esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 4 System Model
### Base Station's Energy Consumption  
The activity factor of a base station is closely linked to its energy consumption and the sleep level that it can switch down to and the associated transition time [21]. When calculating the energy consumption of a base station based on the activity factor, we assume that all sites start serving their users simultaneously. It could lead to slightly overestimated interference and energy consumption values from those observed in a real network. The energy consumption of a base station \(E_{i}\) can be expressed as  
\[E_{i}=\eta_{i}TN_{s}P_{total}+\left(\sum_{j=1}^{3}S_{j}\delta_{j}P_{B}\right) \left(T^{\prime}-T_{ac,\delta_{j}}\right) \tag{16}\]  
Here, \(P_{B}\) is the idle mode power consumption of a base station, \(\delta_{i}\) is the sleep delta and indicates the depth of sleep, \(T^{\prime}\) and \(T_{ac,\delta_{j}}\) are the idle and sleep-level activation-deactivation (transition) times, respectively. Further information on the power model and the values of various parameters is found in [21]. The overall network energy consumption in an area \(A_{r}\) with a base station density \(\beta\) is calculated as,  
\[E_{total}=E_{i}\beta A_{r} \tag{17}\]  
A complete list of the variables and the various controllable parameters is provided in the Appendix A in Table 1.
## 4 Results
### Comparison of DInSAR Displacement with Feature Tracking and DEM Differencing
#### 4.1.1 Kaiserbergtal  
The Kaiserbergtal site is well-suited for DInSAR as it is barely affected by shadow and layover and the predominant direction of movement is north-east to east and, therefore, very detectable in ascending orbit. When comparing absolute values, the limited availability of observations for DInSAR due to decorrelation during snow cover is clearly visible. The DInSAR annual displacement roughly makes up one third of the annual movement observable by FT (Figure 4e). This agrees well with the proportion of scenes that could beused for DInSAR (ascending: 93/286; descending: 101/293). Hence, we scaled the DInSAR results by a factor of three for the following accuracy assessment. The E-W component from FT compared with the corrected DInSAR displacement shows good relative agreement with a Pearson R of 0.7, an RMSE of 11 cm and a standard deviation (sd) of 29 cm. The DEM differencing shows less relative agreement with a Pearson R of 0.42, an RMSE of 7 cm and an SD of 26 cm. Besides the systematic underestimation of displacement by DInSAR, an increasing deviation exists for dx starting at about 0.3 m of displacement in FT. These are predominantly areas in the southeastern part of the rock glacier (Figure 4a,b).
# Analytical Evaluation of Laser Cleaning Effectiveness in the Context of Contemporary Muralism
## 2 Materials and Methods
### Instrumentation
#### 2.2.4 Colorimetric Measurement  
To monitor the color changes after cleaning, colorimetric measurements were performed using a CM-700d Portable Spectrophotometer (Konica Minolta, Japan). In SCI (specular component included) mode, three points for each cleaning test section were measured and three measurements were automatically repeated for each point. The average value of color coordinates L*, a*, b* was calculated, and the change in color (\(\Delta\)E) was determined for each section by comparing the values of L*, a*, b* obtained before and after cleaning.  
Color in this context is typically described using the CIELAB color space, which is defined by the International Commission on Illumination (CIE). Component L* represents the brightness or darkness of a color and ranges from 0 (black) to 100 (white). Component a* measures the position of the color on the red-green axis; positive values represent redness, while negative values represent greenness. Component *b measures the position of the color on the yellow-blue axis; positive values represent yellowness, while negative values represent blueness. The change in color \(\Delta\)E quantifies the perceptual difference between two colors and was calculated using the following formula, known as the CIE76 formula:  
\[\Delta\mathrm{E}=\sqrt{\left(\Delta\mathrm{L*}\right)^{2}+\left(\Delta\mathrm{ a*}\right)^{2}+\left(\Delta\mathrm{b*}\right)^{2}}.\]
## 5 Experiments
### Database Preparation  
To evaluate the quality of the network, in addition to use the common retinal blood vessel dataset DRIVE, we also constructed two cerebral blood vessel datasets, called ELE and CORO datasets. For the selection of brain blood vessel data set, we mainly consider the problem of class imbalance. Our region of interest is blood vessels, so when selecting pictures, we should choose the more evenly distributed blood vessels. Otherwise, there will be many patches with only background but no blood vessels, which will reduce the accuracy of network training. The brain vascular data set is different from the retinal vascular data set in that it may have a more significant imbalance problem. The difficulty of retinal vascular segmentation is that there is no significant difference between the retinal vascular segmentation and the background. Compared with the data set of retinal vascular, ELE, and CORO brain blood vessel data set have a significant gap between the background and blood vessels, but there is a lot of noise. Moreover, the structure of the brain blood vessels is not coherent. It is easy to break during the segmentation, which can be a good evaluation of the performance of the segmentation network. How to make the overall structure of blood vessels clearer is the main problem we need to solve.  
