esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Model Setup  
The meteorological driving data for the historical period were prepared using observations from the site combined with Water and Global Change forcing data (WFD: [56], 1958-2001) and WATCH-ForcingData-ERA-Interim (WFDEI: [57], 1979-2012) reanalysis data for the grid cell containing the site [45]. The WFDEI data were corrected to match the observations for the overlapping time periods, and then, the WFD was corrected to match the WFDEI. This results in a continuous time series of 3-hourly meteorological data from 1901 to 2016. Meteorological observations from the Abisko Research Station were used for correction. However, the air temperature was reduced by 1 \({}^{\circ}\)C to account for the differences between the mire and the Research Station [45]. Calculations from the air temperatures used in the driving data give 525 growing degree days above 5 \({}^{\circ}\)C, an index of plant growth. Snowfall is highly uncertain--here, we estimated the observed snowfall from the observed snow depth by treating any increase in depth as a snowfall event with an assumed snow density [45]. The snow depth at the Research Station is only available every 5-days, meaning there could be compaction of the snow between readings. In addition, this site is a relatively warm permafrost site with potential for melt events during winter; therefore, a relatively high density of 240 kg m\({}^{-3}\) was assumed. The snow depth at the Research Station differs from that at the mire; so, we included an additional correction by scaling the snowfall according to the ratio of monthly snow depths at the mire compared with the Research Station [45]. All the other meteorological observations were taken directly from the Research Station data.  
Vertically resolved soil thermal and hydraulic properties for JULES were derived from the measured profiles of soil carbon and bulk density. Firstly, the volumetric fraction of organic matter in the soil was estimated using carbon content, and secondly, organic and mineral soil properties were combined as described in [58]. The texture of the mineral component of the soil in the mire was assumed to be silt. JULES was set up to have 14 soil layers increasing in thickness from the surface to a maximum soil depth of 3 m (thickness of layer \(n=0.05n^{0.75}\)).
## 3 Results
### TCE Attenuated Dense Granule Release  
Dense granules within platelets store calcium ions alongside small molecules, such as ADP, ATP, and serotonin, which function as key markers of platelet activation. When activated, platelets rapidly release their dense granules through a mechanism consistently initiated by various agonists [28]. In this study, collagen was used as an agonist to evaluate the dense granule release in platelets. Collagen stimulation at 2.5 \(\upmu\)g/mL significantly elevated serotonin release from 44.1 \(\pm\) 25.5 nM to 388.7 \(\pm\) 45.8 nM and ATP release from 1.0 \(\pm\) 0.2 \(\upmu\)M to 20.17 \(\pm\) 1.58 \(\upmu\)M. Conversely, treatment with TCE significantly and dose-dependently suppressed the release of serotonin and ATP (Figure 2A,B). Specifically, TCE at concentrations of 12.5, 25, 50, and 100 \(\upmu\)g/mL reduced serotonin levels to 191.2 \(\pm\) 8.9 nM, 71.5 \(\pm\) 4.7 nM, 48.5 \(\pm\) 6.5 nM, and 35.6 \(\pm\) 2.0 nM, respectively. Similarly, ATP levels decreased to 12.53 \(\pm\) 1.0 \(\upmu\)M, 7.03 \(\pm\) 0.34 \(\upmu\)M, 5.1 \(\pm\) 0.37 \(\upmu\)M, and 3.6 \(\pm\) 0.37 \(\upmu\)M, respectively. Moreover, collagen-induced cytosolic calcium elevation, mediated by IP\({}_{3}\)R\({}_{1}\) channels, increased from 56.3 \(\pm\) 1.9 nM to 166.7 \(\pm\) 38.7 nM. However, pretreatment with TCE dose-dependently inhibited this calcium increase (Figure 2C).
## 2 Related Work  
### Lightweight Single-Architecture Super-Resolution Network  
In recent years, numerous studies have prioritized developing SR models that balance performance and efficiency under such resource-constrained conditions. Conventional approaches typically adopt single-architecture networks, which rely on monolithic ar chitectures (CNN or Transformer) and attempt to achieve lightweight designs through architectural refinements and component-level innovations.  
In the early stages of research, scholars focused on lightweight design strategies for convolutional neural networks. SRCNN [3] is the first CNN-based framework for single-image super-resolution. FSRCNN [4] eliminated preprocessing-stage interpolation by operating directly on low-resolution inputs, significantly accelerating training. VDSR [5] and EDSR [6] both drew on the principles of residual learning to accelerate model convergence and enhance training efficiency. The emergence of attention mechanisms has partially alleviated the CNN's receptive field limitations. RCAN [26] enhanced feature representation by integrating residual channel attention. HAN [27] combined layer-wise and channel-spatial attention to comprehensively model inter-feature dependencies, achieving notable performance gains. With the rise of vision Transformers [28], researchers have begun integrating Transformer-based global modeling into lightweight SISR designs. SwinIR [12] combined local feature modeling with global attention via a sliding-window mechanism, reducing complexity while enhancing reconstruction fidelity.  
