esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images
## 2 Methods
### Gaussian Noise Reduction  
The filter second stage consists in the Gaussian noise elimination process. At this step, we propose a weighted fuzzy average procedure over the pixels of the peer group. To this end, let us present a reminder of the definitions of a peer group [12] and a fuzzy peer group [10]. Consider for each pixel \(\mathbf{x}_{0}\), in RGB format, a \(n\times n\) filtering window \(W\) centered at \(\mathbf{x}_{0}\). Let \(\rho\) be a similarity measure function [1]. Using this measure, the pixels \(\mathbf{x}_{i}\in W\) are descendingly ordered according to their similarity to \(\mathbf{x}_{0}\), resulting in a set \(\{\mathbf{x}_{(0)},\mathbf{x}_{(1)},\ldots,\mathbf{x}_{(n^{2}-1)}\}\) satisfying  
\[\rho(\mathbf{x}_{0},\mathbf{x}_{(0)})\geq\rho(\mathbf{x}_{0},\mathbf{x}_{(1)})\geq\cdots\geq \rho(\mathbf{x}_{0},\mathbf{x}_{(n^{2}-1)}),\]where \(\mathbf{x}_{(0)}\) is the central pixel \(\mathbf{x}_{0}\). Then, according to [12], the peer group with \(m+1\) elements, \(\mathcal{P}_{m}^{\mathbf{x}_{0}}\), corresponding to the pixel \(\mathbf{x}_{0}\) is defined as  
\[\mathcal{P}_{m}^{\mathbf{x}_{0}}=\{\mathbf{x}_{(0)},\mathbf{x}_{(1)},\ldots,\mathbf{x}_{(m)}\}.\]  
In [10], a fuzzy logic technique is introduced to compute the better number of pixels \(\widehat{m}\) in a peer group. As stated in [10], the fuzzy peer group associated with \(\mathbf{x}_{0}\) in a processing window \(W\) centered at \(\mathbf{x}_{0}\) is defined by the fuzzy set \(\mathcal{FP}_{\widehat{m}}^{\mathbf{x}_{0}}\) given over the set \(\{\mathbf{x}_{(0)},\mathbf{x}_{(1)},\ldots,\mathbf{x}_{(\widehat{m})}\}\), and determined by a function of membership \(FP_{\widehat{m}}^{\mathbf{x}_{0}}=\rho(\mathbf{x}_{0},\mathbf{x}_{(i)})\). Thus, the best number of elements, denoted by \(\widehat{m}\), for \(\mathcal{P}_{m}^{\mathbf{x}_{0}}\) is defined as the number \(m,\ 1\leq m\leq n^{2}-1\), which maximizes the certainty for the rule defined as follows:  
Fuzzy rule: Certainty for \(m\in\mathcal{N}_{W}\) to be the better number of pixels in \(\mathcal{P}_{m}^{\mathbf{x}_{0}}\)  
If \({}^{\prime\prime}\mathbf{x}_{(m)}\) is similar to \(\mathbf{x}_{0}\)\" and the accumulated similarity of \(\mathbf{x}_{(m)}\) is considered big, the \"certainty for \(m\) to represent the better cardinality for the peer group \(\mathcal{P}_{m}^{\mathbf{x}_{0}}\) is high.\"  
Let \(C_{FR}(m)\) denote the rule certainty for \(m\). To calculate the better number \(\widehat{m}\) of elements, \(C_{FR}(m)\) is calculated for every \(m\in\mathcal{N}_{W}\), and then the \(m\) maximizing \(C_{FR}(m)\) is chosen as \(\
## 3 Modeling of the System
### Performance Criteria  
In this study, specific work, overall efficiency, and cost of core equipment are chosen as the performance criteria.  