DRIVE data set [4]: We use the retinal vascular public data set DRIVE, and the brain blood vessel 3D CTA image data set ELE, and the brain blood vessel MRA data set. Each of their original images is 565 \(\times\) 584 pixels in size.  
ELE data set: The ELE data set consists of 40 colorful CTA images of the brain of different patients at different angles. Each image corresponds to an artificially labeled vascular mask picture, 20 pictures for training, and 20 samples for testing. The size of the image is 565 \(\times\) 584 pixels.  
CORO data set: The CORO data set consists of 40 brain MRA images, 20 pictures for training, and 20 samples for testing, which are images of multiple patients and multiple different angles. Each image corresponds to an artificially labeled vascular mask map, and the image size is 565 \(\times\) 584 pixels.  
Each picture in these three data sets will generate a square data set during training (see Figure 4). In order to improve the reliability of training results, 190,000 patches were randomly selected, among which 10% was used as the verification data, of which 171,000 patches were used for training, and the remaining 19,000 patches were used for verification. The size of each patch was 48 \(\times\) 48 pixels.  
Figure 4: From left to right are the data sets DRIVE,ELE,CORO. Above are patches of the original image, and below are patches of the output results.
# Evolution of the Residual Pollution in Soils after Bioremediation Treatments
## 2 Materials and Methods
### Analytical Methods  
All samples were taken to laboratory, dried and sieved (<2 mm) prior to the analyses. Soil properties and constituents were determined according to standard methods [17]. Soil granulometry was measured after removal of organic matter with hydrogen peroxide and dispersion of samples with sodium hexametaphosphate, soil pH was measured in a soil:water suspension 1:2.5, electrical conductivity (EC) was measured in a soil:water suspension 1:5. Finely ground samples were used to the measurement of calcium carbonate content by volumetric method, the quantification of the organic carbon (OC) by acid-oxidation method. The concentration of PHEs (As, Pb, Cu, and Zn) was also analyzed in finely ground samples by X-ray fluorescence (XRF) with a NITON XL3t-980 GOLDD+ instrument (Thermo Fisher Scientific, Tewksbury, MA, USA). The precision and accuracy of the main PHEs concentrations were calculated by the measurement of a certified reference material (CRM052-050 RT-Corporation Limited, Salisbury, UK), obtaining the following results (certifiedvs. measured; mean values in mg kg\({}^{-1}\) and standard error in brackets): Pb: 82.6 (4.0) vs. 92.6 (4.8); As: 14.6 (2.9) vs. 15.5 (3.9); Zn: 89.0 (4.0) vs. 94.4 (6.2); Cu: 44.2 (6.9) vs. 48.5 (8.0).  
For the toxicity assessment of the potential pollution in the area, the bioassay with _Lactuca sativa_ was used, according to Organisation for Economic Co-operation and Development (OECD) [18] and United States Environmental Protection Agency (USEPA) [19] recommendations. For each experimental unit, a saturated paste of the soil was obtained after 24 h of contact and vacuum pump extraction; after this, 5 mL of the extract were put in contact with 20 seeds of _Lactuca sativa_ during 5 days of incubation at 25 \(\pm\) 1 \({}^{\circ}\)C. After this period, the percentage of germination and root elongation was measured both in soil samples as in control samples (distilled water). Values range from 0 (maximum toxicity) to 100 (no toxicity).