While single-architecture SR networks offer simplicity and ease of optimization, relying on a singular technical approach can expose inherent limitations. These shortcomings have paved the way for the development of hybrid architectures.
## 2 Materials and Methods
### Plot Selection and Sample Collection  
Three typical larch-forest plots were selected within the radiation range of the flux tower (Table 1), with each plot size of 20 m \(\times\) 30 m. When selecting plots, we ensured that the differences in slope and slope direction were minimal to eliminate the topographic factor influence on WUE\({}_{\text{s}}\). The diameter at breast height (DBH) and tree height (H) were measured for each tree whose DBH was greater than 5 cm in each plot, and the average DBH and H for three plots were calculated independently. Then, three sample trees with DBH values respectively similar to the average DBH and H of the three plots were selected to represent the average state of each plot. The canopy of the sample tree was evenly divided into three parts (upper canopy 20 m, middle canopy 14 m and lower canopy 9 m) based on the top-down pseudowheel [32] in the crown, the center part of each canopy was sampled (Figure 2). The analysis of our previous work showed that there were no significant differences in carbon isotopes between negative and anophytic leaves in four directions: the southeast and northwest directions of each canopy, and the needles in the middle part of each canopy represent the average standard for each canopy WUE\({}_{\text{s}}\).  
Figure 1: Representation of the study site and sampling points.  
To ensure that the leaves can reflect the average WUE\({}_{\text{s}}\) for three days, we performed sampling from 18 to 20 August 2019 to meet the requirements that there is no rainfall for three consecutive days before sampling and no rainfall during the three-day sampling period. During sampling, using 20 m high pruning at each canopy (20 m, 14 m and 9 m), we chose three healthy and fully expanded needle clusters located within each segment in the middle of the foliated branches in sunny, semisunny and shaded crowns that were selected. Photosynthetic parameters were determined immediately after the sampling. After measurement, samples were quickly wrapped in tin foil and stored in a liquid nitrogen tank at low temperature for extraction and testing of leaf soluble sugar and leaf structure.  
At the same time of each sampling day, atmospheric samples were collected from 20 m, 14 m and 9 m at the larch-forest flux meteorological tower using a gas collect bag and brought back to the laboratory to determine their CO\({}_{2}\) concentration (Ca) and stable carbon isotopes (\(\delta^{13}\)C).
###### Abstract  
Damage identification, both in terms of size and location, in bridges is important for timely maintenance and to avoid any catastrophic failure. An earlier experimental study showed that damage in a steel box girder orthotropic plate can be successfully detected using the measured vibration acceleration data. In this study, the wavelet packet decomposition (WPD) method is used to analyze the measured vibration acceleration responses and then the estimation of the permutation entropy (PE) on the re-constructed signals. A damage index is then defined based on the permutation entropy difference (PED) between the damaged and the healthy conditions to detect the location and size of the damage. The method is further validated through the finite element (FE) model of a steel box girder and the computed vibration acceleration responses when subjected to the sinusoidal excitations at different frequencies. In addition, the robustness of the methodology under different white noise interference conditions is also verified. The results show that the proposed methodology can effectively identify the location of human-made damage and accurately estimate the degree of damage under different frequencies of sinusoidal excitation. The method has shown a strong anti-noise property.  
steel box girder; orthotropic plate; wavelet packet decomposition (WPD); damage identification; permutation entropy (PE); permutation entropy difference (PED)
## 10 Conclusions  
This paper presented the use of a nonlinear least squares Levenberg-Marquardt algorithm to measure nonsynchronous rotor blade vibrations in the LP the last stage of a 380 MW steam turbine with various condenser pressures in non-nominal regimes. This algorithm could find synchronous and asynchronous multimode blade vibrations using two sensors in the casing, and a once-per-revolution sensor.  
The blade vibrations were analyzed with condenser pressures ranging from 7.4 to 19 kPa (299.1-300 MW) and from 5.9 to 18.7 kPa (219.5-219 MW). This covers the entire non-nominal work range of a 380 MW steam turbine LP last stage, which is a novelty. This paper showed that rotor blade amplitude was higher for 365 MW than for 300 MW.  
The rotor blade amplitude was higher for 300 MW than for 219 MW. Flutter did not appear in the LP last stage for the condenser pressures and powers analyzed in this paper.  
However, this analysis has shown that rotor blades vibrate with two modes in non-nominal conditions which is a novelty. The rotor blade frequencies are similar in all non-nominal conditions, although the blade amplitude depends on condenser pressure. The coupling between the first and second blade modes is higher with lower turbine power as a result of greater flow unsteadiness.  
