esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodologies
### KL Expansion  
In order to solve the uncertainty problem caused by a large number of parameter inversions, this study uses KL expansion to reduce the dimension of the parameter space. This powerful technique decomposes a random field into a series of uncorrelated random variables and deterministic coefficients. The key advantage of KL expansion lies in its ability to represent a complex random field with just a few parameters, leading to low estimation errors and broad applicability. Assume that the two-dimensional random field of saturated hydraulic conductivity \(K_{\rm S}(x,\omega)\) has a mean value of \(\overline{K_{\rm S}}(x,\omega)\), and that all spatial points on \(E\big{[}K_{\rm S}(x,\omega)-\overline{K_{\rm S}}(x,\omega)\big{]}^{2}\) all exist; then, its KL expansion is described as Equation (9):  
\[K_{\rm S}(x,\omega)=\overline{K_{\rm S}}(x,\omega)+\sum_{i=1}^{\infty}\sqrt{ \lambda_{i}}f_{i}(x)\xi_{i}(\omega) \tag{9}\]  
where \(x\) is the spatial point coordinates; \(\xi_{i}(\omega)\) is a random field generated using a Gaussian distribution, where \(\omega\) is the parameter randomness and in this paper the value is all the same in different samples for control variable; \(\{f_{n}(x)\}\) is the eigenfunction series, and \(\{\lambda_{n}\}\) is the series of eigenvalues.  
\(\{f_{n}(x)\}\) and \(\{\lambda_{n}\}\) are the solution to Fredholm integral equation of the second kind.  
\[\int_{\Omega}\rho(x_{1},x_{2})f_{i}(x_{1})dx_{1}=\lambda_{i}f_{i}(x_{2}) \tag{10}\]  
where \(\Omega\) is the region of integration. The eigenfunction can be decomposed into a linear combination of M orthogonal basis functions.  
In the generation of a random field through the use of KL expansion, a set of M (the truncation order of KL expansion) mutually independent random variables, denoted by \(\xi_{i}(\omega)\), is produced. The eigenfunctions and eigenvalue series are obtained through the solution of the integral equations, \(\{f_{n}(x)\}\), \(\{\lambda_{n}\}\). Once the integral equation has been solved to yield the eigenfunction and eigenvalue series, the value of the Ks random field is generated by Equation (9), thereby enabling the \(K_{\text{s}}\) field to be represented by a small number of M [36].  
Figure 1 depicts the magnitude of the eigenvalues for the first 50 orders. It can be seen from the figure that the eigenvalue size decreases rapidly as the order increases, and the eigenvalue sizes of the lower orders are much higher than the higher-order values as a percentage of the eigenvalue size. Therefore, a small truncation order M can be used to approximate the original complete eigenvalue, which can greatly reduce the number of parameters and also ensure the accuracy of the overall KL expansion on behalf of the random field, which greatly reduces the number of parameters required for the inversion. In this paper, the truncation number of the eigenvalues used is M = 30, which contains more than 99% of the total eigenvalues' size.
## 2 Problem Description
Attitude transition time constraint: When two adjacent tasks, \(i\) and \(k\), are observed by satellite \(j\), the time gap between the two tasks should not exceed the satellite attitude transition time, \(tr_{ijk}\): \[te_{i}+tr_{ikj}\leq ts_{k},\] (6) where the transition time, \(tr_{ikj}\), is the ratio of angles for observing the two tasks on satellite \(j\) at the start time of the observation and the satellite maneuvering velocity. The attitude transition time, \(tr_{ikj}\), is calculated as follows: \[tr_{ikj}=\max\left\{\frac{\left|\theta_{pij}-\theta_{pij}\right|}{v_{p}},\frac {\left|\theta_{rij}-\theta_{rij}\right|}{v_{r}}\right\},\] (7)where \(\theta_{pij}\) is the pitch attitude angle to task \(i\) observed by satellite \(j\), \(\theta_{rij}\) is the roll attitude angle to task \(i\) observed by satellite \(j\), and \(v_{p}\) and \(v_{r}\), are the angular velocities of the pitch and roll maneuvers, respectively. Based on the assumptions, objective function, and constraints of the problem, we present a detailed solving method in Section 3.