## 2 Preliminaries
### Multi-Objective Optimization Fundamentals  
MOO [9] is fundamentally concerned with finding a set of solutions where no single solution is superior to another in all objectives. This concept is mathematically formalized through the notion of Pareto dominance. A solution \(\mathbf{x}\) is said to dominate another solution \(\mathbf{y}\) if \(\mathbf{x}\) is at least as good as \(\mathbf{y}\) in all objectives and strictly better in at least one objective. Mathematically, this can be expressed as  
\[\mathbf{x}\prec\mathbf{y}\text{ if }f_{i}(\mathbf{x})\leq f_{i}(\mathbf{y})\ \forall i\ \in N\text{ and }f_{j}(\mathbf{x})<f_{j}(\mathbf{y})\ \exists\ j\ \in N \tag{1}\]  
where \(f_{i}(\cdot)\) and \(f_{j}(\cdot)\) represent the \(i\)-th and the \(j\)-th objective functions, and \(N\) represents the total number of objective functions. A Pareto optimal solution is defined as one that is not outperformed by any other solution within the search space. The set of all such Pareto optimal solutions constitutes what is known as the Pareto front.  
Representing solutions in a multi-objective space typically involves a vector of objective values, where each dimension corresponds to an objective. For instance, a solution \(\mathbf{x}\) in a bi-objective problem can be represented as \(\mathbf{x}=(x_{1},\,x_{2},\,\ldots,\,x_{n};f_{1}(\mathbf{x}),f_{2}(\mathbf{x}))\), where \(x_{1}\), \(x_{2},\,\ldots,\,x_{n}\) are the decision variables and \(f_{1}(\mathbf{x}),f_{2}(\mathbf{x})\) are the objective functions evaluated at \(\mathbf{x}\). As depicted in Figure 1, circular markers represent the projected positions of solution \(\mathbf{x}\) in the coordinate system under objectives \(f_{1}\) and \(f_{2}\). Assuming both \(f_{1}\) and \(f_{2}\) are minimization problems, it can be observed that the red points form a Pareto front, as illustrated by the orange line, where points on the Pareto front are non-dominating with respect to each other.  
The challenge in multi-objective optimization is to converge to a set of solutions that closely approximate the true Pareto front while maintaining a diverse spread across the front. This balance between convergence and diversity is crucial for providing decision-makers with a comprehensive view of the trade-offs inherent in the problem. The performance of a multi-objective optimization algorithm is often evaluated based on its ability to generate a well-distributed set of solutions that accurately represent the Pareto front.  
In summary, MOO is about navigating the intricate landscape of trade-offs inherent in problems with multiple objectives. It aims to identify a set of solutions, known as the Pareto front, that offer the best possible compromises among these objectives. This process is not merely about finding any solution; it is about uncovering a diverse array of solutions that collectively represent the full spectrum of possible trade-offs. This is particularly beneficial in complex and dynamic environments where traditional SLAM methods may struggle due to their limited ability to balance multiple performance criteria.
## 3 Results and Discussion
### Comparison of Surface Heat Flow of Four Internal Walls  
Figure 8 shows the variation of inner surface heat flow (\(q_{\rm i}\)) of four internal walls with time under short and long heating durations.  
As shown in Figure 8a, the inner surface heat flow \(q_{\rm i}\) of Wall 4 is the lowest of the four walls, and the heat flow \(q_{\rm i}\) of the other three walls are similar to each other during heating period. This is because Wall 4 gives the smallest temperature difference between the inner surface and indoor air, and hence the smallest heat flow \(q_{\rm i}\). It can also be seen from Figure 8a that the internal walls release heat to the indoor air during heating cessation period, and the heat flow achieves a similar effect by the configuration of internal walls.  
The results in Figure 8b indicate that Wall 4 still demonstrates the smallest heat flow among the four internal wall types, which is consistent with the situation presented in Figure 8a. However, the heat flow of Wall 3 drops gradually with the heating duration and is almost equal to the \(q_{\rm i}\) of Wall 4. This is because of the similar temperature different between indoor air and inner surface temperature of Wall 3 and Wall 4.  
Figure 7: Variations of outer surface temperature \(T_{\rm in,o}\) of the internal walls with time: (**a**) \(C_{\rm T=1}\), (**b**) \(C_{\rm T=8}\).  
To evaluate the heat transferring to the adjoining room, the variation of the heat flow \(q_{\rm o}\) between outer surface (close to the adjoining room) of internal wall and adjoining air are shown in Figure 9.  
It can be observed from Figure 9a that the heat flow \(q_{\rm o}\) of four internal walls is always small for heating case \(C_{\rm r=1}\) (less than 5 W), and the difference of \(q_{\rm o}\) between four walls is slight. With the increase of heating duration, the heat flow \(q_{\rm o}\) increases obviously (as shown is Figure 9b). Moreover, a significant difference of \(q_{\rm o}\) between four walls is observed. This is because of the temperature difference of outer surface between the four internal walls.  
