esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Control Optimization of an Underwater Piezoelectric Energy Harvester
## 3 Computational Setup  
A computational model has been developed to characterize the behavior of the circular cylinder of the underwater harvester. The fluctuating lift acting on a stationary circular cylinder in cross flow at four different Reynolds numbers has been investigated. The dimensions of the computational domain are shown in Figure 2. Downstream of the cylinder, a domain of 40 times the cylinder diameter (D) has been chosen to investigate the vortices caused by the fluid passing around the body. Slip condition has been selected for the top and bottom boundaries and velocity inlet at the inlet of the domain.  
The design and construction of a high quality grid is crucial to the success of the Computational Fluid Dynamics (CFD) analysis and have the most direct influence on the precision, convergence, and time required of the solution. In the present case, the mesh consists of 56,000 two-dimensional cells, the most of them where located around the cylinder and downstream of the cylinder in order to capture the vortex shedding in the wake. Around the cylinder 20 prismatic layers of cells where located to obtain a wall y\({}^{+}\) less than 1 and polyhedral cells were chosen for the rest of the domain, as illustrated in Figure 3.  
Figure 1: Energy harvester assembly on a pipe (not to scale).  
The numerical solution has been reached by applying RANS (Reynolds Average Navier-Stokes) equations for unsteady state flow in a finite volume flow solver for unstructured grids STAR-CCM+ [41]. The CFD code uses discretization methods to convert the continuous system of equations to a set of discrete algebraic equations by means of the Finite Volume Method. The time step was calculated as 0.002 s and 15 inner iterations were determined as the optimal number. The time intervals have been chosen to be small enough to capture the vortex shedding. Upwind scheme [42] was used to discretize the convective terms, ensuring the robustness of the solution. The turbulence is modeled through the ku-SST turbulence model by [PERSON] [43]. Unsteady state computations have been successfully applied to the similar cases in the previous studies of [PERSON] et al. [44] and [PERSON] et al. [45]. All the simulations were converged until a satisfactory residual convergence was achieved on the velocities, pressure, and turbulence quantities.
## 2 Nutritional Quality
### Antioxidant Compounds
#### 2.1.1 Water-Soluble Antioxidants
* Vitamin C and vitamin B9 (Folic Acid)  
Vitamin C, or ascorbic acid, is a powerful antioxidant, widely present and studied in fruits; however, many recent works have been focused on the importance of vitamin C in vegetables, mostly in _Brassica_e family. In _Brassica_ vegetables vitamin C concentrationvaries a lot among species and subspecies, and it is strictly genotype- and environment-dependent [33; 34]. Vitamin C performs countless biological activities in the human body and represents a nutritional compound fundamental for health. Ascorbic acid is a radical scavenger, an enzyme cofactor and a donator/acceptor in electrons transport at the plasma membrane level; its role is fundamental in the regeneration of \(\alpha\)-tocopherol, and in the prevention and treatment of malignant and degenerative diseases [33; 35]. Among _Brassica_ genotypes, Brussel sprouts (76-192 mg/100 g edible portion) and kale (92-186 mg/100 g edible portion) seem to possess the highest content of vitamin C, followed by broccoli (34-146 mg/100 g edible portion) and cauliflower (17-81 mg/100 g edible portion), while white cabbage (19-47 mg/100 g edible portion) possesses the lowest amount [14]. Vitamin B9 (Folic acid) is an important vitamin present in _Brassica_, mainly in raw broccoli (63 ug/100 g of edible portion), Brussel sprouts (61 ug/100 g of edible portion) and kale (141 ug/100 g of edible portion) [36], that act as a coenzyme in many single carbon transfer reactions, in the synthesis of DNA and RNA and of protein components. Furthermore, it reduces the level of homocysteine in the blood, a risk factor for cardiovascular diseases. Among the several health activities that folic acid performs, it is strongly important in the prevention of megaloblastic anaemia, neuropsychiatric disorders and various forms of cancer in the foetus during pregnancy, also reducing the risk of neural tube defects [33; 37]. These beneficial effects of folic acid, in particular on the pathogenesis of cancer, and neurological, haematological, and cardiovascular diseases may, in part, be due to its antioxidant activity, via its electron-accepting capacity [38; 39].
