esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Blockchain Technology for Enhancing Digital Twins' Security and Transparency
## 3 Proposed Approach
### Implementation
#### 3.1.1 Blockchain Creation  
The architectural requirements outlined in this research necessitate a blockchain protocol that supports robust smart-contract functionality while allowing for future system upgrades. To meet these criteria, we adopted the Ethereum protocol due to its well-established presence in the blockchain industry and its comprehensive support for smart-contract development.  
The blockchain infrastructure for this study was implemented using Hyperledger Besu [35], a highly regarded Ethereum client known for its enterprise-grade capabilities. Besu offers exceptional flexibility in configuring private networks, making it particularly  
Figure 3: The solution proposed to allow a balanced flow of requests from the system’s actors to the nodes of the blockchain.  
Figure 2: Architecture used for the implementation of the smart contracts’ subsystem. The system’s hashes are stored into a low-privilege contract while managing functionalities and access control capabilities are accessed through a manager contract.  
suitable for applications requiring secure, permissioned environments. Additionally, the rich ecosystem provided by the Hyperledger suite ensures seamless integration with analytical tools and complements the experimental framework.  
Hyperledger Besu was chosen for its compatibility with enterprise-level blockchain requirements, particularly in the context of digital twin systems. Its robust support for smart contracts and permissioned configurations aligns perfectly with the security, scalability, and reliability demands of such systems. Moreover, Besu's interoperability with the broader Ethereum ecosystem ensures long-term adaptability, enabling smooth integration with existing blockchain tools and frameworks.  
Beyond addressing performance and security objectives, Hyperledger Besu enhanced the reproducibility of the proposed blockchain architecture through a docker-based solution. This capability facilitated straightforward deployment across diverse business-oriented blockchain applications, ensuring the scalability, versatility, and practicality of the framework in real-world scenarios.  
It is important to note that while Hyperledger Besu was used in this implementation for its ease of use and rich feature set, the proposed system is not dependent on this specific software. Any blockchain platform that supports robust smart-contract functionality and permissioned configurations can be utilized, as the general design of the approach is platform-agnostic. In order to communicate with the blockchain, each actor involved in the system requires a pre-authenticated address, already available at the moment of network creation or added afterward. Once the communication is established without rejection from the network, the encapsulated stub proceeds to validate its communications with the digital twin by sending a signed message through the blockchain. Each validated transaction is then stored in the isolated blockchain and recorded in the shared ledger.
# Using IoT for Cistern and Water Tank Level Monitoring
## 3 Monitoring of Water Architecture
### Components  
Below are photographs of some of the main devices used in the proposed system.  
Ultrasonic sensor: This sensor emits ultrasonic waves and changes the state of this reception. The time it takes between going and returning for these waves is obtained for a distance between the sensor and some object, in our case, the water surface. We use two sensors to measure the water level in both the cistern and the tank. See Figure 3a.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Items and Brief Details** \\ \hline Two water level sensors (Ultrasonic Sensor) [SN-SROT-2.0] \\ One water flow sensor (Flow meter T-SF0 3/4” Hall Effect) \\ One water quality sensor (TDS Meter T-10) \\ One submersble water pump 70-120L/H \\ One Raspberry P14 Model-8 GB RM \\ One WebMos D1 mini ESP826 WiFi \\ One Relay SV DC SRD-SVDC-SL-C \\ One Node/KD 2 ESP8266 WiFi \\ One case with fan for Raspberry P1 4 \\ Two methods \\ One MicroSD XDHC 32 GB class 10 \\ One Microsoft USB adapter \\ One \(p\) battery \\ One WiFi access point \\ One laptop \\ MM and MF jumpers \\ Modern software \\ Node-RED \\ Arduino IDE \\ Node S \\ Mosquito MQTT \\ Database: Influudb \\ \hline \hline \end{tabular}
\end{table}
Table 2: Materials and equipment used in the proposal. Some elements are software, and others are hardware.  
Figure 2: Monitoring of Water Architecture. (**a**) Half-full cistern level and half-full water tank level, (**b**) half-full cistern level and almost empty water tank level, and (**c**) full cistern level and full water tank level.  
Figure 4: (**a**) Water quality sensor (TDS Meter V1.0). (**b**) Submersible water pump 70-120L/H.  