During operation, the systems can produce electric power (\(W_{\text{net}}\)) by the absorbed solar energy (\(Q_{\text{solar}}\)). Therefore, the overall efficiency of cycle can be calculated by \(W_{\text{net}}\)/\(Q_{\text{solar}}\). The specific work is calculated as \(W_{\text{net}}\)/\(m_{\text{c}}\) (\(m_{\text{c}}\) means the mass flow). These two parameters are used to characterize the thermodynamic properties of the system.  
The economic performance of the system is mainly calculated by the cost of core equipment mentioned in Section 3.3. In fact, the thermodynamic performance, such as specific work, can be used to calculate the return of the systems. However, due to the lack of other data, the calculation of the investment recovery of the systems is not involved in this study.  
In addition, for the calculation of the intercooling cycle and the convenience of comparison, we consider that the cooler between the no.1 compressor and no.2 compressor can reduce the inlet temperature of the no.2 compressor, which makes it equal with the inlet temperature of the no.1 compressor. The pressure ratios of the two compressors are also the same.  
For the calculation of the recompression cycle, we assume that the system has achieved the best shunt coefficient to ensure that the minimum end difference is exactly the set temperature difference.
## 4 Methodology
### InterceptApp  
The InterceptApp provides three services: it emulates the functionality of directional antennas for nodes derived from the AdHocHost component, it provides the means to measure the number of leaked packets by such nodes, and it helps implementing teams/groups (e.g., red, blue, green) while running a separate MANET for each team. To provide these services, InterceptApp uses a subset of the SA functionality from the SA module described above.  
The application subscribes to notifications from the MAC layer about incoming packets received by the wireless network interface. Each notification provides access to the full packet, including the MAC headers. The InterceptApp extracts the source MAC address and the destination MAC address from the packet headers and uses these to look up the identification of the sender and receiver in the node's SA view. If the sender does not belong to the same team as the node that received the packet (i.e., the node where InterceptApp is currently executing) and directional antenna emulation is turned off, then InterceptApp will record the incoming packet as a leaked packet.  
In the case when directional antenna emulation is enabled, the receiving node will run Algorithm 5 with sender and receiver identities as input for the first two parameters. The third parameter, the set \(\mathcal{A}\), will contain a single element, which is the identity of the node that received the packet. In essence, the algorithm answers the following question: would this node have received the packet if the sender from the other team instead of isotropic antennas used directional antennas with a radiation pattern following the tetrahedron model? If the answer is yes, the received packet would be leaked even if directional antennas were in use, and thus, the packet is marked as leaked.  
In either case (directional antenna emulation on or off), the application will force the TCP/IP stack to drop the packet if the sender belongs to a different team. Existing MANET protocol modules in INET/INETMANET are not aware of the concept of a team. Without InterceptApp, they will try and succeed to use nodes from other teams as relay nodes.  
If the sender belongs to the same team, normal packet processing will ensue.
# A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning
## 4 Performance Evaluation
### Scenario 1: Dataset 1
By looking at the figure, we can note that the Random Forest algorithm achieved the best value (0.99), which means the best performance from the other algorithms. Figure 11 displays the scalability of various machine-learning algorithms in the proposed system. The \(x\)-axis represents the training set size, while the \(y\)-axis represents training time (ms). The colored lines represent the algorithms of the proposed system. The model  
Figure 10: Receiver Operating Characteristic-Area Under Curve (ROC-AUC). Figure 9: Execution time of machine learning algorithms in the proposed system. that uses more data in the shortest amount of time is considered the best. Figure 11 shows that the training time for all of the algorithms increases as the training set size increases. However, the rate of increase varies for each algorithm. KNN has the slowest rate of increase, followed by naive Bayes, decision trees, logistic regression, SVM, and random forest. We can observe that the KNN is the most scalable algorithm, as it can be trained on large training sets without significantly increasing the training time. Naive Bayes is also relatively scalable, but not as much as KNN. Logistic regression, decision tree, SVM, and random forest are all less scalable than KNN and naive Bayes. Overall, all algorithms achieved acceptable results, and they are scalable because the difference in training time between them is in order of ms. In conclusion, the results of experiments conducted on two datasets. Although all the algorithms performed well after the pre-processing and feature selection, the Random Forest and Decision Tree achieved the best performance. The random forest achieved high values in terms of accuracy, precision, recall, F1-score, execution time, and ROC-AUC. In comparison, the Decision Tree algorithm achieved good values in terms of scalability and execution time. Generally, the random forest algorithm is considered the best solution among the rest of the algorithms for tackling the research problem.