A comparison between the one-mode and L-M multimode algorithms has shown that blade vibration amplitude is 29% higher in the former. This is because the one-mode analysis is an approximation of blade displacements comprising a few modes, and this indicates the higher accuracy of the L-M multimode algorithm, a finding that could be very important in calculating blade vibration amplitudes in terms of blade life. The procedure of calculating life is presented in [35].  
Figure 23: SDOF [27] and MDOF 1 st mode amplitudes for 53 blades with 10.67 kPa condenser pressure (Measurement 1).  
**Author Contributions:** Conceptualization, R.R. and P.T.; methodology, P.T. and L.K.; software, P.T.; validation, P.T., J.M. and M.K.; formal analysis, P.T. and R.R.; writing--original draft preparation, R.R., P.T. and L.K.; writing--R.R., P.T. and L.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by NCBiR Poland POIR.04.01.04-00-0116/17.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
A Amplitude, m  
BTT Blade tip timing  
EO Engine order  
SDOF Single degree of freedom  
MDOF Multiple degrees of freedom  
f Frequency, Hz  
LP Low-pressure  
NLS Nonlinear least squares  
L-M Levenberg-Marquardt  
OPR Once per revolution
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 4 Results
### Comparisons  
Only a few works propose compensators for Chebyshev sharpening combs [10; 11; 12]. However, we also include a comparison with one of the most recent works, in which another type of sharpening is applied to improve the passband and stopband of comb filters.
# FSOpt_k: Finding the Optimal Anonymization Level for a Social Network Graph
## 3 Problem Statement  
As explained in the introduction, the algorithms presented in the k-degree-anonymity field have the following challenges:  
* For all social network graphs, the input k value is determined independently and regardless of the graph structure to be anonymized. This may impose a higher level of privacy than the graph requires, thereby reducing the graph's utility too much. Additionally, the amount of privacy level applied to the graph may not be sufficient and lead to the disclosure of sensitive information of users. For this reason, it is necessary to determine the privacy level of the graph according to the characteristics of that graph and automatically by the algorithm itself, and in this way, a balance between the level of privacy and the utility of the graph is satisfied.
* The value of the k parameter, the anonymization and privacy level, should be specified as the algorithm input, which is often difficult to find the appropriate k value of the graph for the data owner. As explained before, improper selection of k can reduce privacy or increase information loss and reduce graph utility. Therefore, an algorithm should be presented that has the following properties:  
\begin{table}
\begin{tabular}{c c c c} \hline
**References** & **Graph Type** & **Method** & **Characteristics** \\ \hline
[44] & Simple, undirected & k-degree sequence anonymity & Edge Modification \\ \hline
[32] & Simple, undirected & k-core anonymization & Edge Modification \\ \hline
[36] & Labelled, directed & k-anonymity and l-diversity & Vertex and Edge Modification \\ \hline
[35] & Dynamic, directed & k-in\&out-degree anonymity & Vertex and Edge Modification \\ \hline
[37] & Simple, undirected & k-degree sequence anonymity & Edge Modification \\ \hline
[39] & Simple, undirected & k-anonymity and l-diversity & Edge Modification \\ \hline
[40] & Simple, undirected & k-anonymity & Edge Modification \\ \hline
[41] & Simple, undirected & k-degree sequence anonymity & Edge Modification \\ \hline \end{tabular}
\end{table}
Table 1: The comparison of some recent methods in graph modification techniques.  
* It should determine the optimal value of the k parameter by the algorithm itself. This way, the data owner's concerns about the appropriate level of graph privacy are decreased.
* In determining the k value, it should consider the features of the graph degree sequence. As discussed before, considering the graph structure in determining the privacy level of the graph can increase the anonymization of sensitive information and decrease information loss. Therefore, the utility of the anonymized graph increases.  
For this purpose, in this paper, we present an algorithm that calculates the optimal value of k for a given value of the graph's utility with respect to its degree sequence and then anonymizes the social network based on the level of privacy obtained (i.e., the value of k). In the following section, the steps of this algorithm will be explained and discussed in detail.