# Blasting of Unstable Rock Elements on Steep Slopes
## 2 Materials and Methods
### General Constraints of Blasting Operations on Rock Slopes  
Following an extensive geostructural survey, the development of a drill hole pattern necessitates a delicate balance among numerous constraints:  
* Assessment of the rock quality and lithological and strength properties of the rock formations to ensure effective drilling and blasting outcomes;
* Understanding the joints' orientation (dip, dip direction) and characteristics of joints within the rock mass to optimize drilling angles and minimize potential instability;
* Implementation of measures to mitigate vibrations generated during drilling and blasting operations, reducing the risk of structural damage and instability of additional rock blocks or discomfort to nearby structures and communities;
* Determination of the required fragmentation size of the blasted rocks, in order to enable the required removal of the subsequent muck or material after blasting;
* Assessment of the accessibility of the drilling site to ensure efficient deployment of drilling equipment and personnel;
* Incorporation of time-sensitive requirements, such as the need to swiftly reopen roads or access routes, into the scheduling and execution of drilling and blasting operations.  
By carefully addressing these constraints, the design of the drill hole pattern can be optimized to ensure safe, efficient, and effective drilling and blasting operations while meeting project objectives and minimizing potential impacts on the surrounding environment and infrastructure.
# Cross-Attention U-Net for Elastic Wavefield Decomposition in Anisotropic Media
## 3 Results
### Comparative Evaluation of the U-Net and Cross-Attention U-Net
despite the higher computational cost, the cross-attention mechanism effectively leverages the interplay between the horizontal and vertical displacement fields to capture subtle wave interactions in anisotropic media, resulting in improved accuracy and cleaner separation as confirmed by both visual comparisons and quantitative metrics.
# Machine Learning-Based Lithium Battery State of Health Prediction Research
## 2 Review of Related Algorithms
### Review of the PSO Algorithm  
The PSO algorithm, proposed by [PERSON] and [PERSON], was inspired by the foraging behavior of bird flocks and is a type of evolutionary computation technique based on swarm intelligence theory [29]. Due to its simplicity, few parameters, fast convergence, and ease of implementation, PSO has been widely applied in parameter optimization, path planning, production scheduling, and other fields. In a D-dimensional search space, a swarm is composed of N particles, each of which updates its velocity and position based on its individual experience and the collective experience of the swarm. The fitness function serves as the evaluation criterion, ultimately leading to the discovery of the global optimal solution. During the \(t\)-\(th\) iteration, the velocity vector of each particle is represented as \(V_{i}^{t}=(v_{i1}^{t},v_{i2}^{t},\ldots,v_{id}^{t})\),and the position vector is denoted as \(X_{i}^{t}=(x_{i1}^{t},x_{i2}^{t},\ldots,x_{id}^{t})\).  
The formula for particle updating is expressed as follows:  
\[v_{id}^{t+1}=\omega v_{id}^{t}+c_{1}r_{1}(p_{id}^{t}-x_{id}^{t})+c_{2}r_{2}(p _{gd}^{t}-x_{id}^{t}) \tag{10}\]  
\[x_{id}^{t+1}=x_{id}^{t}+v_{id}^{t+1} \tag{11}\]  
where \(\omega\) is the inertia weight, which characterizes the global search ability of the algorithm. \(c_{1}\) and \(c_{2}\) are acceleration factors that represent individual learning factors and social learning factors, respectively, and control the extent to which particles converge towards individual optimums and global optima. \(p_{id}^{t}\) is the individual optimal position, \(p_{gd}^{t}\) is the global optimal position. \(r_{1}\) and \(r_{2}\) are random numbers in the interval [0,1], which add randomness to the algorithm.