## 5 Conclusions  
Our findings showed poor agreement between ADP and BIA (InBody 770) in %BF for males and between manual measurements in WC and calculated from BIA. Moreover, the statistical analysis demonstrated systematic bias and/or proportionate bias between BIA and ADP, indicating that these devices cannot be used interchangeably for determining fat content in males and WC and WHR in females. Given the documented differences between methods in clinics and medical facilities where patient body composition assessment is part of treatment and where testing efficiency is key, it appears that the use of the InBody 770 may provide an alternative to ADP with attention paid to possible bias. Still, WC should be measured manually, especially in females. It is important to consider the individual characteristics of the subjects when assessing nutritional status, taking into account gender differences and various types of body fat distribution.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15073480/s1](https://www.mdpi.com/article/10.3390/app15073480/s1)]([https://www.mdpi.com/article/10.3390/app15073480/s1](https://www.mdpi.com/article/10.3390/app15073480/s1)). Figure S1: Bland-Altman plots of BIA compared to ADP estimations of %BF (A) in total group, (B) in males (C) in females (ADP as reference technique); Figure S2: Bland-Altman plots of WC from BIA compared to manually measurement (A) in total group, (B) in males (C) in females (manual measurement as reference technique); Figure S3: Bland-Altman plots of WHR compared calculated from manual measurements and estimated by BIA (A) in total group, (B) in males (C) in females (manual measurement as reference technique).  
Conceptualization, R.S., M.G. ([PERSON]) and M.G. ([PERSON]); methodology, R.S., K.S. and M.G. ([PERSON]); formal analysis, R.S., M.G. ([PERSON]), N.S. and K.S.; investigation, R.S., M.G. ([PERSON]), D.K. and N.S.; data curation, R.S., M.G. ([PERSON]), D.K. and N.S.; writing--original draft preparation, R.S., M.G. ([PERSON]) and D.K.; writing--review and editing, K.S. and M.G. ([PERSON]); visualization, K.S., D.K. and N.S.; supervision, [PERSON] ([PERSON]) All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee for Scientific Research Involving Humans at the Institute of Human Nutrition Sciences, Warsaw University of Life Sciences, Poland, on 20 December 2021 (Resolution No. 52/2021).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available in the article.  
Thanks are expressed to the participants for their contributions to the study.  
The authors declare no conflict of interest.
# An Improved Jacobi-Based Detector for Massive MIMO Systems
## 2 System Model
### MMSE Detector  
The MMSE approach achieves a near-optimal detector in massive MIMO systems, where the resulting estimated symbol vector \(\mathbf{\tilde{x}}\) by utilizing MMSE detector is given by  
\[\mathbf{\tilde{x}}=\left(\mathbf{H}^{H}\mathbf{H}+\sigma^{2}\mathbf{I}_{N_{t} }\right)^{-1}\mathbf{H}^{H}\mathbf{y}. \tag{2}\]  
In Equation (2), \(\mathbf{A}=\mathbf{H}^{H}\mathbf{H}+\sigma^{2}\mathbf{I}_{N_{t}}\) is the symmetric positive definite matrix and \(\mathbf{b}=\mathbf{H}^{H}\mathbf{y}\) is regarded as the output of the matched filter, respectively. The computational complexity of direct matrix inversion \(\mathbf{A}^{-1}\) is on the order of \(O\left(N_{t}^{3}\right)\), which incurs a heavy computational burden in massive MIMO systems when the large-dimensional matrix inversion needs to be performed for large number of users.
# Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation
## 2 Building a Realistic UAV Simulator
### Modeling
#### 2.2.2 Quadrotor Differential Kinematics  
On the other hand, differential kinematics studies the motion relationships between the linear and angular velocities in a quadrotor concerning the translation and rotation ones. In this sense, the linear velocity in the quadrotor concerning the inertial system \(\{I\}\) can be obtained as the time derivative of \(\xi\) as:  
Figure 2: [PERSON]’s angle rotations sequence using the convention \(ZXY\), also called Pitch-Roll-Yaw (PRY). (**a**) Initial quadrotor frame \(\{B\}\). (**b**) New frame \(\{B^{\prime}\}\) after a pitch rotation of frame \(\{B\}\) with angle \(\theta\). (**c**) New frame \(\{B^{\prime\prime}\}\) after a roll rotation of frame \(\{B^{\prime}\}\) with angle \(\phi\). (**d**) New frame \(\{B^{\prime\prime\prime}\}\) after a yaw rotation of frame \(\{B^{\prime\prime}\}\) with angle \(\psi\).  
\[\dot{\xi}=[x,\dot{y},\dot{z}]^{T} \tag{5}\]  
As for the angular velocity of the quadrotor, this requires additional analysis. Angular velocity is the rate of change in rotations around the three main axes in frame \(\{l\}\) concerning time. This velocity is represented by the vector \(\omega=[p,q,r]^{T}\), whose components are observed in Figure 3, where frame \(\{I^{\prime}\}\) is the main frame \(\{l\}\) translated to the mass center of the quadrotor. The vector \(\omega\) is related to the rotation velocities \(\dot{\eta}=[\dot{\phi},\dot{\theta},\dot{\varphi}]^{T}\) by:  
\[\mathbf{\omega}=\dot{\theta}\mathbf{j}_{I}+\dot{\phi}R_{y}^{T}(\theta)\mathbf{i}_{I}+\dot{ \psi}R_{y}^{T}(\theta)\mathbf{R}_{x}^{T}(\phi)\mathbf{k}_{I} \tag{6}\]  
where \(\mathbf{i}_{I},\mathbf{j}_{I}\), and \(\mathbf{k}_{I}\), are the unit vectors of the main axes \(x\), \(y\), and \(z\) in \(\{l\}\), respectively.
## 3 Reduced Dynamic Orbit Determination of COSMIC-2
### Assessment of Orbit Accuracy  
In this study, the accuracy validation methods of the RD orbit of COSMIC-2 include internal validation and external validation. The former is carried out by carrier-phase residuals and overlapping orbit comparison, and the latter is performed by comparison with the reference orbit.
###### Abstract  
Wind profiling within operating wind farms is important for both wind resource assessment and wind power prediction. With increasing wind turbine size, it is getting difficult to obtain wind profiles covering the turbine-affecting area due to the limited height of wind towers. In this study, a stepwise quality control and optimizing process for deriving high-quality near-surface wind profiles within wind farms is proposed. The method is based on the radial wind speed obtained by the Doppler Wind Lidar velocity-azimuth display (VAD) technique. The method is used to obtain the whole wind profile from ground level to the height affected by wind turbines within a utility-scale onshore wind farm, in northern China. Compared with the traditional carrier-to-noise ratio (_CNR_) filter-based quality control method, the proposed data processing method can significantly improve the accuracy of the derived wind. For a 10 m wind speed, an increase in coefficient of determination (R\({}^{2}\)) from 0.826 to 0.932, and a decrease in mean absolute error (MAE) from 1.231% to 0.927% are obtained; while for 70 m wind speed, R\({}^{2}\) increased from 0.926 to 0.958, and MAE decreased from 1.023% to 0.771%. For wind direction, R\({}^{2}\) increased from 0.978 to 0.992 at 10 m, and increased from 0.983 to 0.995 at 70 m. The optimized method also presents advantages in improving the accuracy of derived wind under complex wind environments, e.g., inside a wind farm, and increasing the data availability during clear nights. The proposed method could be used to derive wind profiles from below the minimum range of a vertically operating scanning Doppler Lidar to a height affected by wind turbines. Combined with Doppler beam-swinging (DBS) scanning data, the method could be used to obtain the complete wind profile in the boundary layer. These wind profiles could be further used to predict wind power and evaluate the climate and environmental effects of wind farms.  
