esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Key Distribution Networks: Challenges and Future Research Issues in Security
## 2 Background to Quantum Key Distribution (QKD) Networks  
The QKD network is used to extend the range of the QKD protocol, and it consists of several static quantum nodes that have complete quantum capabilities (e.g., generating a single photon/entanglement state, storing qubits, and performing the quantum unitary operation). The quantum nodes execute the QKD protocol (e.g., the BB84 protocol) to distribute secure keys (also called local keys) between the neighboring nodes, and then, the hop-by-hop manner is adopted to assist the remote end users/applications to distribute unconditional security session keys.  
The QKD network comprises quantum nodes and quantum links, and the framework of the QKD network can be divided into three layers: a communication layer, a key management layer, and a quantum layer (shown in Figure 1). The communication layer (the top layer) manages the routing tasks and provides the application interface (API) to allow the end users/applications to access the secure session keys generated by QKD protocols. The middle layer--the key management layer--is responsible for managing key generation and storage to effectively utilize the resources of quantum devices in the quantum layer. The quantum layer (the bottom layer) comprises several quantum devices and an authenticated public classical channel, and it is responsible for executing the QKD protocol for sharing local keys with neighbor nodes.  
Because of limited spaces, we only focus on the quantum layer technologies to introduce quantum nodes and quantum links, and the details of classical technologies are not provided here. This study introduces the following two main components.
###### Abstract  
In this study, the effective value of low-frequency (50 Hz) and high-frequency (700-2500 MHz) broadband electric field exposures was measured for 24 h in four different selected environments in a house. All the statistical values of 1000 data points recorded with two measuring devices for 24 h in each environment are calculated, the most appropriate curves are fitted to the data, and the curves are plotted by expressing their changes with respect to time. All statistical and parametric values of the density and cumulative probability functions of the curves are calculated and plotted. In broadband measurements, the broadband is divided into fifteen sub-bands, but the data are available in only six of these sub-bands. The six-layer human head model is created by using the middle frequencies of the six sub-bands used, and taking the permittivity (\(\epsilon_{e}\)) and conductivity (\(\sigma\)) of each layer into consideration. The specific absorption rate (SAR) value in the brain is calculated by using the total transmission coefficient of six layers. The SAR value surrounding the head is obtained. These SAR values are interpreted by considering the Federal Communications Commission (FCC) and the Community European (CE) occupational and general public SAR limits.  
SAR; broadband; cumulative probability; curve fitting; electromagnetic field; probability density function Article
## 1 Introduction
For this aim, this paper compares two different approaches. The first approach (_Approach A_) is based on a classical approach, where only one MOEA is used to obtain all non-dominated solutions. In the second one (_Approach B_), the best non-dominated solutions of all algorithms are selected.Furthermore, results are good for different time windows. The main contributions of this work are as follows:  
* We propose a new evolutionary algorithm approach where different well-known MOEA algorithms collaborate to solve multi-objective problems. This is the main contribution of this work, since, to the best of our knowledge, it is the first time that several MOEAs operate together to construct the Pareto front. * We apply this approach to the real-world problem of generating trading rules for calculating technical indicators parameters for the FOREX exchange market. * We conduct a comparison with approaches that use only one MOEA algorithm and show that our proposal of combining different algorithms covers a more extended part of the Pareto front and obtains non-dominated solutions. The rest of the paper is structured as follows. Section 2 briefly reviews related work on Evolutionary Computation and finance. Section 3 explains the method proposed in this paper. Section 4.1 presents the real-time investment framework. Section 4.2 shows the performed experiments. Section 4.3 presents results and discussion. Finally, conclusions are presented in Section 5.