Figure 3: (**a**) Water level sensors (Ultrasonic Sensor JSN-SR04T-2.0). (**b**) Water flow sensor (Flow meter YF-SF01 3/4” Hall Effect).
## 2 Methods
### SAR Intensity Correlation  
Contrary to the interferometric coherence, the intensity correlation is not based on the phase of the SAR signal, but on its intensity \(I\), which is the square of the SAR amplitude value \(A\):  
\[I=A^{2}\,=Re^{2}\,+Im^{2} \tag{8}\]  
with \(Re\) and \(Im\) representing the real and imaginary part of the complex SAR value, respectively.  
The SAR intensity, _i.e._, the SAR backscattering, is influenced by the imaging geometry of the SAR sensor (e.g., orbit direction (ascending/descending), incidence angle and polarization) as well as by the orientation, size, shape, construction material (especially its dielectric properties) of the object observing on the ground.  
The intensity correlation \(\rho\), ranging from 0 to 1 is defined by [76, 78]:  
\[\rho=\frac{E\langle I_{1}I_{2}\rangle}{\sqrt{E\langle I_{1}^{2}\rangle E \langle I_{2}^{2}\rangle}} \tag{9}\]  
where \(I_{1}\) and \(I_{2}\) are the corresponding pixel values for the intensity of the two acquisition dates t\({}_{1}\) and t\({}_{2}\), respectively.
# Interplay Between Wear and Thermal Expansion in 6082 Aluminum: A Simulation and Experimental Study
## 3 Results and Discussion
### Effective Wear Rate  
Figure 7 illustrates the effective wear rate under varying operating parameters. Generally, the effective wear rate increases with higher load and rotational speed. Simulations with thermal effects show greater fluctuations in the wear rate due to dynamic contact changes. In contrast, simulations without thermal effects show a more stable wear rate due to consistent contact behavior.  
At lower loads and speeds, the wear rate difference between scenarios with and without thermal effects is minimal. However, as load and speed increase, the difference becomes more pronounced, especially at 15 N and 300 RPM. With thermal effects, the wear rate decreases and becomes lower than in scenarios without thermal considerations. This change is explained by differences in contact pressure and contact area. Under identical conditions, the differences between scenarios with and without thermal expansion lie in contact pressure and contact area. As previously analyzed, thermal expansion reduces contact area (decelerating wear) but increases average contact pressure (accelerating wear). These opposing trends create a competition mechanism influencing wear rate. Figure 7 shows that reduced contact area dominates this competition mechanism.  
The authors of [28; 29] also observed a decrease in wear rate with thermal expansion, but it cannot be concluded that thermal expansion universally decelerates wear. However, the authors of [30] reported an increase in wear rate with thermal expansion. Additionally, the experiments show that wear rate initially increases rapidly, then decreases as temperature rises further [31]. Thus, the relationship between thermal expansion and wear rate may depend on material properties and experimental parameters.  
Figure 7: Comparison of the effective wear rate with and without thermal effects at (**a**) 5 N and 100 RPM, (**b**) 10 N and 200 RPM, and (**c**) 15N and 300 RPM (the results are from the FEM simulation).
