esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Optimization Using NSGA-II
Variation: Crossover and mutation operators were applied to create an offspring population Gt, effectively doubling the size to Pt + Gt. Simulated binary crossover (SBX) was used with a probability of 0.9, and polynomial mutation with a probability of 0.1. 3. Non-dominated sorting: All candidate solutions were ranked based on Pareto dominance. Rank 1 included the non-dominated Pareto set, followed by subsequent dominated layers (Rank 2, Rank 3, etc.). 4. Crowding distance calculation: Within each rank, individuals were assigned a crowding distance value based on their Manhattan distance to neighbors in objective space. This distance preserves solution diversity and prevents premature convergence. Selection: Elitist selection was applied. Individuals with higher ranks and greater crowding distances were selected to form the next generation P\({}_{t+1}\), ensuring both convergence and diversity. 6. Termination: The loop was repeated until either 250 generations were completed or a predefined early-stopping criterion was met (hypervolume improvement \(<\) 1% over 20 generations). These steps are visually summarized in Figure 2a (algorithmic pipeline) and Figure 2b (Pareto-based rank evolution). As seen, new generations are produced through variation, sorted by non-domination, evaluated for crowding, and selected for survival using elitist strategies. Moreover, to ensure the realism and operational validity of the solutions generated by the NSGA-II algorithm, domain-specific feasibility constraints were explicitly encoded into the optimization process. The decision vector included four continuous variables (X\({}_{1}\): transport distance, X\({}_{2}\): energy intensity, X\({}_{3}\): moisture content, X\({}_{5}\): grid CO\({}_{2}\) factor) and one discrete ordinal variable (X\({}_{4}\): gradation adjustment level, with values [0,1,2]). The ordinal variable was handled using integer encoding and subjected to discrete mutation and crossover operators. Feasibility rules--such as enforcing X\({}_{2}\geq 1.8\) when X\({}_{3}>\) 10% (to ensure sufficient drying energy), or coupling X\({}_{4}=2\) with minimum X\({}_{2}\geq 1.5\) (to reflect increased processing demand)--were implemented as constraint checks post-variation. Infeasible offspring were either repaired or penalized by exclusion from the next generation. This hybrid continuous-discrete structure was managed within a modular implementation that applied constraint validation at each evolutionary cycle, ensuring that only technically viable configurations contributed to the evolving Pareto set. The resulting Pareto front--consisting of trade-off optimal solutions between cost and emission--was visualized in 2D space and analyzed for diversity, inflection zones, and practical feasibility. To support final decision making, this non-dominated set was further evaluated using the TOPSIS ranking method, detailed in Section 3.5. Figure 2: NSGA-II algorithm mechanism: (**a**) algorithmic pipeline, (**b**) Pareto-based rank evolution.
## 2 Project Overview  
The project of shield machine cutting bridge piles in Harbin Metro Line 3 is located in interval between Hesong Street station and Gongludaqiao station, which is in the central area of Harbin city in China. According to the construction plan, the interval tunnel from Hesong Street station to Gongludaqiao station has to cut and pass through the reinforced concrete pile foundation of Qianjinlu Bridge, as shown in Figure 1.  
In the section of crossing the bridge piles, the tunnel is stacked up and down, and the vertical net distance is 4.5 m. The lower tunnel is constructed first and then the upper  
Figure 1: Plan of the project.  
tunnel is constructed. Each tunnel is 6.0 m in outer diameter and 5.4 m in inner diameter. The tunnels are constructed with precast segments, and the length of each segment is 1.2 m. Three piles are scheduled to be cut by the shield machine in construction and one pile is passed by. The piles have a diameter of 1.2 m, a length of 33 m and are built of C25 concrete. The measure of pile foundation underpinning has been taken to ensure the safety and normal use of the bridge before cutting the piles. The main stratum in the excavation range of the tunnel is medium sand and silty clay layers. The soil mass surrounding the foundation of Qianjinglu Bridge is mainly composed of medium sand and silty clay layers. Starting from the surface, the soil layers are miscellaneous fill 1\({}_{1}\), fine sand layer 2\({}_{3}\), medium sand layer 2\({}_{2}\), silty clay layer 2\({}_{4}\), medium sand layer 2\({}_{2}\), silty clay layer 2\({}_{1}\) and medium sand layer 2\({}_{2}\). The water table is about 4.8 m from the surface and is in the medium sand layer 2\({}_{3}\). The basic physical and mechanical soil properties are shown in Table 1. The relative position of the piles and tunnels is shown in Figure 2.  
Figure 2: The relative position of tunnels and piles: (**a**) Top view; (**b**) Front view and side view.  