The results in Figure 9b also shows that the heat flow is nearly zero when the heating duration is less than 5 h. It means the process of heat absorption and storage of internal walls lasts 5 h before heat transferring to the adjoining room, no matter what the configuration of internal wall is.
## 2 Theory  
### CS, TV, and MaxEnt Based MRSI Reconstruction  
The under-sampling of 4D MRSI datasets and subsequent non-linear reconstruction by CS, TV, or MaxEnt have been described by previous work and will only be covered here briefly [16,17]. Each of these methods can be used to iteratively reconstruct missing samples from an NUS MRSI dataset by enforcing a fidelity constraint in the sample domain and either a maximum sparsity, minimum differences, or maximum entropy signal model in some transform domain [10,23,24]:  
\[\begin{array}{ll}\min_{u}&\phi(u)\\ \text{such that}&\|R\mathcal{F}u-f\|_{2}\leq\sigma\end{array} \tag{1}\]  
where \(u\in\mathbb{C}^{N}\) is the reconstructed spatial, spectral-domain \((Y,X,F_{2},F_{1})\) data, \(\mathcal{F}\) is the 4D Fourier operator, \(R\in\mathbb{R}^{MxN}\) is the under-sampling mask that determines which samples were acquired in the \(k_{y}-t_{1}\) plane, \(f\in\mathbb{C}^{M}\) is the sampled k-space, time-domain \((k_{y},k_{x},t_{2},t_{1})\) data, and \(\sigma\) is the standard deviation of noise in \(f\).  
The choice of \(\phi(u)\) depends on the signal model used during reconstruction. The transform sparsity signal model used in CS uses \(\phi(u)=\|\Psi u\|_{1}\), which is the \(l_{1}\) norm of the reconstructed data in some transform domain, \(\Psi\), and attempts to remove NUS artifacts by reducing the amplitude of each transform coefficient and increasing the sparsity of \(\Psi u\)[25]. The minimum TV signal model uses \(\phi(u)=\|\
abla_{y}\cdot\Psi u+\
abla_{F_{1}}\cdot\Psi u\|_{1}\), where \(\
abla_{y}\) and \(\
abla_{F_{1}}\) are anisotropic differential operators applied to \(\Psi u\) along the \(y\) and \(F_{1}\) dimensions, respectively, and removes NUS artifacts by minimizing the differences between adjacent transform coefficients. Because the mixed-domain \(k_{y}-t_{1}\) plane of a 4D EP-COSI dataset is self-sparse, no sparsifying transforms are applied to \(u\) for the CS and TV reconstruction so \(\Psi=I\)[16]. The MaxEnt signal model uses \(\phi(u)=-S_{\
icefrac{{1}}{{2}}}\), which is the spin-\(\
icefrac{{1}}{{2}}\) entropy derived from the statistical distributions defined by the density matrices of spin-\(\
icefrac{{1}}{{2}}\) nuclei, such as \({}^{1}\)H [26]. It removes NUS artifacts by modeling each transform coefficient in the spatial-spectral domain as the byproduct of an ensemble of \({}^{1}\)H spins whose phase dispersion determines the signal amplitude; in-phase spin ensembles have high signal amplitude and low phase entropy, while high entropy ensembles have low phase coherence and low signal amplitude. Because Shannon entropy is derived for discrete processes that result from a Poisson-based distribution, it cannot be used for reconstructing NMR data, which do not follow a Poisson distribution [26; 27].
## 3 Computational Aeroacoustic (CAA) Model of Human Phonation
### Geometry, Mesh and Numerical Solution  
The aeroacoustic field is numerically solved by the Finite Element Method. Figure 7 illustrates the geometry and structure of the acoustic mesh. Geometries of vocal tracts for vowels [u:] and [i:] were modeled from frustums concatenated one after another. The shape of the frustums was defined according to vocal tract area functions measured by magnetic resonance imaging [53]. The vocal tracts (purple) were conformally attached to the larynx (red). The connection is formed by two hexahedral layers, with minor influence on the wave propagation. The right edge of the vocal tract is attached to the downstream radiation zone. The radiation zone is overlaid by a Perfectly Matched Layer (PML) (green) [54], with an implemented damping function to avoid reflecting waves backward. The second PML layer is inserted upstream from the inlet boundary. Microphone mic1 is located 1 cm downstream from the mouth.  