## 1 Introduction
### Problem Statement and Motivation
Effective routing protocols ensure reliable data transmission, minimize energy consumption, and balance the load among DFHs [26; 27]. Conversely, inefficient routing can lead to imbalanced load distribution, increased latency, and excessive energy consumption. Adaptive routing protocols that consider factors such as SN distance to the sink node, RE levels, and data aggregation potential are essential for maintaining network performance under varying conditions [28]. To this end, efficient DFH selection, data fusion grouping optimization, and adaptive routing are foundational to addressing the challenges of IoT networks. Achieving energy-efficient, reliable, and scalable solutions is paramount for enabling IoT's full potential across diverse applications,from smart cities to industrial automation and healthcare monitoring. However, these problems are intertwined and demand holistic solutions that integrate data fusion grouping and routing optimization. Emerging machine learning and AI-driven techniques offer the potential to address these challenges, but their application to resource-constrained, dynamic IoT environments requires further exploration. This paper aims to address these challenges by exploring state-of-the-art techniques for DFH selection, data fusion grouping optimization, data fusion, and adaptive routing. By integrating emerging optimization methods and evaluating their effectiveness in real-world scenarios, the study seeks to contribute to the development of efficient, scalable, and adaptive solutions for IoT networks, unlocking their full potential across various domains.
## 3 Materials and Methods
### Evaluation Methodology
#### 3.6.1 Attack Impact Assessment  
The impact of attacks was measured using the following metrics:  
* **Trajectory Analysis**: We track and compare the vehicle's actual trajectory against the planned route, calculating point-to-segment distances to measure route deviation. This analysis is particularly important for GPS spoofing attacks where perceived and actual positions may differ significantly.
* **Control Stability**: We analyze steering, throttle, and brake commands through detailed time series analysis. For steering bias attacks, we perform statistical analysis of steering angle distributions to detect anomalous patterns.
* **Sensor Performance Metrics**:
* RGB Camera: Noise percentage measurements during salt-and-pepper attacks
* DVS: Event count tracking to detect abnormal spikes in event generation
* Depth Camera: Mean depth measurements to identify tampering
* LiDAR: Point cloud density analysis to detect phantom objects
* **Defense Effectiveness**: Comparison of performance metrics with and without defensive measures enabled, including
* Adaptive median filtering for RGB noise,
* Spatial clustering analysis for DVS event flooding,
* Gradient-based analysis for depth tampering,
* Density-based filtering for LiDAR attacks, and
* Rate limiting for DoS attacks.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 5 Bridge Digital Twin Modeling Using Multi-Source Heterogeneous Data
### Establishing Bridge Digital Twin for Smart O&M
To ensure data accuracy and minimize environmental interferences, the UAV flight paths and sensor configurations were carefully calibrated. Following data collection, rigorous preprocessing was conducted to enhance data quality. The processed datasets were analyzed using advanced computational models tailored to each data type. The YOLOv8 algorithm was employed to detect surface defects, accurately identifying cracks and spalling in multiple locations. The PointNet model analyzed the 3D point cloud data, revealing structural deformations, including a vertical displacement of 12 mm at the mid-span. The LSTM model processed time series sensor data, predicting vibration amplitude trends and identifying potential fatigue under traffic loads. These processed datasets were integrated into the DK-DTM framework and further enriched with an expert knowledge model, incorporating historical maintenance records and design specifications for the Hedong Bridge. This comprehensive integration facilitated an in-depth assessment of the bridge's health risks and generated precise maintenance recommendations. The proposed framework effectively identified high-priority areas requiring immediate repair and confirmed the presence of early-stage fatigue risks. This case study of the Hedong Bridge substantiated the feasibility and reliability of the proposed methodology in addressing critical challenges in structural health monitoring. The detailed experimental workflow is depicted in Figure 9.
# Studying the Cable Loss Effect on the Seismic Behavior of Cable-Stayed Bridge
## 3 The Effect of Incremental Cable Loss on the Seismic Behavior
### Earthquake Loading  
In order to evaluate the effect of seismic load on the target bridge, the El-Centro earthquake, widely used in many studies, was used to conduct seismic analysis [20; 21]. The ground acceleration of the El-Centro earthquake was integrated to convert it into ground displacement, and then applied as loading to the supports of the numerical analysis model in both the longitudinal and transverse directions (as shown in Figure 7). For massive structures such as bridges, accurate analysis requires SSI (Soil-Structure Interaction) analysis that takes into account the characteristics of the soil or rock under the structure during seismic analysis [22; 23]. However, the effect of the foundation was not considered in this study since the effect of the foundation is not significant in evaluating the impact of cable loss under seismic conditions.  