A slurry balance shield machine manufactured by CCCC Tianhe Machinery and Equipment Manufacturing Co., Ltd., Changshu city, China was used in tunneling. The shield machine with a cutter head diameter of 6270 mm is equipped with 60 modified shell-shaped tools (special tools) to efficiently cut reinforced concrete piles. The cutter head and cutter layout are shown in Figure 3.
In recent years, nanobubbling was considered as a potential technology to be applied for water purification to remove organic compounds. It has advantages such as a small size, large specific surface area, long residence time in the water, high mass transfer power, high zeta interface potential and the capacity to produce hydroxyl radicals [23; 24; 25]. The methods to generate nanobubbles include pressure variation [26], hydrodynamic cavitation [27], ultrasonic cavitation [28], electric fields [29] and porous membranes [30]. [PERSON] et al. [27] produced air and oxygen bulk nanobubbles in a water solution using hydrodynamic cavitation with an optimum processing time of 30 min. The produced nanobubbles had sizes between 190 and 680 nm and long-term stability up to three months. The molecular dynamics simulations used by [PERSON] et al. [29] found that the origin of the nanobubbles' movement was due to dielectrophoresis, while the substantial nanobubble stabilization was attributed to surface-polarization interactions. On the other hand, [PERSON] et al. [30] developed porous alumina films with straight nanoholes to generate bulk nanobubbles, and observed that the minimum size of the nanobubbles depended on the solubility of the encapsulated gases in water, but it was less than 100 nm. There are studies on the use of nanobubbling for antibiotic degradation [24], but to the best of our knowledge, there is no study on a DCF treatment method using this technology. In this way, the aim of this work was to study the effect of the combined treatment of electron-beam (EB) radiation with nanobubbling pretreatment on the removal of DCF from aqueous solutions, in order to find the conditions for optimal energy usage. Previously, other authors investigated the use of nanobubbling aeration to enhance ozone diffusion in an electro-peroxone AOP for the removal of pharmaceutical contaminants from hospital wastewater [31], which obtained individual removal efficiencies higher than 85%. Nevertheless, as far as the authors know, this study represents the first application of the combination of EB and nanobubbling to degrade DCF. After describing the decomposition studies with different experimental conditions, a discussion on the intermediate products will be carried out.
# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 5 Analysis and Discussion
### Ablation Study  
To better demonstrate the effectiveness of the architecture of our method, we conducted an ablation study combining three factors, namely, cGAN, the decomposition guided module (DGM) and the pipeline and perceptual loss (PL). We constructed the following variants with different component combinations: (1) cGAN, where only pix2 pix [28] was used; (2) cGAN + DGM, where the results of DM and the hazy image were concatenated to be passed on to pix2 pix; (3) cGAN + DM + pipeline, where a pipeline extracted the features of the results of the decomposition guided module; and (4) cGAN + DGM + pipeline + PL, which considered additional perceptual loss to train the network.  
We implemented ablation experiments on SOTS [32]; these results are given in Table 3. The results demonstrated that the proposed method achieved the best image dehazing performance compared with pix2 pix [28]. PSNR and SSIM improved by 2.71 dB and 0.023, respectively.
## 3 Methods
### Bare Soil Scattering  
The backscattering from a bare soil surface mainly depends on the soil moisture content and soil surface roughness [17]. To determine the backscattering of the underlying soil (\(\sigma^{o}_{soil}\)), the IEM [6] was employed in this study. In general, the IEM quantifies the backscattering coefficient from a surface as a function of its roughness parameters, its dielectric constant, and the characteristics of the acquired images (polarization, incidence angle, and frequency). The soil roughness is characterized by the root mean square height and correlation length. The dielectric constant is derived from the surface volumetric moisture content [33]. In this study, the dielectric constant was calculated by the Topp model [36], which was validated for a wide range of soils under different conditions [37]. In a prairie environment, the vegetation complicates the assessment of the soil roughness. To overcome this problem, the surface height and the autocorrelation length was estimated using the method proposed by [PERSON] _et al._[38] over the bare area.
## 4 Pre-Selection of Predictive Variables
### Principles for Pre-Selection of Predictive Variables and Limitations  
Principles for pre-selecting predictive variables may change with disciplines. For environmental sciences, the main principle is that predictive variables need to be closely related to the variable to be predicted (i.e., the response variable) [67; 68]; ideally, they should be causal variables, or variables directly caused by the response variable (e.g., optical reflectance of vegetation types, backscatter of seabed substrates). They are usually identified based on expert or professional knowledge. However, in many cases, it is hard to know what the causal variable(s) is (are) for a response variable. Proxy (or surrogate) variables are often used instead of causal variable for spatial predictive modeling. Again, they are usually identified based on expert or professional knowledge [69]. Certainly, predictive models can use causal variables, proxy variables, or both if causal variables are not all available.  