## 4 Discussion
### Relationship between BIA Vector Changes and CK  
As shown in Figure 6, there was a high correlation between the kinetic of the CK and the kinetic of muscle-localized R/h, which opens a new line of pioneering research in sports science.  
Regarding CK, not only was there a significant increment at POST (53.4 \(\pm\) 29%), but also at POST24H compared to POST (34.9 \(\pm\) 29.5%) and to PRE (109.2 \(\pm\) 76.4%), which would indicate that the muscles were not fully recovered and that the levels of CK were still increasing during the 24 h after the training ended, as seen in other studies [7].  
A possible explanation for this correlation may be that the intracellular water is expelled from the inside of the cell due to the muscle damage caused by training, which would cause an increase in extracellular water and a reduction in the localized resistance, while the levels of CK in the blood increase during the high-intensity exercise.  
However, it is important to be cautious with this correlation since, as was previously mentioned, the muscle-localized R/h changes were not significant. Therefore, we must ask ourselves to what extent the correlation between a significant change and a non-significant change is reliable. It could be possible in the case of athletes who were more sensitive to CK than R/h adaptations.  
No correlation with localized reactance was obtained, which is surprising considering it is a parameter related to cell integrity that is affected by muscle damage and it would have made more sense to have a relation to CK rather than localized resistance.  
The lack of studies published in the scientific literature that apply BIVA to assess changes in muscle hydration and CK induced by a training session prevent the present findings from being currently reliable in practice.
# DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain
## 3 System Preview
### Definitions
#### 3.2.2 The Key Encryption Key Tree  
The key encryption key (KEK) tree is a complete binary tree constructed based on the user set [41], as shown in Figure 3. KEK tree can provide a non-revoking user update function to achieve attribute revocation. For example, suppose the system user set is \(\text{User}=\{u_{1},u_{2},\ldots,u_{n}\}\), and the system attribute set is \(U=\{att_{1},att_{2},\ldots,att_{m}\}\). Then, the steps for the system to construct a KEK tree are as follows:  
1. Each user in the user set User is designated in the leaf node of the binary tree, and each node stores a random value \(\theta_{i}\);
2. Path node generation algorithm \(Path(u_{i})\): For any user \(u_{i}\), all nodes passing through the path from its corresponding leaf node to the root node are defined as the path nodes of user \(u_{i}\);
3. The minimum coverage set algorithm \(Mincs(G_{j})\): For the attribute group \(G_{j}\) with the attribute \(att_{j}\), the minimum set of nodes in the KEK tree covering all users of \(G_{j}\) is the minimum coverage set;
4. Calculate the intersection of \(Path(u_{i})\) and \(Mincs(G_{j})\): If the user has the attribute \(att_{j}\), then the intersection has only one node \(V_{k}\). \(\theta_{k}\) is a random value stored in node \(V_{k}\). If the user has no attributes \(att_{j}\), then the intersection set is empty.  
Figure 2: The attribute-weighted access tree.
# Improving Low Earth Orbit (LEO) Prediction with Accelerometer Data
## 4 Results of LEO Orbit Prediction
The RMS values of LEO orbits for three directions with another one hour prediction and the corresponding URE values are shown in Figure 12.  
We could easily find that the method with accelerometer data with one set of biases and scales for each direction (ACC_B_K) still shows the most stable results while the method with empirical models (EMP) fluctuated, especially in the radial component. The RMS value for the along-track component could be at one meter for the method with empirical models while it is still better than 10 cm for the method with one set of biases and scales. Figure 11: URE for every 10 min over a one hour orbit prediction. Figure 12: _Cont._
# Seismic Performance Evaluation of Steel Buildings with Oil Dampers Using Capacity Spectrum Method
## 4 Application of the Proposed CSM on Steel Frame Buildings
### Description of Target Buildings  
Steel buildings, both 4-story and 10-story, were selected from the JSSI manual [17; 31]. The buildings were designed as steel moment-resisting frames to resist the gravity loads, while oil dampers were considered to control the response under the earthquake ground motion. The plans and elevations of the buildings are shown in Figure 6. The ground floor height is 6 m, and the typical floor height is 4 m for both target buildings. The sizes of columns and beams are summarized in Tables 3 and 4.  