## 6 The Real Data Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Set** & \begin{tabular}{c} **Number of** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Variables** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Training** \\ **Sample** \\ \end{tabular} &
\begin{tabular}{c} **Number of Test** \\ **Sample** \\ \end{tabular} \\ \hline Boston House Prices & 506 & 13 & 407 & 99 \\ Real Estate Valuation & 414 & 5 & 332 & 82 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Datasets summary. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Algorithm** & **WMSE** & **WRMSE** & **WMAE** & **Selected Trees** \\ \hline \multirow{2}{*}{Ntree = 300} & Random Forest & 107.8263312 & 10.38394584 & 5.227023752 & 300 \\  & ECAPRAF–Lasso & 63.90547096 & 7.994089752 & 5.353364544 & 74 \\  & ECAPRAF–EN & **60.54003401** & **7.780747651** & **5.318630768** & **105** \\  & ECAPRAF–GL & 66.21407167 & 8.137202939 & 5.412917123 & 110 \\ \hline \multirow{2}{*}{Ntree = 500} & Random Forest & 108.0136379 & 10.39296098 & 5.251413432 & 500 \\  & ECAPRAF–Lasso & 32.94807873 & 5.740041701 & 4.32224372 & 67 \\  & ECAPRAF–EN & **30.33592275** & **5.507805621** & **4.207588771** & **111** \\  & ECAPRAF–GL & 31.62320614 & 5.623451444 & 4.211087924 & 135 \\ \hline \multirow{2}{*}{Ntree = 800} & Random Forest & 107.9326224 & 10.38906263 & 5.248644659 & 800 \\  & ECAPRAF–Lasso & 47.00931756 & 6.856334119 & 4.895265056 & 77 \\  & ECAPRAF–EN & **45.70566097** & **6.7605962** & **4.874371821** & **137** \\  & ECAPRAF–GL & 47.01065198 & 6.856431432 & 4.917456438 & 140 \\ \hline \multirow{2}{*}{Ntree = 1000} & Random Forest & 107.5598427 & 10.37110614 & 5.241761999 & 1000 \\  & ECAPRAF–Lasso & 45.89325716 & 6.774456226 & 4.876710703 & 75 \\  & ECAPRAF–EN & **44.71192516** & **6.686697627** & **4.800701571** & **141** \\  & ECAPRAF–GL & 47.84737551 & 6.917179737 & 4.954152998 & 155 \\ \hline \end{tabular}
\end{table} 
Table 5: The results of the proposed method for the real-estate-valuation dataset. Figure 7: 3D scatter plot shows the number of clusters and the relationship between variables with different colors on three axes. (**a**) The dataset consists of 13 features from which 3 features are the average number of rooms (rm), the pupil–teacher ratio by town (ptratio), and weighted distances of employment centers (dis). (**b**) The dataset consists of 5 features from which 3 features are longitude, latitude, and house age.
## 2 Background and Theory
### Feature Extraction Techniques
#### 2.2.6 Detrended Fluctuation Analysis  
Detrended fluctuation analysis (DFA) [48] is a widely used technique for quantifying the self-similarity and long-range temporal correlations of non-stationary signals. DFA provides a valuable measure of signal complexity by evaluating the fractal properties of EEG time series. The method effectively distinguishes between different cognitive and motor tasks by capturing scale-invariant patterns in brain activity. The DFA process begins by constructing the integrated profile \(X(i)\) of the mean-centered EEG signal \(x(i)\), as in Equation (18):  
\[X(i)=\sum_{k=1}^{j}\left(x(k)-\langle x\rangle\right) \tag{18}\]  
In this context, \(\langle x\rangle\) is the mean of the signal. The integrated profile is then divided into non-overlapping segments of length \(s\). In each segment \(v\), a linear least-squares polynomial \(P_{\texttt{D}}(k)\) is fitted to remove local trends, and the root mean square (RMS) fluctuation is computed, as in Equation (19):  
\[F_{\texttt{D}}^{2}(s)=\frac{1}{s}\sum_{k=1}^{s}[X((v-1)s+k)-P_{\texttt{D}}(k )]^{2} \tag{19}\]  
The overall fluctuation function \(F(s)\) is obtained by averaging all the segments, as in Equation (20):  
\[F(s)=\left(\frac{1}{N_{s}}\sum_{v=1}^{N_{s}}F_{\texttt{D}}^{2}(s)\right)^{1/2} \tag{20}\]  
\(N_{s}\) is the number of segments. The DFA exponent \(\alpha\), representing the fractal scaling behavior of the signal, is estimated as the slope of the log-log plot of \(F(s)\) vs. \(s\), as in Equation (21):  
\[\alpha=\frac{\Delta\text{log}\,F(s)}{\Delta\text{log}\,s} \tag{21}\]The DFA exponent \(\alpha\) provides insight into the temporal correlation structure of the EEG signal, with varying values indicating different types of dynamics.