The seismic load was applied after the cable's loss behavior stabilized (after 40 s of the last cable breakage), and the cable loss scenario was set as presented in Table 4. The effect of the earthquake on the bridge was evaluated using changes in the compressive force of the girders generated by the earthquake and changes in cable tension.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Case** & \multicolumn{2}{c}{**Element No. of Lost Cables**} &
\begin{tabular}{c} **Quantity** \\ **(EA)** \\ \end{tabular} \\ \hline  & 0 & - & 0 \\ \hline  & 1-1 & 6136 & 1 \\  & 1-2 & 6136, 6135 & 2 \\
1 & 1-3 & 6136, 6135, 6134 & 3 \\  & 1-4 & 6136, 6135, 6134, 6132 & 4 \\  & 1-5 & 6136, 6135, 6134, 6132, 6131 & 5 \\ \hline  & 2-1 & 6136, 6137 & 2 \\
2 & 2-2 & 6136, 6137, 6135, 6138 & 4 \\  & 2-3 & 6136, 6137, 6135, 6138, 6134, 6139 & 6 \\ \hline  & 3-1 & 6172 & 1 \\  & 3-2 & 6172, 6171, & 2 \\
3 & 3-3 & 6172, 6171, 6170, & 3 \\  & 3-4 & 6172, 6171, 6170, 6169 & 4 \\  & 3-5 & 6172, 6171, 6170, 6169, 6168 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantity of cable loss per case.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 3 Classification and Evolution of Interference Management Strategies in B5G Networks
### Integration and Interplay Among Interference Management Strategies in B5G Networks  
Modern B5G networks are expected to support UD connectivity, mMTC, and dynamic UM, all of which require the concurrent application of multiple IM strategies. While individual IM frameworks such as D2D communication, HetNets, and AI-driven control mechanisms have shown promising results, their joint integration offers significantly more robust and adaptive IMI.  
For example, D2D communication within a HetNet architecture can benefit from RL-based MOS algorithms that dynamically determine whether a UE should operate in direct D2D mode or via a BS, depending on interference levels and link quality [148]. Similarly, AI-assisted RA can be leveraged in HetNets to coordinate inter-tier and co-tier interference by learning from historical data and adapting in RL [151].  
However, hybrid IM approaches also introduce trade-offs. The complexity of coordination between centralized macro-cells, distributed SCs, and D2D nodes increases signaling overhead and demands accurate CSI [116]. Furthermore, combining multiple strategies often requires cross-layer optimization, where decisions made at the MAC or PHY layer must align with application-layer service requirements  
Therefore, while standalone IM techniques provide modularity and ease of deployment, integrated frameworks enable more efficient SR, improved EE, and greater scalability at the cost of increased implementation complexity and coordination latency [150; 156; 157]. Future B5G networks may benefit from adaptive orchestration platforms that intelligently switch between standalone and hybrid IM modes based on traffic demands, mobility patterns, and environmental constraints.
# Modeling and Parametric Simulation of Microplastic Transport in Groundwater Environments
## 1 Introduction
The analysis of the samples obtained from wells (<65 m) and springs in the karst terrains of the Salem Plateau and Drift area in Illinois, U.S., indicated the presence of MPs in 16 out of 17 samples, which were tentatively assumed to have flowed from septic tank effluents [23]. Foreign studies have been actively conducted on MPs in marine ecosystems and surface water (lakes, rivers, etc. ), and soil-related research is becoming increasingly popular [24; 25]. However, both domestic and foreign studies on MPs in groundwater are limited to the drinking water of karst terrains and groundwater origins [26; 23; 27]. In several countries, including the U.S., Germany, Canada, and Denmark, MPs were detected in drinking water of groundwater origin, as well as in spring water products (bottled water) [2; 7; 13]. Up to 30,000 pore-scale MPs (20 to 50 \(\upmu\)m) were detected in hyporheic zones where surface water and groundwater actively mixed, and the possibility of MPs entering groundwater through these mixing sections has been reported [13]. Furthermore, the possible influx of MPs into groundwater through soil due to soil characteristics (pores and fractures), biological activity, human activity, and precipitation has been suggested in studies on the transport of MPs in soil [28; 29]. Most studies on the subsurface transport of MPs are based on the transport of colloids or nanoparticles, using the traditional advection-dispersion equation for dissolved species, mainly involving indoor experiments [30; 31]. Despite the growing worldwide interest in MPs, the existing research on the transport of MPs is confined to marine and terrestrial environments that only include coastal areas and surface water [5; 9], with very few studies being related to actual soil [13] and groundwater environments [13; 23; 27]. The extensive physical and chemical properties and size of MPs that are smaller (or larger) than colloids but larger than nanoparticles are not reflected in such studies, and the application of existing models is limited [29]. As mentioned above, in the conventional evaluation of the transport of MPs, advection-dispersion equation models for evaluating the transport of colloids or nanoparticles in unsaturated and saturated media have been used. However, the sizes of MPs in real-life environments vary widely from sizes smaller to larger than colloids, and the conventional advection-dispersion equation for dissolved species is insufficient for modeling the transport of MPs. In addition, existing models such as MT3D [32] and RT3D [33] in MODFLOW [34], which can simulate the transport of contaminants in groundwater, are specifically used to evaluate the diffusion of dissolved species and may not be suitable for evaluating the transport of MPs. Therefore, in this study, the existing transport models are analyzed, and the details of their main parameters are identified.