Doppler Lidar; onshore wind farm; velocity-azimuth display; quality control; wind profile; complex wind field 2024
## 4 Numerical Simulation Test  
After denoising and threshold segmentation of the CT images, this study successfully eliminated redundant interference. The pore structure reconstruction model of the rock was then developed based on the processed CT images using MIMICS software, followed by importing the model into Abaqus for numerical simulation experiments. It mimics enables high-resolution 3D reconstruction of rock microstructures from CT data, featuring advanced algorithms for quantifying pore networks, mineral distributions, and fractures. It directly interfaces with geomechanical (ABAQUS 2021) software for digital rock physics applications.
# PriSign, A Privacy-Preserving Single Sign-On System for Cloud Environments
## 5 Construction of PriSign
### New Results and Building Blocks
#### 5.2.1 Attribute-Based Credentials with Traceability
The user chooses \(r_{1},r_{2}\xleftarrow{R}\mathbb{Z}_{p}^{*}\), computes \(\sigma_{1}^{\prime}=\sigma_{1}^{r_{1}},\sigma_{2}^{\prime}=\sigma_{2}^{r_{1}}\), \(\mu=\sigma_{1}^{\textit{task}},\
u=\sigma_{1}^{\prime r_{2}},\kappa=\widetilde {X}\prod_{j\in[1,a]/\mathcal{D}}\widetilde{Y}_{j}^{m_{j}}\widetilde{g}^{r_{2}}\), and computes a ZKSoK \(\Pi_{2}=\mathsf{ZKSoK}\{(usk,r_{2},\{m_{j}\}_{j\in[1,a]/\mathcal{D}}):\mu= \sigma_{1}^{\prime task}\wedge\
u=\sigma_{1}^{\prime r_{2}}\wedge\kappa= \widetilde{X}\prod_{j\in[1,a]/\mathcal{D}}\widetilde{Y}_{j}^{m_{j}}\widetilde{ g}^{r_{2}})\left(\mathsf{CTX}\right)\). Finally, the user outputs a credential presentation \(pst=(\{m_{j}\}_{j\in\mathcal{D}},\sigma_{1}^{\prime},\sigma_{2}^{\prime},\mu, \
u,\kappa,\Pi_{2})\). Note that CTX is a random value used to prevent replay attacks. * ABCT.Verify\((ipk,pst,\mathsf{CTX})\to 0/1\): This algorithm is operated by a verifier. The verifier outputs \(1\) if the user's attributes \(\{m_{j}\}_{j\in\mathcal{D}}\) satisfy an attribute disclosure policy enforced by the verifier, \(e(\sigma_{1}^{\prime}\
u,\widetilde{g})=e(\sigma_{1}^{\prime},\kappa\prod_{j \in\mathcal{D}}\widetilde{Y}_{j}^{m_{j}})e(\mu,\widetilde{Y}_{0})\), and \(\Pi_{2}\) is verified, and outputs \(0\) otherwise. * ABCT.Trace\((ipk,pst,\mathsf{CTX},\mathcal{L})\to\textit{uid}/\bot\): This algorithm is operated by the issuer to trace the identity of a user who generates the presentation \(pst\). The issuer outputs \(uid\) if there exists a tuple \((uid,utk)\) in \(\mathcal{L}\) satisfying \(e(\mu,\widetilde{g})=e(\sigma_{1}^{\prime},utk)\); otherwise, it outputs \(\bot\). The details of the ZKSoK and the correctness analysis of our ABCT scheme are shown in Supplemental Material B. Naturally, our ABCT scheme satisfies the unlinkability and unforgeability the same as the anonymous credential scheme in [34]. In this paper, we use the ABCT scheme to issue a credential for the public key and attributes set of a user.