# A Low-Profile Wideband Linear-to-Circular Polarization Conversion Slot Antenna Using Metasurface
## 5 Conclusions  
A new low-profile wideband circularly polarized microstrip slot antenna based on a metasurface was presented herein. The metasurface was used to realize reconfigurable polarization and widen the working frequency bandwidth. The proposed CP metasurface antenna has a compact structure with a low profile of 0.06\(\lambda_{0}\). Simulated results showed that the S\({}_{11}\) bandwidth and 3 dB ARBW of the proposed metasurface antenna were 42.94% from 4.28 GHz to 6.62 GHz and 18.83% from 5.1 GHz to 6.16 GHz, respectively. The measured S\({}_{11}\) bandwidth and 3 dB ARBW were 39.25% from 4.28 GHz to 6.37 GHz and 17.77% from 5.18 GHz to 6.19 GHz, respectively. The proposed CP metasurface antenna exhibits good prospects for C-band satellite communication applications with its compact structure, wide impedance bandwidth, and axial ratio bandwidth.  
Conceptualization, J.D. and C.D.; methodology, J.D. and C.D.; validation, C.D. and J.M.; investigation, C.D.; resources, J.D.; data curation, J.D. and C.D.; writing--original draft preparation, C.D.; writing--review and editing, J.D. and J.M.; visualization, C.D.; supervision, J.D. and J.M.; project administration, J.M.; funding acquisition, J.D. and J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China, grant number 61801521, 61471368, and 61971450, in part by the Natural Science Foundation of Hunan Province, grant number 2018 JJ2533, and in part by the Fundamental Research Funds for the Central Universities, grant number 2018 gczd014 and 20190038020050.  
The authors declare no conflict of interest.
## 6 Evaluation and Performance
### Comparative Evaluation with Other Signature Models  
To highlight the practical innovations of the proposed temperate blind signature (TBS) scheme, this subsection provides a comparative analysis with existing representative models: partially blind signatures (PBS) and threshold blind signatures (ThBS). The comparison includes key parameters such as anonymity control, performance, resistance to attacks, and suitability for constrained environments.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Scheme** & **Blinding (ms)** & **Time** & **Signing (ms)** & **Time** & **Signature Size** \\ \hline TBS (proposed) & 2.6 & 8.1 & 256 \\ \hline Partially blind signature (PBS) & \(\sim\)3.5 & \(\sim\)8.9 & \(\sim\)512 \\ \hline Lattice-based scheme & \(\sim\)6.0 & \(\sim\)20.0 & \(\geq\)1024 \\ \hline Threshold blind signature (ThBS) [6] & \(\sim\)4.2 & \(\sim\)12.1 & \(\sim\)768 \\ \hline ECC-based blind signature & \(\sim\)2.8 & \(\sim\)9.3 & 320 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison with other schemes (metrics for \(n=2048\) bits or equivalent).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Metric** & **Average Value** \\ \hline Blinding time & 1.7 ms \\ Signing time & 5.2 ms \\ Verification time & 4.6 ms \\ Signature size & 256 bits (for n = 2048) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Efficiency in restricted environments.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Characteristic** & **Proposed Scheme** & **Lattice-Based Schemes** \\ \hline Blinding complexity & \(O(k^{2})\) & \(O(k^{4})\) \\ Signature size & 256 bits & 1024 bits or more \\ Signing time & 5.4 ms & 20 ms (approx.) \\ Energy efficiency & High & Low \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scheme performance.  
As shown in Table 5, the TBS scheme provides notable advantages in resource-constrained environments. Unlike PBS, which embeds fixed message elements that may reduce privacy flexibility, the TBS parameter \(T\) allows dynamic adjustment between full anonymity and traceability. In contrast with ThBS, which relies on cooperative signing between multiple parties and is thus less suitable for embedded IoT systems, our scheme remains lightweight and efficient.  
Therefore, the TBS design addresses a practical need for adjustable confidentiality without introducing additional operational overhead, making it an effective choice for modern applications where both privacy and auditability must coexist.