Figure 4: (**a–d**) steps of the proposed capacity spectrum method (CSM) applied to the ESDOF system using the damping-adjusted design spectra of a Level-2 earthquake.  
Figure 5: Multi degree of freedom (MDOF) with oil dampers corresponding to the first model of vibration.  
Figure 6: Target buildings plan, elevation and oil damper configuration: (**a**) 4-story building: (**b**) 10-story building.  
The steel grade is SN490B with a yield strength of 325 MPa and a tensile strength of 490 MPa. The arrangement of oil dampers in the plan and the evaluation of the building is shown in Figure 6. Since the oil dampers were diagonally installed in a longitudinal direction only, the following analysis is limited to the longitudinal direction. The technical parameters of oil dampers are presented in Tables 5 and 6.
## 1 Introduction  
Many studies have attempted to solve pedestrian tracking problems, but challenging issues still remain due to a range of factors, such as Region of Interest (ROI) size and background variation, background clutter, pedestrian motion, and drifting of object. Mean-shift is a kernel object tracking of non-rigid objects based on visual features, such as color and/or brightness, whose statistical distributions characterize the object of interest [1]. In the case of infrared pedestrian tracking, pedestrians, whose images are captured by a single stationary infrared camera, are represented based on the pixel brightness. In particular, [PERSON] has raised the opinion that mean-shift-based IR object tracking becomes increasingly difficult due to the target variations, background clutter, and the lack of homogeneity of brightness in the background area [2]. Extracting the characteristic information of the object separated from the background is very difficult when the brightness values of the background and object are equal. Previous studies have focused on methods using image pre-processing [3; 4; 5], optimizing parameter settings [6; 7; 8] and combining tracking algorithms [9; 10; 11]. The conventional methodologies, such as brightness-based baseline methods, have limitations in solving poor imaging contrast issues [12]. On the other hand, the temperature data is unaffected directly by the image contrast and target variation because the pedestrian temperature does not change abruptly and is limited to a specific thermal range with thermoregulation [13]. Therefore, this paper addresses the pedestrian tracking method using the temperature, which is a further development from previous work.  
This paper presents three main contributions. First, the temperature information feature is used based on the 14-bit digital count value instead of converting the histogram based on the existing eight-bit relative brightness feature. This method is robust against background change, poor image contrast, and target variation when the temperature information feature is applied to the mean-shift. Finally, if there is no temperature information, the histogram bin is extracted from the digital count to assess the tracking performance and speed improvement.  
The remainder of this paper is organized as follows. Section 2 briefly introduces previous studies related to the IR brightness-based method weak point of brightness. Section 3 outlines the proposed temperature infrared mean-shift called TIR-MS. Section 4 introduces various experimental results and finally, Section 5 reports the conclusion.
## 1 Introduction
We obtain typical conversion efficiency of \(-3.6\) dB for OOK and \(-5.4\) dB for DPSK. Those values of conversion efficiency outperform previous reported results while requiring much lower pump powers [8,10]. In [10] many compromises were made to achieve polarization independence, at the cost of a bit error floor and extremely limited tuning range. We establish the viability of our proposal to obtain good phase noise performance with QD-MLL pumps in an architecture that assures phase stability during tuning. In addition, the spectral efficiency we report is higher than previous reports in the literature, when combined with a wide tuning range and moderate pump power. We also highlight the particular challenges in achieving this performance when passing from OOK to DPSK modulation. This paper is organized as follows. In Section 2, we review the structure and principle of operation of the proposed converter. In Section 3, we present the experimental setup. Experimental demonstrations and results are provided. We conclude the paper in Section 4.