## 2 Place- and Person-Based Accessibility Measures
### Person-Based Measures  
Three main types of person-based measures address the gaps of the place-based measures: the _utility-based_, _individual integral_ and _space-time_ measures ([11; 17]; Table 1). The _utility-based measure_ (e.g., refs. [9; 29; 30]) grounds on economic theories and calculates accessibility as the maximal economic utility individuals can get from the access to spatially distributed opportunities based on their perception of the utility of the options at their disposal. The _individual integral measure_ (e.g., refs. [31; 32; 33]) is a gravity-based measure adjusted to be person-specific. The adjustment is performed either by disaggregating data and analysis by, e.g., trip purposes, transport modes, age or income groups; or by using a non-zonal method as the point-based approach, which allows a focus on specific point locations and measurement of point-to-point travel costs at the individual level. Although person-based, this last measure focuses on a single reference location, and it overlooks the spatio-temporal constraints affecting people on a daily basis [34]. The _space-time measure_ addresses these limitations in the most comprehensive manner (e.g., refs. [14; 35; 36]).  
For this reason, this is considered an effective approach to measure accessibility at the individual level and to discuss individual accessibility differences [10]. It derives from the time geography framework elaborated by [PERSON] [37] and focuses on the set of discretionary opportunities (i.e., non-mandatory daily activities) that individuals could reach on a daily basis given the spatio-temporal constraints posed by their fixed daily activity chain (i.e., mandatory daily activities) [38]. This set is called a Feasibility Opportunity Set (_FOS_), and is obtained in three steps. First, the daily sequence of fixed activities constrained in space and time for a person is schematised. This sequence generates a so-called Space-Time Path (_STPA_). Based on the _STPA_, the Potential Path Areas (_PPAs_) are calculated for each couple of the following fixed activities in the _STPA_. Each _PPA_ includes all the locations that an individual could visit between two subsequent fixed activities, given the mandatory departure time from the former, the mandatory arrival time at the latter, the time needed to travel between them, and the time required to visit such locations. By extending the calculation of the _PPA_ to all couples of sequential fixed activities, the Daily Potential Path Area is obtained (_DPPA_). All the opportunities that belong to the _DPPA_ constitute the _FOS_ and define the space-time accessibility measure (_STA_).
