects between variables, which traditional regression-based or spatial autocorrelation methods often fail to fully address. The results indicate that production and ecological lands dominated the landscape, while living space expansion slowed, leading to escalating conflicts, particularly in the southern and central regions. The PLES conflict index shows that severe conflict units rose from 0.89% in 2010 to 2.15% in 2020, despite over 80% of spatial units remaining stable. Moderate conflicts peaked in 2015 before declining, while stronger conflicts intensified, especially in rapidly urbanizing areas. Conflict hotspots were most pronounced in rapidly urbanizing zones, particularly at the interface of urban expansion and ecological conservation areas. Moreover, the driving forces behind these conflicts transitioned from economic and urbanization factors to a multifaceted interplay of natural and social determinants, underscoring the growing intricacy of spatial dynamics. These findings offer crucial insights into the mechanisms driving PLES conflicts, guiding urban planners and policymakers in developing strategies to balance competing land-use priorities. By quantifying conflicts and identifying key drivers, this study helps prioritize interventions that mitigate tensions between production, living, and ecological spaces, supporting policies that reconcile urban expansion with ecological preservation for sustainable development in urban fringe areas.  
PLES; spatial tensions; urban fringe areas; spatial-temporal evolution; optimal parameter geographic detector +
Footnote †: journal: Applied sciences
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 3 Effect of Cl\({}^{-}\) Corrosion on the Seismic Performance of Bridge Column
### Two-Parameter Seismic Damage Index
The damage index calculated by the M-Park damage model was smaller than that calculated by the Park-Ang damage model at small displacement, and larger than that calculated by the Park-Ang damage model at large displacement. From the perspective of durability, the Cl\({}^{-}\) corrosion played a non-negligible role on the ability of column to resist seismic load and the damage after its own dynamic response. It is clear from the curves of two damage models for specimens LL and LLE that the Cl\({}^{-}\) corrosion aggravated the damage indicators of column, which grew linearly with the increase of horizontal displacement and both were twice as high as the damage index of column in normal conditions. The damage curves of specimens MM, MME, LL, and LLE showed that the ductility of column increased with the growth of column height. In the case of small displacement, the impact of corrosion on the weakening of seismic  
Figure 37: Comparison of damage indicators between specimens MH and MHE. Figure 35: Comparison of damage indicators between specimens LL and LLE. Figure 36: Comparison of damage indicators between specimens MM and MME. performance of the column was relatively small. When the displacement was greater than 40 mm, the damage model index of the column in corrosive conditions ascended sharply and could be up to twice the damage index of the column in normal conditions at the peak of displacement. Excessive reinforcement ratio will lead to a significant increase in stiffness of the column and a reduction in its ductility. The MH column, although with a good level of dissipated energy, suffered from premature cracking of concrete and spalling of the protective layer compared with the MM column, which in turn exacerbated damage to the column. As a result, for the two-parameter seismic damage index, the specimen with middle height and medium reinforcement ratio could better take the deformation and energy dissipation into account simultaneously among the three types of specimens. Owing to the corroded effect of Cl\({}^{-}\) on reinforcement, the weakening and deterioration of the reinforcement section in the plastic hinge area of the column with a high reinforcement ratio was more severe, which resulted in a gradual decline of the maximum allowable displacement of the column in corroded conditions with the growth of the reinforcement ratio. That is, necessary precautions ought to be taken against the harsh conditions for columns with high reinforcement ratios.
## 2 Random Wind-Speed Model Based on Weibull Distribution  
It is very important to know how to simulate the random wind-speed model for studying the load-sharing characteristics of a planetary gear transmission system. This is because the input speed of the wind generator gearbox is determined by the random wind speed in a wind farm. The Weibull distribution is a two-parameter function commonly used to fit the wind-speed frequency distribution. The Weibull function provides a convenient representation of wind-speed data for wind-energy calculation purposes, and the function is commonly referred to as the wind-speed distribution. The probability density function of the Weibull distribution is given by:  
\[f(v)=\frac{kv^{k-1}}{c^{k}}\exp\left[-\left(\frac{v}{c}\right)^{k}\right], \tag{1}\]  
where \(v\) (\(v>0\)) is random wind speed, \(k\) is the Weibull shape parameter and \(c\) is the Weibull scale parameter.  
The two Weibull parameters are related by:  
\[k=\left(\sqrt{D(v)}/E(v)\right)^{-1.086}\text{and} \tag{2}\]  
\[c=E(v)/\Gamma\left(1+k^{-1}\right), \tag{3}\]  
where \(E(v)\) is the mean value of random wind speed \(v\), \(D(v)\) is the variance of random wind speed \(v\) and \(\Gamma()\) is the gamma function.  