## 2 Methodology
### Source-Time Function Estimation
\tag{9}\]  
where, \(S(\mathbf{x}_{r},t_{i})\) indicates the denoised time sequence of the \(r\)th windowed and aligned waveform at the time \(t_{i}\) (\(r=1\), \(2\),..., \(M\), and \(i=k\), \(k+1\),..., \(k+N-1\)), \(k\) represents the start point of the windowed waveform. \(\overline{S}(w)\) and \(\overline{S}(at)\) separately stand for the Fourier transforms of \(S(t)\) and \(S(at)\), in which \(a\) is a scaling factor in time domain. The optimal source-time function can be estimated by updating the two parameters \([u,a]\) searching for the maximum value of normalized cross-correlation coefficient between recording waveform and synthetic waveform modeled from corresponding source-time function.
## 5 Conclusions  
This work provides the first evidence of the effectiveness of mycoremediation for the removal of TCE from soil. The two fungi assessed in this work, _P. ostreatus_ and _P. eryngii_, were able to grow and produce ligninolytic enzymes in the presence of a very high concentration of TCE in both liquid broth and soil. Both fungi colonized soils with TCE concentrations that were twice the maximum amounts allowed for industrial use according to Spanish regulations. The two fungi clearly enhanced the TCE dissipation in the soil produced by autochthonous microbiota and caused the complete removal of TCE. However, more research should be done to clarify the complex factors and mechanisms that influence the mycodegradation of TCE in soil, which is probably one of the most difficult environmental matrices from which to clean pollutants.  
Conceptualization, B.M., R.C.-A., C.G.-D., N.N. and E.E.; Formal analysis, B.M., R.C.-A., C.G.-D. and R.A.-H.; Funding acquisition, E.E.; Investigation, B.M., R.C.-A., C.G.-D. and R.A.-H.; Methodology, B.M., R.C.-A., C.G.-D. and E.E.; Project administration, E.E.; Resources, C.A. and N.N.; Supervision, E.E.; Writing--original draft, B.M., R.C.-A. and C.G.-D.; Writing--review and editing, B.M., R.C.-A., C.G.-D., R.A.-H., C.E. and E.E. All authors have read and agreed to the published version of the manuscript.  
This study is a part of the research agreement \"Mycoremediation of polluted soils with PAH, antibiotics and chlorinated hydrocarbons\" funded by the company Kepler Ingenieria y Ecogestion SL.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** We would like to acknowledge to Kepler Ingenieria y Ecogestion SL for their economic support and for the donation of the soil used in these experiments. We would also like to thank [PERSON] and [PERSON] for their contributions to the discussion of the results. The authors also thank the Laboratorio Regional de Salud Publica de la Comunidad de Madrid for their analytical support.  
**Conflicts of Interest:** This article project is the result of a research project funded by the company Kepler Ingenieria y Ecogestion SL. The coauthors from Kepler participated in the experimental design and the decision to publish it but not in the investigation or the evaluation of the results.
## 4 Conclusions  
The importance of a simultaneous determination of PAEs and BPA in different matrices (alimentary, environmental, biological fluids, etc.) is still an important issue. A further proof of this importance is that only 30 papers have been published since 2015, whereas more than 1000 papers deal with PAE determination in the same period (source: Scopus) and more than 2000 papers deal with BPA determination. Among the different methods investigated for this simultaneous determination, no official approach has been established, while many authors have developed different procedures. The protocol proposed in this study manages to analyze PAEs and BPA simultaneously in beverages contaminated by the release of such compounds from plastic bottles. The protocol does not require toxic solvents (except a small amount of hexane) and also saves the operator and the environment. At the same time, obtaining a pre-concentration factor of 5,5000 allows these compounds to be detected at very low levels in such matrices. To this end, the authors applied the entire procedure to 15 water samples after a long contact with the plastic and identified the presence of DiBP, DBP, DEHP, and DOP at a concentration below 1.2 ng mL\({}^{-1}\) in two water samples.  
Conceptualization, P.A.; methodology, P.A.; software, I.N. and B.B.; validation, I.N. and M.V.; formal analysis, I.N. and C.P.; investigation, I.N.; resources, P.A.; data curation, I.N.; writing--original draft preparation, P.A. and I.N.; writing--review and editing, P.A. and C.P.; visualization, B.B.; supervision, P.A.; funding acquisition, P.A. and M.V.  