## 4 Simulation Experiments
### Friction Torque Simulation Experiment
Figure 7c illustrates the error characteristics of Joint 3, with deviations consistently confined within \(\pm\)0.015 N-m. Notable spikes occur at t \(\approx\) 2.1 s and 8.7 s, aligned with nonlinear transitions in friction dynamics such as the Stribeck effect. The BLS framework accurately captures these nonlinearities while preserving overall convergence, reflecting strong sensitivity to hysteresis and transient dynamics. Figure 7d illustrates the error of Joint 4, featuring higher-frequency fluctuations with the maximum amplitude reaching \(\pm\)0.12 N-m. These oscillations likely arise from harmonic reducer meshing dynamics. Despite the challenging conditions, the PSO-tuned regularization parameter (\(\lambda\) = 1 \(\times\) 10\({}^{-4}\)) ensures stable output and suppresses low-torque noise amplification, underscoring the model's robustness. Figure 7e illustrates the prediction error for Joint 5. The error signal is dense and irregular, with peaks near \(\pm\)0.013 N-m, primarily concentrated in frequency bands associated with mechanical meshing. By applying a widened BLS structure with 20 enhancement nodes, the network successfully attenuates transient disturbances, validating its effectiveness in handling nonstationary structural noise. Figure 7f illustrates the error profile for Joint 6, showing a composite signal of white noise with low-amplitude periodic components. The error remains stably bounded within \(\pm\)0.015 N-m without significant drift. These features reflect the BLS model's strong filtering capability under compound disturbances such as commutation noise and joint flexibility. In conclusion, the proposed BLS-based friction modeling framework achieves consistently high torque prediction accuracy across all six joints. It demonstrates strong capability in handling nonlinear friction phenomena, flexible modal coupling, and dynamic noise interference. The model's low error margins, robust convergence, and adaptability to multi-source disturbances highlight its potential for deployment in high-precision motion control and model-based predictive systems in advanced robotic applications.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 4 Key Capabilities for Resilience in IoMT
### Distributed Functionality  
As the number of nodes increases in the IoMT networks, the chance of a fault occurring also increases. A single point of failure in a large IoMT network can halt the entire data transmission. Hence, the distributed functionality brings fault tolerance into the networks. This can be realized by dividing the computational tasks into different nodes in the network. Microservices architecture can provide a distributed functionality in the IoMT networks [31]. By doing this, it can avoid the single point of failure in the IoMT networks, as each subsection handles different functions of the network.
# Median Filter Aided CNN Based Image Denoising:
## 4 Experimental Results
### Results  
After thorough testing, as illustrated in Tables 1 and 2, we note that the ensemble model outperforms all of the other model combinations and also some non-deep learning based models for the BSD500 dataset. For the Set12 dataset, we see that it provides extremely competitive results and outperforms other model combinations in most of the cases. When compared to non-deep learning based models, it completely outperforms them. Additionally, in Table 2, for noise level 15, out of the three non-deep learning models, the highest PSNR value is 23.54 dB, whereas the lowest PSNR value in the case of CNN based methods is 32.36 dB, which is 8.82 dB more than the best result of the non-deep learning models. Additionally, in the case of noise levels of 25 and 50, the CNN based methods perform better than the non-deep learning based methods. For the Set12 dataset, the trend is similar, as shown in Table 2. Thus, it is clear that the CNN based denoising models perform far better than the non deep learning based methods for the purpose in concern. Figure 2 shows the denoised images, as produced by DrCNN, IRCNN, ADNet, and our proposed ensemble model, for a noisy image of noise level 50. Various noisy versions of a single image with noise levels of 5, 10, 15, 25, and 50, along with their denoised versions,are shown in Figure 3. Additionally, we have shown the denoised results, for a noisy image of noise level 25, produced by the non-deep learning methods and the proposed ensemble method in Figure 4.  
Figure 3: _Cont_.  
Figure 2: Denoised images by DrCNN, IRCNN, ADNet, and Proposed ensemble methods.  
Figure 3: Noisy and denoised versions of the original image.
## 2 Mathematical Formulation of OPF Problem
### Power Losses and Voltage Deviation  
The most significant factor besides the reduction of total generation cost is the reduction of power loss in the network. Due to inherent line resistance, power losses are inevitable in transmission systems. The total network loss can be calculated as:  
\[P_{loss}=\sum_{i=1}^{nl}\sum_{j\
eq 1}^{nl}G_{ij}V_{i}^{2}+V_{j}^{2}-2V_{i}V_ {j}\cos(\delta_{ij}) \tag{24}\]  
The difference between the voltage angles of two buses is represented by \(\delta_{ij}=\delta_{i}-\delta_{j}\); \(G_{ij}\) represents transfer conductance and \(nl\) shows the total number of lines in the network.  