From Equation (1), the probability function of the Weibull distribution is given by:  
\[F(v)=1-\exp^{-\left(\frac{v}{c}\right)^{k}}. \tag{4}\]  
In Equation (4), it can be concluded that the probability function of the Weibull distribution \(F(v)\) is nondecreasing and ranges from 0 to 1. Therefore, the random wind speed \(v\), subject to the probability function of Weibull distribution, is equivalent to the random number \(m\) subject to a uniform distribution function ranging from 0 to 1. The random wind speed \(v\) can be obtained by:  
\[m=F(v)=1-\exp^{-\left(\frac{v}{c}\right)^{k}}\text{ and} \tag{5}\]  
\[v=c[-\ln(1-m)]^{\frac{1}{k}}. \tag{6}\]  
For example, it is known that \(E(v)\) is 11 m/s and \(D(v)\) is 5.16 m\({}^{2}\)/s\({}^{2}\) for a wind field. According to Equations (2) and (3), it can be calculated that the shape parameter \(k\) = 5.6 and the scale parameter \(c\) = 12. Then, using Equation (6) and the Rand function in MATLAB software to generate the random number \(m\), the time-history curve of the random wind-speed model is obtained by correlating the wind-speed data with the sampling points, as shown in Figure 1.  
Figure 1: Time-history curve of a random wind-speed model.
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 3 TDS-OFDM-NOMA Scheme
### Underwater Acoustic Channel Model  
The UWA channel is considered a time-variant linear system with a time-varying impulse response of \(h(t,\tau)\); this impulse response describes the Doppler spreads and the channel multipath. For the UWA time-varying channel, the noise-free signal at the receiver side is described as a convolution of the channel impulse response and transmitted signal \(s(t)\). In UWA communications, there are different models of the UWA impulse response \(h(t,\tau)\) used to represent the UWA channel. The discrete multipath component models the UWA channel as [1; 25]:  
\[h(t,\tau)=\sum_{p=1}^{L}A_{p}(t)\delta\big{(}\tau-\tau_{p}(t)\big{)} \tag{12}\]  
where \(A_{p}(t)\) and \(\tau_{p}(t)\) are time-varying delays and amplitudes; \(L\) is the maximum length of the multipath components. \(\delta(t)\) is the Dirac delta function. In this paper, we assume, \(\tau_{p}(t)=\tau_{p}-at\); hence, the channel model affects the received signal with a dilation factor \(a\), and that factor is the same for all UWA channel multipaths. The received data frame over the UWA multipath channel can be represented as follows:  
\[y(t)=\int\limits_{-\infty}^{\infty}h(t,\tau)s(t-\tau)d\tau \tag{13}\]  
The received signal in the passband can be written as:  
\[\begin{split}\tilde{y}(t)=Re\left\{\sum_{p}A_{p}\left[\frac{1}{ \sqrt{N}}\sum_{k=0}^{N-1}s(k)e^{j2\pi k\Delta ft/t(t+at-\tau_{p})}+\sum_{k=N}^ {N+M}\frac{1}{\sqrt{M}}\sum_{k=N}^{N+M}c(k)\ e^{j2\pi k\Delta ft}\right]\times \\ g\big{(}t+at-\tau_{p}\big{)}\times e^{j2\pi f_{c}(t+at-\tau_{p})} \right\}+\tilde{n}(t)\end{split} \tag{14}\]  
where \(\tilde{n}(t)\) represents the total UWA channel noise; this noise includes the additive noise and the ambient noise. The received signal in the baseband version is satisfied by \(\left\{\tilde{y}(t)\times e^{j2\pi f_{c}t}\right\}\) and can be written as:  
\[\begin{split}\tilde{y}(t)=\frac{1}{\sqrt{N}}\sum_{k=0}^{N-1} \{s(k)e^{j2\pi k\Delta ft}e^{j2\pi af_{k}t}\times\left[\sum_{p}A_{p}e^{-j2\pi f _{k}\tau_{p}}g\big{(}t+at-\tau_{p}\big{)}\right]\}+\frac{1}{\sqrt{M}}\sum_{k =N}^{N+M}\{c(k)e^{j2\pi k\Delta ft}e^{j2\pi af_{k}t}\times\left[\sum_{p}A_{p} e^{-j2\pi f_{k}\tau_{p}}g\big{(}t+at-\tau_{p}\big{)}\right]\}+\tilde{n}(t)\end{split} \tag{15}\]  
Below, the acoustic propagation pass loss and the ambient noise will be addressed in detail. Over the UWA channel, each path \(T\sim\frac{T}{\big{(}1+\Delta t\big{)}}\). Each subcarrier practices a Doppler-made frequency shift \(e^{j2\pi\Delta ft/t}\), and it depends on the frequency of the subcarrier. The frequency-dependent Doppler shifts present severe intercarrier interference if an inefficient Doppler reparation is not achieved before the OFDM demodulation.