# A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System
## 3 A Hybrid State Estimator Based on SCADA and PMU Measurements
### The Improved Robust State Estimation with Normalized Residuals and Adaptive Thresholds
 \tag{10}\]  
where \(\overline{\mathbf{W}}\) represents the modified measurement weight value and \(\mathbf{J}=\mathbf{H}\left(\mathbf{H}^{\mathsf{T}}\overline{\mathbf{W}} \mathbf{H}\right)^{-1}\mathbf{H}^{\mathsf{T}}\overline{\mathbf{W}}\). In the case that real-time measurements are not enough, pseudo-measurements are usually added to ensure the observability. The accuracy of the pseudo-measurements is different from that of the real-time measurements; besides, the measurement accuracy of SCADA and PMU are also different, thus the variances of residuals are not the same. In this paper, we use the internal student residual method to standardize the residuals as given in Equation (11):  
\[\left\{ \begin{array}{l}r_{s}=\frac{l\sqrt{1-\mathbf{J}_{ii}+\varepsilon}}{ \sqrt{\mathbf{R}^{-1}}}\\ r_{s,\;i}=\frac{r_{i}}{r_{s}}=\frac{r_{i}\sqrt{\mathbf{R}^{-1}}}{l\sqrt{1- \mathbf{J}_{ii}+\varepsilon}}\end{array} \right. \tag{11}\]  
where \(l\) represents the scale parameter of the standardization and \(l^{2}=r^{\mathsf{T}}R^{-1}r/(m-n)\), \(m\) represents the number of measurements, \(n\) represents the number of state variables, and \(\varepsilon\) is a very small number to avoid \(1-\mathbf{J}_{ii}=0\).
## 3 Results
### Site Assessment  
#### 3.3.1 Evaluation of CRs Sites  
All the CR sites were evaluated with respect to normalized RCS. The theoretical maximum RCS (\(\mathrm{RCS_{T}}\)) at C-band of a triangular trihedral CR of 1.5 m is equal to 38.378 \(\mathrm{dbm^{2}}\) and is derived by the following equation [58]:  
\[\mathrm{RCS_{T}}=\frac{4\pi\alpha^{4}}{3\lambda^{2}} \tag{8}\]  
where \(\alpha\) is the inner-length dimension of the CR and \(\lambda\) is the signal's wavelength.  
The backscatter coefficient (\(\sigma^{0}\)) of a distributed target is the ensemble average of the RCS in decibels per illuminated area (A), as follows [59]:  
\[\sigma^{0}=\frac{\mathrm{RCS}}{\mathrm{A}} \tag{9}\]  
The illuminated area can be calculated using the following equation [22]:  
\[\mathrm{A}\ =\frac{\rho_{r}\cdot\rho_{a}}{\mathrm{sin}\vartheta} \tag{10}\]  
where \(\rho_{r}\) and \(\rho_{a}\) are the slant range and azimuth pixel spacing, respectively, and \(\vartheta\) is the local incidence angle. Therefore, the normalized RCS of any point target in a SAR image can be estimated using the equations mentioned above. [PERSON] et al. [22] noted that an RCS of 34-38 \(\mathrm{dbm^{2}}\) using an SAR image of coarser beam modes (e.g., IW swath) at C-band, is the suitable range for a CR of 1.5 m.  
Figure 20: The ALEV site’s GNSS and CR (for the descending pass) collocation.  
The evaluation process started on 16 June 2021, with the RCS of each CR extracted from the converted sigma 0, using thirty SAR pictures of ascending and descending orbit directions, respectively. Sentinel-1A and B SLC products with IW swath mode and VV polarization were analysed using SNAP software. The average normalized RCS for each CR is presented in Table 14.  
A representative example of the backscatter coefficient before and after the CRs installation is illustrated in Figure 21.
# Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning
## 1 Introduction  
Since the time of [PERSON], marketing issues have addressed, either directly or indirectly, the marketing mix model, which deals with the creation and implementation of product, pricing, promotion, and distribution strategies [1]. This article addresses the issue of building a pricing strategy within the specific model of dynamic pricing. This model has been successfully adopted in many industries [2, 3].  
It appears that the popularity and utility of dynamic pricing issues will continue to grow [4]. This is due to the ability to collect diverse data on transactions across many industries. This article focuses on the e-commerce sector, where it is particularly easy to gather a range of information about customers and their behaviors and purchasingpreferences. This trend is also supported by the dynamic development of machine learning (or, more broadly, artificial intelligence), for which the collected data serve as fuel, enabling accurate prediction, classification, and evaluation of phenomena and transactions in the modern economy.  
Given the presented premises, the aim of this article is to propose a dynamic pricing method for the e-commerce industry. The developed method also serves as a decision support system for managers in the dynamic pricing model. With this system, a manager can receive recommendations on whether the price of a given product in the online store should be increased, decreased, or maintained at the current level. The developed method takes into account criteria from various perspectives--customer-related, enterprise-related, and market condition-related criteria. Effective pricing enables the optimization of outcomes both financially and in terms of product performance.  
To achieve this goal, well-known machine learning methods were used, such as the Naive Bayes classifier, support vector machines (both linear and nonlinear), decision trees, and the k-nearest neighbor algorithm. The structure of this paper is as follows: Section 2 provides a brief literature review on dynamic pricing in marketing theory. Section 3 describes the applied dynamic pricing method using selected machine learning mechanisms. Section 4 presents the results of empirical research, verifying the method's effectiveness based on data containing the transaction history of 500 customers from a selected e-commerce store. Finally, Section 5 concludes the article with a summary and outlines future research directions.