## 2 Rock Fall Hazard Zoning and Protection Measures  
According to definitions widely accepted at the level [2; 18], landslide hazard can be defined as a combination of the intensity and frequency of occurrence of an event. In the specific case of rock falls, the intensity of the phenomenon can be expressed by the kinetic energy E of a rock block along its path, while the frequency of occurrence f of a block crossing a given location on the slope by the return period T, defined as T = 1/f. The frequency of occurrence f, in turn, is given by the product of the temporal frequency of detachment P\({}_{\text{f}}\) of a block from an unstable rocky cliff and the frequency P\({}_{\text{r}}\) of that block reaching a given point of the slope (frequency of reach), so that it results:  
\[\text{f}=\text{P}_{\text{f}}\cdot\text{P}_{\text{r}} \tag{1}\]  
\[\text{T}=1/\text{f}=1/(\text{P}_{\text{f}}\cdot\text{P}_{\text{r}}) \tag{2}\]Each location of a slope affected by a rock fall can therefore be characterised by a value of energy and a value of return period, all over the potential rock fall runout area, and these energy-return period \"couples\" (E, T) can be used to define the corresponding rock fall hazard H(E, T).  
A rock fall hazard zoning map is meant to provide this type of information all over the endangered area, usually grouping hazard values into several classes or levels, as a function of the combination of energy and return period.  
If a given location of the slope is found to be characterised by a hazard H(E, T) which is considered too high compared to criteria defining the safety of the assets at that location, the hazard has to be mitigated. To this effect, the choice of appropriate protection measures can effectively reduce the energy and increase the return period (i.e., decrease the frequency of occurrence) of the event, lowering the \"initial\" hazard H(E, T) down to a new mitigated level H(Em, Tm)--either negligible or, at least, such that the subsequent modification of the type and extent of each hazard zone beyond the location(s) of the rock fall protection(s) results in a much milder threat and, consequently, less restricting the land use.  
However, as briefly stated in the introduction to this paper, during the life span of a protection measure, many factors can, in principle, generate flaws and causes of malfunctioning over the years [19; 20] and it is only under the condition of an optimal performance of the measure that its effect can be properly accounted for in hazard zoning (so that land use planning can be adjusted accordingly). Therefore, maintenance of the engineering solutions adopted is as crucial a point to be tackled as their correct design, to ensure the hazard mitigation desired.
# Soil Classification from Piezoone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory
## 5 Conclusions  
A new soil classification system using FCM clustering and the neuro-fuzzy technique with piezocone test results was developed, and the main findings are summarized as follows:  
1. FCM clustering of the local database suggested that three input parameters of \(q_{t}\), \(R_{f}\), and \(B_{q}\) combined with three soil groups, i.e., clay, silt, and sand presented the highest success rate of 74.0%, and the m value representing the partition was optimized as 4.
2. The neuro-fuzzy model was developed on the basis of the FCM clustering results with three input parameters \(q_{t}\), \(R_{f}\), and \(B_{q}\) and three output classes, i.e., clay, silt, and sand. The training procedure was performed with a total of 5173 data points using various combinations of fuzzy membership functions. As a result, a maximum success rate of 79.09% was shown when the triangular membership function for \(q_{t}\), Gaussian membership function for \(R_{f}\), and sigmoidal membership function for \(B_{q}\) were applied.
3. [PERSON] and [PERSON]'s method revealed low applicability when the penetration pore pressure by piezocone was negative or the same as the hydrostatic pressure since this method does not consider the pore pressure as an input parameter. The two charts presented by [PERSON] et al. sometimes failed to classify the upper reclaimed sand layer and interbedded sand or silt layer. Since [PERSON]'s method was adjusted to best match [PERSON]'s diagram, both methods tended to yield essentially similar soil classification results. However, since [PERSON]'s method expressed a single soil classification using the overlapping fuzzy membership in [PERSON]'s two diagrams and limited the maximum value of the fuzzy membership to 0.8, the soil classification result from [PERSON] did not always match that of [PERSON] et al.
4. The suggested neuro-fuzzy model matched well with boring logs and provided a better agreement with the classification in Korea. In addition, it has strong advantages in terms of revising or updating the model when the database is supplemented with new data.  