# Stochastic Extreme Wind Speed Modeling and Bayes Estimation under the Inverse Rayleigh Distribution
## 4 A Bayes Method of Inference for the Risk Index Based upon the IRD Model
### A Premise. Bayes Estimation of the Frequency of Exceedances
Such a prior PDF on (X,Y) implies--as will be shown for the cases under study--a prior PDF on the RI \(R\) and is based upon the well-known Bayes formula used to derive the posterior PDF \(q(r|\mathbf{D})\), conditional to data \(\mathbf{D}\) (the observed EWS data in this application), which has been collected:  
\[q(r|\mathbf{D})=\frac{p(r)L(\mathbf{D}|r)}{C} \tag{20}\]  
where \(L\) is the Likelihood function [26; 27], given the observed data \(\mathbf{w}\), and \(C\) has the following expression:  
\[C=\int\limits_{0}^{\infty}p(r)L(\mathbf{D}|r)dr \tag{21}\]  
Finally, as well known [26; 27], the best Bayes estimate--in the mean square error sense--of \(R\) is given by its \"posterior mean\", evaluable through the posterior PDF of \(R\), \(q(r|\mathbf{D})\):  
\[R^{\circ}=\mathrm{E}[R|\mathbf{D}]=\int\limits_{0}^{\infty}rq(r|\mathbf{D})dr \tag{22}\]  
In practice, due to the nature of \(R\), i.e., the fact that it is a probability, the above integration is confined to the interval (0,1). Analogous formulations applied to the estimation of the single parameters (\(\phi\) and \(\alpha\)) will be illustrated in the following two subsections. Therefore, before considering the relevant features of the above general formulations to the practical cases of the IRD model, the Bayes estimation of \(\phi\) is illustrated here in a brief summary since, differently from the estimation of \(\alpha\), it is well established in the literature [25; 31]. As above recalled, f is the mean frequency of the process N(\(t\)) counting the number of exceedances: these are random events which occur at random time; T\({}_{1}\), T\({}_{2}\),..., T\({}_{n}\), by well-known properties of the Poisson process; such random times instants are gamma random variables [25; 31], and the times between two subsequent events, \(\tau_{k}=T_{k+1}-T_{k}\) (assuming and T\({}_{0}\) = 0) are Exponential random variables with mean value \(\mu\) = 1/\(\phi\). It is well-known [28] that a gamma PDF, also recalled in Appendix C, apart from being very flexible for describing positive random variables is a valid choice for the prior PDF of the RV \(\phi\), since it is a \"conjugate\" PDF [26; 27], i.e., the posterior PDF is still a gamma PDF. Thus, in this paper, a gamma prior PDF--denoted as \"_Gampdf_ (\(\phi;p_{0}\),\(\theta_{0}\))\"--for \(\phi\) is assumed:  
\[p(\phi)\big{|}\rho_{0},\delta_{0})=\frac{\phi^{\rho_{0}-1}}{\Gamma(\rho_{0}) \theta^{\rho_{0}}}e^{-(\frac{\phi}{\theta_{0}})} \tag{23}\]  
Then, let a sample of EWS data \(\mathbf{x}=\{x_{1},x_{2},\ldots,x_{n}\}\) be observed in a given time interval (0,\(t\)), and the number of exceedances of \(z\) be denoted by \(M\) as in the previous section.
## 3 Methods
### Data Processing
#### 3.1.1 Satellite Imagery Preprocessing  
In order to perform LCZ classification of satellite imagery, besides standard procedures such as the clip and merge of tiles to reconstruct a complete scene of the study area at any time period, band pixels need first to be converted to their real reflectance values. This ensures that classifications are not negatively affected by atmospheric disturbances so that images acquired in different seasons can be compared. Specific processing for the different satellite imagery is described below.  
* Landsat 8: Level-1 data available to users consists of radiometrically and geometrically corrected images. The Level-1 image is presented in units of DNs, which can be easily rescaled to spectral radiance or Top of Atmosphere (TOA) reflectance. In order to obtain real surface reflectance values, band pixels need to be further corrected. The Semi-Automatic Classification Plugin for _QGIS_[45] was used for such preprocessing.
* Sentinel-2: The Copernicus Open Access Hub provides Sentinel-2 imagery with different level products. In this study Level 1C imagery is used, which is ortho-corrected and with pixel radiometric measurements provided in Top of Atmosphere (ToA) reflectance. Moreover, with the aim of exploiting the complete band set of Sentinel-2 imagery at 10 m spatial resolution, resampling is required. As for Landsat 8 the preprocessing, including atmospheric correction and resampling, was performed through the Semi-Automatic Classification Plugin in _QGIS_.
* RapidEye: The Planet Explorer platform distributes only ortho-corrected imagery with bands at 5 m spatial resolution. Atmospheric disturbances can be adjusted by exploiting the band reflectance and geometric coefficients available in the metadata file of each tile, in either eXtensible Markup Language (XML) or Javascript Object Notation (JSON) formats, and by applying a Dark Object Subtraction (DOS) procedure [46]. To automate this process, an R script based on the open source Geospatial Data Abstraction Library (_GDAL_) [47] was created. The script makes also use of the _raster_ R package.