esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion  
### Simulation Environment  
The proposed method was tested by simulation to prove its effectiveness. The microgrid studied in [25], [42; 43; 44] was modeled with a slight modification and is shown in Figure 4. The power rating of all DGs (DG1 and DG3 are grid-forming units and DG2 and DG4 are grid-feeding units) in the microgrid is 2 MVA and the line parameters are shown in Figure 4. DG1 and DG3 are ESSs and both have a capacity of 4 MWh. Since it takes too long to see the change in SOC in the simulation, the capacity of ESS is scaled down to 1/3600 of 4 MWh. This means that the change in SOC in 1 h can be seen in 1 second (1/3600 h) of simulation time. The _P-f_ droop coefficient for both DG1 and DG3 is 0.01, and the _P-schad_ for all units is 0.4 MW.  
Figure 3: Proposed frequency deviation signal generator.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Proportional** & **Integral Gain (pu)** & **Time Constant** & **Dead Band** & **\(K_{int}\) (Hz-s)** & **Hysteresis Loop** \\
**Gain (pu)** & **(pu)** & **of LPF (s)** & **Range (Hz)** & **ON/OFF Value (Hz)** \\ \hline
0.00005 & 0.0006 & 0.03 & \(\pm\) 0.1 & 50 & 0.1/0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in Figures 2 and 3.
## 2 Materials and Methods  
### Datasets  
To verify the accuracy of the proposed model in water body recognition and its generalization ability in different scenarios, this study constructed an experimental system using multi-source heterogeneous data, which consists of two parts: a global satellite image dataset and a dataset collected autonomously by unmanned aerial vehicles (UAVs). All satellite and UAV images used in this study are based on optical remote sensing and contain three spectral bands: red, green, and blue (RGB). The RGB configuration was selected due to its wide availability across both satellite and UAV platforms, its high spatial resolution, and its compatibility with mainstream deep learning architectures.
## 4 Result  
### Result of Converting Three-Phase Voltage with Computer Simulation  
A computer simulation was conducted using PSIM, based on the topology described in Section 3.2. As shown in Figure 11, the input voltage was observed to be 220 V, and the output voltage comprised three waveforms, each with a phase voltage of 220 V.  
Computer simulations were performed under four different conditions: (A) normal load conditions, (B) DER connection, (C) nonlinear load connection, and (D) short-circuit fault occurrence.  
(A) Normal Load Conditions  
Under normal load conditions, as shown in Figure 11, both the input and output voltages remained within their reference ranges, and the THD was maintained below 5%. In addition, it was confirmed that the voltage, current, and THD of the line branched separately from the secondary side of the transformer--regardless of the STPC--remained within acceptable limits.  
(B) DER Connection  
In the second scenario, the DER was connected to the branch line. Since the STPC operates based on output current sensing, it does not support bidirectional power flow. However, a DER can still be connected to the branch line. The simulation observed whether the DER's power injection affected the input voltage or current of the STPC. As a result,  
Figure 11: Result of computer simulation.  
the output voltage of the STPC remained stable, while the magnitude of the branch line current was observed to change.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 8 Experimental Evaluation
### Experimental Methodology and Environment
#### 8.1.4 Limitations and Assumptions  
While our local testing environment provided valuable insights, it is important to acknowledge its limitations as follows:  
* Network idealization: The local environment does not capture the full complexity of the Ethereum network, including aspects such as network latency, congestion, and the impact of other users' transactions;
* Gas price stability: In our local environment, gas prices remained constant, whereas they fluctuate in the real Ethereum network based on demand;
* Block time consistency: Ganache produces blocks instantly when transactions are available, which differs from the Ethereum mainnet's average block time of around 13 s;
* Scalability simulation: While we attempted to stress-test the system, the local environment cannot fully replicate the challenges of operating at a global scale;
* Security considerations: The controlled environment does not account for potential security threats and attacks that might occur in a public network.  
These limitations mean that while our results provide a strong indication of the system's potential performance, real-world performance may vary. Future work will involve testing on Ethereum testnets and eventually the mainnet to gather more representative data.  
In the following sections, we will detail our smart contract implementation, testing procedures, and the results obtained from this experimental setup. Despite the acknowledged limitations, this methodology provides a solid foundation for evaluating the core functionality and performance characteristics of our NFT-based global digital registry system.
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
## 4 The Framework and Construction for Privacy-Preserving Image Classifier
### Core Idea of the _IPPNN_  
The main challenge in privacy-preserving machine learning is to compute the non-linear functions without at the expense of user privacy, which is hard to support using the existing cryptographic technologies, including homomorphic encryption, Boolean circuit, and secret sharing. Hence, prior work proposes to replace the non-linear functions by approximation polynomials or piece-wise linear functions. These substitutions can be computed using secure computation techniques efficiently.  
Our _IPPNN_ removes the need for the use of polynomial approximation by using FEIP scheme, and removes the multi-round communications between the users and servers by using mask matrix. Consequently, the private training protocol does not alter any of the computation of the original ANN training algorithm and, therefore, result in the same output, i.e., there is no accuracy loss in the secure version. In addition, our scheme ensures that any user can go offline after uploading the encrypted local data to the server.  
Figure 2: System model.  
In the _IPNN_ scheme, a user needs to encrypt his training data in two ways: _(i)_ by the random matrix; and _(ii)_ by the public key generated by the \(\mathcal{TPA}\).  