The voltage profile of the system has been expressed in terms of voltage deviation (_VD_), which is a gauge of the voltage quality within the system. Voltage deviation is computed by the following expression:  
\[VD=\sum_{p=1}^{nl}\left|1-V_{lp}\right| \tag{25}\]
## 4 Conclusions  
The results of the present study showed that the examined experimental set up, compromising plug flow reactors followed by sand filtration, could achieve Cr(VI) removal rates of more than 90%. These results are satisfying because the possible future limit for Cr(VI) in drinking water at 25 or 10 \(\upmu\)g/L was achieved, even under pH and dissolved oxygen conditions that favor the competition for Fe(II) oxidation between oxygen and Cr(VI). The removal of hexavalent chromium from initial concentrations of 50 \(\upmu\)g/L was efficient with a coagulant dose of 1 mg/L and a linear velocity of 8 m/h at pH 7.3. The further advantage of this study was that the treatment could result in the production of about 1 m\({}^{3}\)/day drinking water, which could satisfy the needs of a family in rural areas who are exposed to Cr(VI)-contaminated groundwater and do not have access to community piped drinking water. Increasing the linear velocity, thus producing more drinking water per day, showed a slight decrease in chromium removal rate, but even at a rate of 15 m/h, the residual concentration of Cr(VI) was lower than 10 \(\upmu\)g/L from initial concentration of 50 \(\upmu\)g/L. In experiments with increasing concentrations of Cr(VI), thus applying under-stoichiometric Fe/Cr molar ratio values, the mass of Cr(VI) reduced per mass of Fe(II) dose increased by decreasing the Fe/Cr molar ratio, although the percentage Cr(V) removal rate decreased. This might indicate, that when treating waters with relatively high concentrations of Cr(VI), multiple dosages of Fe(II) might be more efficient than dosing the entire quantity at once, thus cutting the amount of reducing reagent dosed and correspondingly the amount of sludge produced.  
[PERSON] and [PERSON] conceived, designed and supervised the experiments and the revision of the draft paper. [PERSON] conducted the experiments and wrote the draft paper. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
## 3 Instrument Characteristics and Tests
### Time bandwidth product
#### 3.2.1 Antenna  
The return loss and the isolation between the horizontal and the vertical port of the antenna are important parameters. Both are measured with an Agilent E4408 spectrum analyzer attached to the antenna pointed towards the sky. Furthermore, knowing the directivity of the horn antenna is essential to know, as it determines the extent of the observed footprint. Measurements of these antenna characteristics are presented hereafter.
# Nonparaxial Propagation Properties of Specially Correlated Radially Polarized Beams in Free Space
## 4 Conclusions  
On the basis of the generalized vectorial Rayleigh-Sommerfeld diffraction integral formulas, analytical expressions for the 3 \(\times\) 3 cross-spectral density matrix of an SCRP beam that propagates nonparaxially in free space has been derived. Furthermore, with the help of numerical calculations, the intensity, polarization, and SDOC of an SCRP beam have been illustrated and compared with those of a PCRP beam. It was found that the intensity distribution, degree of polarization, and SDOC of nonparaxial SCPR and PCRP beams determined by their beam waist width and spatial correlation function are substantially different. Therefore, by modulating the initial beam waist width and spatial correlation function, one can modulate the statistical properties of a nonparaxial partially coherent vector field. When we go a step further, the nonparaxial statistical properties can be used to manipulate the optical forces induced by the interaction of the optical fields and the nanoparticles [30; 31; 45]. Therefore, our findings can have potential use in nanoparticle trapping in nonparaxial systems.  
L.G. (Data curation, Writing--original draft); L.C. (Formal analysis, writing--original draft preparation); R.L. (Supervision, Writing-review and editing); M.Z. (Validation, Writing--review and editing); Y.D. (software, Validation); Y.Chen (Formal analysis, Writing--review and editing); [PERSON] (Supervision, Project administration, Writing--review and editing).  
This research was funded by the National Natural Science Fund for Distinguished Young Scholar [11525418], the National Natural Science Foundation of China [91750201], Characteristic Innovation Project (Natural Science) of the Education Department of Guangdong Province [2017 KTSCX114].  
The authors declare no conflict of interest.
## 5 Discussion
### Limitations and Future Directions  
Although our proposed MS-RRBR method has achieved outstanding results, there are still certain limitations that merit further examination. Future research can be intensified in the following aspects to improve both the robustness and applicability of our approach.