## Materials and Methods  
### Materials  
PEDOT:PSS (Clevios(tm) P, Heraeus Holding GmbH, Hanau, Germany) was used without further purification. The solid content, viscosity, and weight ratio of PEDOT to PSS were 1.2%, less than 150 mPa\(\cdot\)s, and 1.2.5, respectively. 1,2-Epoxybutane, 1,2-epoxyhexane, 1,2-epoxydodecane and 1,2-epoxyhexadecane, 2-propanol (IPA, 99.5%), dimethyl sulfoxide (DMSO, 99.0%), and silver paste (?75%) were purchased from Sigma-Aldrich.  
XPS and FT-IR spectra were recorded on a K-Alpha instrument (Thermo Scientific), and from KBr pellets on a Perkin Elmer spectrometer, respectively. The TGA (TGA 4000, Perkin Elmer) was carried out under a nitrogen atmosphere (nitrogen flow rate: 20 mL/min) while the temperature was increased from room temperature to 800 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min. Resistance of the films was monitored using a four point probe (M4P-302, MSTECH) setup with a source measurement unit (Keithley 2400). The values were converted into sheet resistance by taking correction factors into account [17]. For films coated on a glass, thickness was also measured by a surface profilometer (Alpha Step, P-10 of KLA-Tencor), and conductivity was calculated by using the following equation:  
\[\sigma=1/\text{ Rs}\cdot\text{t}\]  
where \(\sigma\) is conductivity, Rs is surface resistance, and t is thickness.  
Surface resistances of the films coated on a flexible film (PET), which are over \(10^{3}\)\(\Omega\)/sq, were measured using a surface resistance meter MODEL ST-4 (SIMCO Japan). A two point probe (289 True RMS Multimeter, Fluke) was employed to measure linear resistance of films for the bending test. Surface properties of the films were evaluated by measuring the contact angle of the water droplet by utilizing a drop shape analyzer (DSA30, Kruss GmbH). Surface morphology was studied by atomic force microscopy (AFM) (XE-100 of Park Systems).
# Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking
## 5 Performance Evaluation
### Performance Criteria
#### 5.1.2 Handover Latency  
Handover latency is a critical factor in mobile networks where the _mobile node (MN)_ moves from one network coverage area to another. This criterion measures the total time required to transfer the active connection of the MN from one access point (or domain) to another without interrupting communication.  
Handover latency is defined as the delay between the moment the mobile node (MN) leaves the range of the old station or domain (access point) and the moment it is fully reconnected to the network through the new access point. This process involves several steps:  
1. Movement detection: The network or the MN detects that the current connection is becoming weak or unstable.
2. Handover decision: A new access point or route is selected for the MN.
3. Data transfer: The context of the active session is transferred to the new access point.
4. Reconnection: The MN is connected to the new domain or access point, and the session is restored.  
The total time for these steps constitutes the handover latency, and it is essential to minimize it to avoid a degradation of quality of service (QoS).  
In the _RI-SDN_ approach, handover latency is considered during route selection. The network performs real-time calculations to select routes that minimize the end-to-end latency, including during handovers. Equation (11) gives the expression of handover latency.  
\[L_{handover}=t_{detection}+t_{decision}+t_{anchoring}+t_{transfer}+t_{ reconnection} \tag{11}\]  
* \(L_{handover}\): Total handover latency.
* \(t_{detection}\): Time to detect that the MN needs to change domain.
* \(t_{decision}\): Time to select a new access point and route.
* \(t_{anchoring}\): Time required to run the mobility anchor selection algorithm.
* \(t_{transfer}\): Data transfer delay.
* \(t_{reconnection}\): Time to reconnect the session.