# Sensing of Transition Metals by Top-Down Carbon Dots
## 2 Materials and Methods  
**Synthesis of CQDs**: The synthesis of CQDs was conducted by adding 500 mg of carbon nanopowder (Sigma Aldrich-nanoparticles with a nominal average size \(<\) 100 nm) in a reaction flask to a mixture of H\({}_{2}\)SO\({}_{4}\) (Sigma Aldrich, 96%, Munich, Germany) and HNO\({}_{3}\) (Sigma Aldrich, 65%, Munich, Germany) in a 3:1 ratio (total volume 50 mL), under continuous stirring and maintaining the temperature at 140\({}^{\circ}\) for 48 h. At the end of the reaction, a fraction of about 10 mL was taken, and 10 mL of acetone was added in a still acidic environment in order to dope the surface with oxygen-rich groups, and obtain the TDA sample. The other fraction was not passivated to extract the TD sample. The two resulting solutions were finally buffered with a saturated sodium carbonate solution, and then purified by dialysis.  
**Steady-State Spectroscopy**: All absorption measurements were carried out under ambient conditions using the Star Line spectrophotometer ULS2048 CL-EVO (Avantes, Apeldoorn, The Netherlands), which has a wide spectral range (200-1200 nm), and measurements were conducted in 1 cm quartz cuvettes, diluting the CQDs solutions appropriately to obtain 0.3 optical density (OD). Fluorescence measurements were conducted with the FP6500 spectrofluorometer (JASCO Corporation, Tokyo, Japan), which features a 150 W Xenon source.  
**Time-resolved Spectroscopy**: Fluorescence decay kinetics for the samples examined, TD and TDA, were acquired under excitation with the tunable VIBRANT laser (Opotek, Carlsbad, CA, USA), which has a Nd:YAG crystal providing 5 ns pulses and a signal amplification on CCD (Princeton Instruments, Trenton, NJ, USA). The lifetimes were acquired in the absence and presence of metal ions using an excitation wavelength of 440 nm.  
**Raman Spectroscopy**: Raman measurements were obtained with a LabRam HR Evolution microRaman spectrometer (Horiba, Kyoto, Japan), using the excitation length in the UV of 325 nm in order to avoid competition with fluorescence. Measurements were acquired after an average of 10 spectra per sample at different positions, using an integration time of 5-10 s.  
**Fourier-Transform Infrared Spectroscopy (FT-IR)**: FT-IR measurements were carried out with a FT/IR 7600-JASCO spectrometer (JASCO Corporation, Tokyo, Japan) in an inert nitrogen atmosphere, depositing CQDs droplets on a silica support evaporated for 2 h, before conducting the acquisition of spectra in the range 1000-4000 cm\({}^{-1}\).  
**Sensing tests**: Sensing tests were conducted by preparing the TD and TDA solutions using the same dilutions, after which the metal ions (Fe\({}^{3+}\), Co\({}^{2+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), and Zn\({}^{2+}\), introduced as sulphates or chloride salts-Sigma Aldrich) were added to the concentration range 0-40 \(\upmu\)M by exciting the samples at a wavelength of 440 nm, and monitoring their PL. The contact time of CQDs in solution to analytes in solution is approximately four minutes, and two minutes more are required for the changes of the PL to be analysed.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 3 Resonance Conditions  
As the SPP and LSPP have different coupling mechanisms, the resonance condition for two different polaritons also needs to be explained in terms of the surface plasmon resonance and localized surface plasmon resonance.