Conceptualization, Y.-S.K. and C.-H.K.; methodology, Y.-S.K. and C.-H.K.; validation, Y.-S.K. and [PERSON]-S.M.; investigation, C.-H.K.; writing--original draft preparation, Y.-S.K. and C.-H.K.; writing--review and editing, Y.-S.K. and [PERSON]-S.M.; supervision, Y.-S.K.; project administration, Y.-S.K.; funding acquisition, Y.-S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Oceans and Fisheries, Korea, grant number 20180323.  
Not applicable.  
The authors declare no conflict of interest.
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 4 Discussion and Recommendation
### Insufficient Spectral Coverage  
The unwanted emissions of electronic and electric equipment is generally stronger on lower frequencies and the propagation of man-made noise from the noise source to the sensor depends on the propagation, which is also frequency dependent. Consequently, the man-made noise level will be frequency dependent. There is some debate however, whether the straight parallel lines in the double-logarithmic graph of Figure 1 correctly represent the debit of the noise level with increasing frequency.  
When regarding the high number of measurements (90,205), one would expect sufficient measurement points to redraw these graphs. However, a closer look at the data set reveals that the number of frequencies observed is limited and that not every country has measured over the entire spectral range. This is depicted in Figure 8. Table 5 shows the number of frequencies per environment in each of the countries. The USA, UK and Spain measured on 3 frequencies or less. This spectral data scarcity makes it very difficult (or impossible) to derive good models from the empirical data alone.  
It is noteworthy that one major shortcoming of the data sets is caused by the tendency to measure very few frequencies in a frequency range of interest. This is often done because of efficiency or due to technical limitations. This makes it impossible to capture the relationship between noise level and frequency. If time is limited, instead of doing 10,000 measurements always on the same 3 frequencies, it would be much more productive to select 3 different random frequencies at each measurement location, as that will provide a picture of the overall spectrum of the radio noise.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 7 Performance Evaluation
### Experimental Results
#### 7.3.1 Impact of Task Inter-Arrival Time
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Case ID** & **1/A** & \(\overline{t}_{total}\) & \(\overline{t}_{tx}\) & \(\overline{t}_{queue}\) & \(\overline{t}_{process}\) \\ \hline Only-Cloud & 2.1 & 5 & 47.31 & 0.199 & 1 & 0 \\ \hline Only-Cloud & 2.2 & 20 & 63.66 & 0.282 & 1 & 0 \\ Only-Cloud & 2.3 & 40 & 71.04 & 0.358 & 1 & 0 \\ \hline AGG-1 & 2.1 & 5 & 209.27 & 153.71 & 13 & 17.46 \\ \hline AGG-1 & 2.2 & 10 & 138.02 & 94.43 & 12 & 20.8 \\ \hline AGG-1 & 2.3 & 15 & 107.07 & 73.59 & 7 & 20.76 \\ \hline AGG-2 & 2.1 & 5 & 200.54 & 160.24 & 1 & 17.45 \\ \hline AGG-2 & 2.2 & 10 & 131.07 & 97.19 & 2 & 20.77 \\ \hline AGG-2 & 2.3 & 15 & 106.64 & 75.82 & 2 & 20.56 \\ \hline ADP & 2.1 & 5 & 194.17 & 149.76 & 0 & 16.27 \\ \hline ADP & 2.2 & 10 & 128.57 & 88.57 & 0 & 20.4 \\ \hline ADP & 2.3 & 15 & 91.34 & 59.75 & 0 & 20.87 \\ \hline Q-OPT & 2.1 & 5 & 94.96 & 33.96 & 0 & 20.73 \\ \hline Q-OPT & 2.2 & 10 & 73.05 & 35.7 & 0 & 20.35 \\ \hline Q-OPT & 2.3 & 15 & 70.44 & 39.34 & 0 & 19.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Impact of task inter-arrival time on temporal results for task completion. Figure 8: Impact of task inter-arrival time on task delay composition. Figure 9: System delay and deadline distribution for ADP.