Towards this, the main contributions of our work are as follows:  
* We present an FL use-case for a healthcare provider with geographically distributed branches, highlighting the challenge vectors of resource fitting and deploying multiple FL workflows. These challenges are fundamental and remain relevant across numerous other applications considering FL adoption. * We document the experimental setup designed for rapid and reproducible benchmarking of the FL infrastructure and deployment configuration. This includes con tainerizing the FL framework (Flower), using Kubernetes as the cluster orchestrator, employing a testing toolkit (Frisbee) to adjust Kubernetes' placement policies, and extensively monitoring infrastructure, virtual, and FL execution metrics. * We conduct a comprehensive experimentation study, systematically analyzing performance, scalability, and resource efficiency. We evaluate different combinations of orchestrator policies and FL configurations using popular ML/FL datasets (MNIST [18] and CIFAR [19]) and frameworks (PyTorch [20] and TensorFlow [21]). We explore various resource configurations, server/client co-location, pod placement policies, and investigate the effects of running multiple FL training workloads simultaneously. The remainder of this paper is organized as follows: In Section 2, we provide a short introduction to Federated Learning. In Section 3, we describe a high-level problem statement, including a critical evaluation of the associated challenges that exist nowadays. In Section 4, we provide an overview of our testing infrastructure, tools, and experiment methodology. In Section 5 we provide an in-depth performance analysis for a single workflow, whereas in Section 6 we provide an analysis for two concurrently executed workflows. Section 7 presents a literature review including a critical evaluation of the strengths and limitations of existing approaches. Finally, in Section 8, we summarize the key findings of our experiments, discuss their implications on Federated Learning deployments, and suggest future research directions.
## 2 Materials and Methods
### Methodology
#### 2.4.3 Sample Transferring Strategy for Earlier Years  
The trained random forest model looks perfect to be used for land cover mapping directly in earlier years (before 2011); however, the seasonal surface reflectance composites for each year were not as consistent as expected (see Figure 2) and the accuracy by directly using the trained model was subsequently degraded. Figure 5 gives an example of land cover classification based on machine learning transferring. The model used in 2010, 2005, and 2000 was the same one that was trained by the samples collected in Section 2.4.2. It is obvious that the results are not temporally consistent, especially for barren land and forests.  
Figure 4: Illustration of sampling strategy from land cover maps of 2011–2015.  
In addition, the large number of samples did not allow manual checking. Therefore, a sample transferring strategy for earlier years needs to be established to lower the labor and time costs while guaranteeing the sampling accuracy. The major procedures for the sample transferring are as follows:  
Figure 5: A classification example at HRB based on machine learning model transferring.  
1. The trained random forest model was applied to each year's seasonal surface reflectance composites to produce a land cover map as a reference.
2. The training samples in Section 2.4.2. were compared with the land cover reference map from step \(1\) and the unmatched samples were removed from the sample collection.
3. The surface reflectance composites were automatically checked to remove those samples whose surface reflectance was abnormal, such as noise, cloud contamination, and cloud shadow.
4. Finally, if the number of samples was lower than the requirement, some new samples would be manually added to correct the amount of the samples. Although some manual work is required, only a few samples need to be added, which greatly reduced the labor and time costs while compared to all-labor sampling.  
Based on the above procedures, the samples for every year before 2011 were collected and were subsequently put into the random forest model for training. Finally, the land cover maps before 2011 were made.
## 8 Conclusions  
With the presence of a massive number of advanced network devices, CW optimization can now be examined using DRL methods. DRL is suitable for optimizing wireless networks as it deals with smart software agents that take action in a specific environment to maximize the reward. We have proposed DCWO, a method that takes advantage of the principles of DRL for correct CW configuration for 802.11 ax under different network conditions. It makes use of a trainable control algorithm, DDQN. Our experiments have shown that DRL can be used successfully for dealing with CW optimization problems. We have evaluated our proposed scheme in terms of total network throughput, instantaneous throughput and fairness in static as well as dynamic scenario and cumulative reward. The simulation  
Figure 11: Cumulative Reward vs. No. of Time Frames.  
Figure 10: Network Fairness Index for Dynamic Scenario.  
results show the effectiveness of our scheme as it yields overall better network performance than those obtained from CCOD. In terms of total network throughput DCWO gives 28% and 23% better performance than CCOD in static and dynamic scenarios, respectively. DCWO also gives around 10% better instantaneous throughput than the CCOD. In terms of fairness, DCWO with DDQN achieves almost near to optimal fairness in static scenario and better than CCOD with DQN and DDPG in dynamic scenarios. Similarly, cumulative reward achieved by DCWO with DDQN is much better than DQN with CCOD and almost the same with CCOD with DDPG; however, the uptrend of DCWO is better.  
Conceptualization, K.A. and B.K.; methodology, K.A. and B.K.; writing--original draft preparation, K.A.; writing--review and editing, G.-Y.K. and B.K.; supervision, B.K. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by Basic Science Research Program through the National Research Foundation(NRF) Korea funded by the Ministry of Education (No. 2018R1D1A1B07049758).  
The authors declare no conflict of interest.