This research received no external funding.  
The authors would like to thank Drs. [PERSON] and [PERSON] for their help in the experimental activity and useful comments in the manuscript elaboration.  
The authors declare no conflict of interest.
## 3 Experiments and Results
### Experimental Settings  
The reconstructed image is used to analyze and calculate _NDVI_[27] and _LST_[29]. The approach was applied to a single reference image, and for the reconstruction of satellite images with the presence of clouds, the images are prepared with areas covered by synthetic clouds and presented as inputs. Since LaMa is based on a generator architecture _Res-Net_, the network selects a single reference image.  
As described in Section 2.2.3, the LaMa approach uses masks to reconstruct the region of interest, and the data for training, as described in Section 2.3, are given in pairs (image, mask). For training, the masks were randomly selected to form the input pairs. The LaMa network is trained with forty epochs with a batch size of two and inputs set at 256 \(\times\) 256. In addition, the Big LaMa model is trained, also with forty epochs, a batch size of two and  
Figure 10: Cropping applied in the images to generate patches.  
Figure 9: Deskew applied to generate the resulting image.  
18 residual blocks. The hardware configuration and software environment are listed in Table 3.  
In addition to visual interpretation for the qualitative evaluation analysis, three evaluation metrics were used for the quantitative analysis: root mean square error (_RMSE_), structural similarity (_SSIM_) and peak signal-to-noise ratio (_PSNR_), as described in Section 2.4.
# Securing SCADA Energy Management System under DDs Attacks Using Token Verification Approach
###### Abstract  
The advanced connection requirements of industrial automation and control systems have sparked a new revolution in the Industrial Internet of Things (IIoT), and the Supervisory Control and Data Acquisition (SCADA) network has evolved into an open and highly interconnected network. In addition, the equipment of industrial electronic devices has experienced complete systemic integration by connecting with the SCADA network, and due to the control and monitoring advantages of SCADA, the interconnectivity and working efficiency among systems have been tremendously improved. However, it is inevitable that the SCADA system cannot be separated from the public network, which indicates that there are concerns over cyber-attacks and cyber-threats, as well as information security breaches, in the SCADA network system. According to this context, this paper proposes a module based on the token authentication service to deter attackers from performing distributed denial-of-service (DDoS) attacks. Moreover, a simulated experiment has been conducted in an energy management system in the actual field, and the experimental results have suggested that the security defense architecture proposed by this paper can effectively improve security and is compatible with real field systems.  
SCADA security; DoS attack; token verification; energy management system +
Footnote †: journal: Applied Sciences  
1
# An Overview of Fog Data Analytics for IoT Applications
## 4 Current State-of-the-Art
### Vehicular Networks  
The number of vehicles on the road significantly increases each year. Managing communication more securely and effectively is more difficult due to urbanization and population convergence. A driver mistake is a major factor in many accidents and catastrophes. Because of this, it is crucial to monitor driver behavior [48] continuously. The concept of connected things is popular; it has made its way to the network of vehicles to exchange data using wireless technology and several other devices, such as RSUs and computational resources, present in the vehicles. Data aggregation and communication become even more complicated when multiple sensors, in addition to sensors for the vehicle and surroundings, perform this function. Intuitively, these networks are highly latency-sensitive and need a quick response, which could be accommodated by using fog computing and computational resources present in the vehicles to act as fog nodes. This implementation supports a low latency in data communication between RSUs and vehicles, and vehicle-to-vehicle. communication happens by sending different types of messages to nearby vehicles. These messages could be a query, response, or cooperative, serving the purpose of intelligent data sharing [49]. The vehicular network has great advantages as it helps the driver to safely change the lane, provides estimated congestion at certain points, and suggests alternative routes to avoid congestion. The smart network of connected vehicles implemented with the SDN approach further amplifies its capabilities and advantages [50]. Many traffic signals combined with vehicular networks would result in effective traffic management and a reduced waiting time [51].