When the accuracy of a resultant predictive model is unexpectedly poor, then we may need to consider that we may have missed some important predictive variables, for which we may have no knowledge or even awareness (e.g., hidden variables [70]). Further actions are required to expand the professional knowledge pool in order to identify such possible predictive variables.  
For spatial predictive modeling, the selection of potential predictive variables is even more challenging. This is because the selection could be constrained by certain factors. For example, predictive variables need to be continuously available for a target region. Spatial resolution is also a critical issue as the resolutions of various predictive variables need to meet the desired resolution for the final predictions, although they can be rescaled. Sometimes, even though we know the possiblecausal predictive variables based on expert or professional knowledge, they may not meet these requirements and cannot be used for spatial predictive modeling. This is particularly true in marine environmental sciences.
## 5 Experiments and Discussion
### Limitations Analysis of TRSA  
In Figure 8, one can see that our model's performance was not outstanding when the number of posts received by the model was between 0 and 20%. To further analyze the performance of the model in scenarios with few posts (low resources), we finely divided the test set into three parts based on the number of posts in the news (Source Information). The details are shown in Table 3. One can see that TRSA gave an outstanding performance on the test set for any test sample with more than 10 posts, while on the test set in which there were samples with fewer than three posts, TRSA did not perform well. This indicates that TRSA can capture valuable semantics from multiple posts, but its performance can be limited by samples with fewer posts.
# Autonomous Environment Generator for UAV-Based Simulation
## 4 Artificial Intelligence Algorithms
### Mask R-CNN
#### 4.2.1 Dataset  
The dataset used for our implementation comes from the Spacenet Road Network Detection Challenge [30]. The data we used includes satellite imagery from the WorldView-3 Satellite Sensor over various parts of Vegas and Shanghai. These regions were chosen for their variety in road type/layout and scalability. The raw source geouffs of RGB raster data are converted to 8-bit JPEG using the Geospacial Data Abstraction Library that can then be processed by Mask-R-CNN [31]. Additionally, the image annotations are also parsed into JSON files that include the relevant information for segmenting roads. This is because the original annotations differentiate between various types of roads, lane numbers, and whether or not is paved, which are too specific for our current implementation. One final consideration is the fact that only road center lines are given in the unaltered annotations. The entirety of the road is captured in the formatted JSON annotation files by applying the OpenCV flood fill algorithm to each image instance [32]. Our result is nearly 1.6k road images that pair favorably with the Mask R-CNN training model.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Unsupervised Learning
#### 4.2.7 MIMO  
With multiple antennas at the transmitter and receiver, Multiple Input Multiple Output (MIMO) has been widely adopted in wireless systems. The authors in [93] propose an unsupervised fast beamforming DNN design method for maximization of sum-rate in a MIMO single base station system. The proposed approach can preserve the performance, while improving considerably the computational speed, thus achieving results close to optimal.
## 3 From EPOV to Content Neutrality
### Main Effects of Content Neutrality  
**Breaking up monopolies of content gatekeepers:** As in every search or knowledge portal, also in Wiki systems recommendations or rankings must help the user locate the desired information. In many cases this process is implicit in the workflow of the portal. In search engines, the PageRank algorithm or similar mechanisms point the user to pertinent pages. It is well known that users view only the first hits of search engine ranking lists, which turns ranking algorithms into effective content gatekeepers. Still the situation is better than in Wikipedia, since the user is offered a (wide) choice and consciously makes a selection decision. In WP, however, the single most up to date version of an article creates the impression of an allegedly optimal and objective selection of relevant information--the user is not explicitly made aware of the process of this selection and of the bias produced by NPOV and various other WP article criteria. Thus, Wikipedia serves as a monopolistic gatekeeper to content. A content neutral EPOV approach, however, empowers the reader allowing him or her to decide how the ranking, selection and valuation of content should be done.  
**Becoming a helpful instrument to science:** An important aspect in scientific endeavors is the documentation of the process which accompanies a thesis through its different lifecycle phases from conjectured to accepted and finally obsolete, and the lines of reasoning which back this development. A content-neutral, EPOV knowledge base allows the administration of multi-perspective approaches to a topic and provides a variant-augmented Wiki system with a real chance of becoming a truly helpful instrument for science.  
**Example:** Collaborative work on biological pathways may provide an example for the benefits of such an approach. Pathways are biochemical models of the myriad of interactions in biological processes. They arise from a tremendous amount of data gained in biochemical laboratories world-wide and interpretation of the experimental data is not always without controversy. An open, Wiki-based knowledge management system as in WikiPathways proves very helpful for the pathways community but the optimal balance between quality control and cooperation has not yet been found. Since there are more and less accepted laboratory methods and interpretation standards, a variant approach allows expressing all possible points of view and empowers the reader to choose those which are acceptable to his or her standards [18].
# Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones
## 3 Approach and Implementation
### Single Point Positioning Algorithm of the Smartphone  
GPS SPP, also known as standalone or autonomous positioning, determines the user's position via a single frequency receiver. SPP determines its own coordinates with respect to the center of the Earth by tracking four or more GPS satellites simultaneously. Its accuracy is generally poor, because errors in the broadcast (BRDC) ephemeris data and clock, as well as signal delay in the atmosphere, propagate into the signal. There have been many attempts to increase the accuracy of GPS SPP. The generally accepted technique is to remove the atmospheric delays using standard models [24; 25; 26; 27]. Single frequency receivers typically use the Klobuchar Model according to the IS-GPS-200 method to calculate ionospheric delay [26], and the coefficients for modeling are included in the satellite navigation message [27]. tropospheric effects are a function of the satellite elevation angle and the altitude of the receiver, and are dependent on the atmospheric pressure, temperature, and water vapor pressure [28]. As shown in Figure 3, the satellite clock offset (\(b\)) is obtained from the navigation message, and the atmospheric delays (\(\hat{T}_{sans}\) and \(\hat{I}_{klob}\)) are computed from standard models to obtain the error-reduced PVT (Position, Velocity, Timing) solution.  
The compensated pseudorange \(\rho_{c}\) is calculated as shown in Equation (1), and this can be used in SPP in place of \(\rho\) to mitigate the positioning error.  
\[\rho_{c}=\rho-\hat{T}_{sans}-\hat{I}_{klob} \tag{1}\]  
Figure 4 shows the horizontal and vertical error variation logged from the Galaxy S5 smartphone at Sejong University RS. The RMS values (horizontal 1.4 m, vertical 4.4 m) are far smaller than those of the high-cost Novatel Flexpak receiver (horizontal 4.4 m, vertical 21.4 m). From these results, we inferred that the smartphone compensates for its pseudorange using the atmospheric model and Equation (1). The time offset between other GNSS constellations is a source of bias in multi-GNSS positioning, and can result in errors of up to 45 m after corrections using the parameters in the navigation message. The maximum error in the Galaxy S5 results is below 5.2 m, significantly less  
Figure 2: NMEA Position Output Example for a Samsung Galaxy S5.  
than 45 m. Therefore, we can assume that the module in this phone introduces an additional unknown parameter, namely the clock bias of each GNSS, into the solution.
# Fusion-Based Localization System Integrating UWB, IMU, and Vision
## 3 Tightly Coupled UWB/IMU Fusion Localization Based on Sliding-Window Factor Graph  
### UWB Ranging Model  
In fusion-based localization systems, UWB technology has been widely adopted for indoor positioning due to its high accuracy and strong resistance to multipath interference. Our system employs a TOA-based positioning method, which determines the signal propagation time by directly measuring the interval between when the UWB pulse is transmitted by the mobile station and when it is received by the reference station [32]. The distance is then calculated by multiplying the propagation time by the speed of light in free space. This distance is subsequently used to determine the position of the mobile station. Specifically, the UWB mobile station transmits a pulse signal at time \(t_{1}\) and the reference station receives it at time \(t_{2}\). The propagation time is \(t_{2}-t_{1}\) and the distance \(d_{i}\) between the two stations is given by:  
\[d_{i}=c\cdot(t_{2}-t_{1})+n_{i} \tag{1}\]Here, \(c\) denotes the speed of light, \(n_{i}\) represents the measurement noise, and \(d_{i}\) is the measured distance between the \(i\)-th reference station and the mobile station. In a two-dimensional localization scenario, let the unknown position of the UWB mobile station be denoted as \((x,y)\), and the known position of the \(i\)-th reference station be \((x_{i},\ y_{i})\). The corresponding TOA ranging equation can be expressed as:  
\[d_{i}=\sqrt{\left(x-x_{i}\right)^{2}+\left(y-y_{i}\right)^{2}}+n_{i} \tag{2}\]  
As illustrated in Figure 1, by introducing multiple reference stations (at least three), the nonlinear equations can be jointly solved to accurately estimate the two-dimensional position of the UWB mobile station. This method achieves high ranging accuracy and stability, provided that strict time synchronization is maintained among all base stations. Compared with other positioning models such as TDOA and Angle of Arrival (AOA), the TOA-based method demonstrates superior absolute positioning performance in systems where hardware-level time synchronization is supported. It is particularly suitable for indoor navigation and mobile robotics applications that require high positioning accuracy.