The acoustic element length \(\Delta l^{\text{a}}\) and time step \(\Delta l^{\text{a}}\) for acoustics are given by estimations [51]  
\[\Delta l^{\text{a}}=\frac{c}{20\,f_{max}}=3.43\,\text{mm},\ \ \Delta l^{\text{a}}= \frac{1}{20\,f_{max}}=1\ \ 10\ \ ^{5}\,\text{s}, \tag{27}\]  
assuming that 20 linear finite elements per one acoustic wavelength are sufficient. In our case the spatial discretization is limited by 3.43 mm and time step by 1 \(\ \ 10\ \ ^{5}\,\text{s}\) in order to resolve properly acoustic frequencies up to \(\,f_{max}=5\,\text{kHz}\). If the condition is not satisfied, the acoustic results are affected by high dissipation and dispersion [55]. The discretization uses hexahedral first order finite elements: 19k for vowel [u:] and 23k vowel [i:]. The acoustic material properties are defined by the density \(\rho=1.11703\,\text{kg}\,\text{m}\)\(\ \ {}^{3}\), the bulk modulus \(K=0.1156\,\text{MPa}\) and the speed of sound \(c=343.25\,\text{m}\,\text{s}\)\(\ \ {}^{1}\).  
The computation of acoustic sources and simulation of the wave propagation were realized on the same computational cluster as the CFD simulations using the finite element software OpenCFS [56]. The computational times for CAA simulations are much lower than those for the CFD simulations, about 5 h on a single CPU core compared to 30 days on 20 cores. It should be noted that the conservative interpolation of the acoustic source from the CFD grid to the much coarser acoustic grid was performed also by a finite element software OpenCFS. The work of [PERSON] et al. [48] contains an overview of the conservative strategies, granting a reduction of simulation time.  
Figure 7: Geometry, mesh and probe location for computational aeroacoustic (CAA) simulations— vocal tracts [u:] (**top**) and [i:] (**bottom**). Red—larynx, purple—vocal tract, green—PML.
in terms of the reactivation of bank erosion and the replenishment of the local bedload in gravel-bed floodplain rivers with a sufficient amount of freedom space. In the Allier River (France), they conclude the removal of riprap is an effective measure for certain, but not all, channelized floodplain reaches. The geomorphological and sedimentary contexts are two criteria that should be considered when selecting sites for restoration. [PERSON] et al. [3] propose to model the diachronic evolution of connectivity in small agricultural basins of Seine Maritime in the Parisian basin (France) using a multiagent system (MAS) and to develop synthetic indicators characterizing spatial links in the flow processes. The model outputs show that spatial connectivity has been steadily increasing over the past 70 years due to the enlargement of the parcel grid and the growth of runoff surfaces. [PERSON] and [PERSON] [4] examine the shifting pattern of the Ganges-Padma River (Banglades) using satellite images taken between 1973 and 2016. They conclude that both the sinuosity ratio and the braiding index increased over time, exceeding the meandering threshold. Overall, the variation in the braiding index exceeds that of sinuosity. [PERSON] and [PERSON] [5] focus on the interpretation and quantification of sedimentary processes in low-energy fluvial environments. They compare five methods of grain size data processing (statistic moments, textural analysis, multivariate statistics combining Principal Component Analysis and hierarchical cluster analysis, and CM image and end-member modeling analysis). The authors discuss their effectiveness in characterizing low-energy floodplain deposits in the downstream section of the Charente River (France). They conclude that the multivariate statistics approach and end-member modeling analysis present interesting results and allow the robust identification of sub-units. However, the results of the multivariate statistics depend on the choice of input variables and do not support non-zero values, while the second method, the most recent and most complex, needs to be developed further to clearly link the end-member classes to sedimentary processes.  
Although submissions for this Special Issue have been closed, more in-depth research in the field of geo-hazards analysis, hydrogeology and river restoration continues to address the challenges we face today, such as maintaining biodiversity, water quality, sustainable regional development, reducing natural and technological risks in the context of climate change and global population growth.  
This research received no external funding.  
Thanks to all the authors and peer reviewers for their valuable contributions to this Special Issue \"New Technologies in Geo-Hazards Analysis, Hydrogeology and River Restoration\". I would also like to express my gratitude to all the staff and people involved in this Special Issue.  
The author declares no conflict of interest.