Specifically, in the first encryption way, the encrypted data are deliberately defined a tuple \((Ax,A^{-1})\), where \(A\) is a random matrix chosen by the user. The pair \((Ax,A^{-1})\) is split into two parts and sent to \(\mathcal{S}_{\mathcal{AU}}\) and \(\mathcal{S}_{\mathcal{NNN}}\), respectively. By exploiting the mask matrix \(A\) the server \(\mathcal{S}_{\mathcal{AU}}\) will obtain the inner-product \(\mathbf{w}\cdot\mathbf{x}\). Combining the linear transformation \(\mathbf{w}\cdot\mathbf{x}\) and non-linear activation function \(f(\cdot)\) together, the output of neuron \(f(\mathbf{w}\cdot\mathbf{x})\) is sent to \(\mathcal{S}_{\mathcal{NNN}}\) by \(\mathcal{S}_{\mathcal{AU}}\). The operations for obtaining the gradient \(\partial E_{S}/\partial w^{(o)}_{i,j}\) can be executed by the server \(\mathcal{S}_{\mathcal{NNN}}\) independently because the remainder computations only depend on model weights \(\mathbf{w}\) and the provided label.  
In the second encryption way, we observe that for obtaining the gradient \(\partial E_{S}/\partial w^{(h)}_{j,k}\) the feature dimension secure aggregation are required. From the computation process of the gradient \(\partial E_{S}/\partial w^{(o)}_{i,j}\), we can obtain the coefficient \(u^{(l)}_{k}\) of the attribute \(x^{(l)}_{k}\) in the update rule (4), where \(u^{(l)}_{k}\) is defined by (7). In order to compute the gradient \(\partial E_{S}/\partial w^{(h)}_{j,k}\), the user encrypt his samples using the FEIP cryptosystem with the public key. With the functional-derived key depending on the vector consisting of the coefficients \(u^{(l)}_{k}\), the server \(\mathcal{S}_{\mathcal{NN}}\) is able to decrypt the ciphertext and acquire the gradient \(\partial E_{S}/\partial w^{(h)}_{j,k}\).
## 4 PM\({}_{2.5}\) Estimation  
The overall methodology used for the PM\({}_{2.5}\) estimations over Malaysia is illustrated in Figure 2. The model inputs consist of hourly AOD, SO\({}_{2}\), NO\({}_{2}\), CO, O\({}_{3}\), WS, WD, TEMP and RH values. Hourly AOD data from Himawari-8 were extracted at 5 \(\times\) 5 km over the air quality monitoring stations and temporally collocated with ground measurements. Wind direction was used in the model because wind blowing from a highly polluted area can influence air quality in other downwind places. Wind speed enables to accelerate pollutants travelling from other places but also contributes to the dilution processes at local level [108]. On the other hand, temperature can trigger biogenic emissions, photochemical reactions and secondary aerosol formation over the region [109] and also control the temperature inversions, which can trap the pollutants near the surface [45; 108; 110]. Finally, RH may affect the hygroscopic growth of particles and enhance the aerosol scattering [111; 112; 113].  
In this study, we utilized and evaluated two ML models, namely, Support Vector Regression (SVR) and Random Forest (RF) to estimate PM\({}_{2.5}\) concentrations at the 65 air quality monitoring stations across Malaysia. The input variables to the SVR and RF models are selected based on our previous study [18; 31] and other literature [114; 115]. In total, we developed 7 different models to represent the spatial and seasonal effects on PM\({}_{2.5}\) distributions. Model 1 considers all data from the 65 stations, but o  
Figure 2: Flowchart of the overall dataset and methodology used for the estimations of PM\({}_{2.5}\) concentrations.  
2 and 3, only represent urban/industrial and suburban sites, respectively, while models 4 to 7 represent different seasons (wet, dry and two inter monsoon). The models are described in the subsequent sections.
## 4 Discussion  
For the APPJ under investigation, no difference in emitted species and plasma parameters were found for different gas flows of 35 L min\({}^{-1}\) and 45 L min\({}^{-1}\). Similar results are in the literature, where properties of APPJ are independent of gas flow rates over a wide range [10; 17].  
As depicted in Figure 5, excitation temperatures are about 1.5 times higher than rotational temperatures; hence, it can be assumed that the APPJ is in a non-equilibrium state. Both the rotational and excitation temperatures of the nitrogen species were found to be high at the nozzle exit and steady with distance, while their intensity and concentration decreased. Although the measurement series was consistent, some uncertainties needed to be addressed. It is known, that N\({}_{2}^{+}\)(B) molecules are not only populated by direct excitation from the ground state but are also by highly vibrationally excited N\({}_{2}\)(X) molecules through resonant energy transfer [30]. These \"hot\" molecules cannot be detected by simple Boltzmann plots; therefore, results should be confirmed by rotational temperature determination of further bands or systems or by the application of other methods.  
For most plasma jets, gas temperatures were reported to decrease with increasing axial distance. This behaviour was due to convective cooling and the entrainment of ambient air as shown by [PERSON] et al., who studied the effect of ambient air on the gas temperature of a nitrogen APPJ using a glass tube for shielding and thermocouples for measuring [11]. [PERSON] et al. reported on a nitrogen APPJ, where excitation and rotational temperatures measured by OES were not dependent on distance [10]. For long axial distances the approximation of the rotational temperature equalling the gas temperature seemed not to be valid. While for long distances, the gas temperature was mainly governed by cold neutral temperatures from the ambient air, which still had a fair amount of reactive plasma species, such as N\({}_{2}^{+}\) and atomic nitrogen with high rotational and excitation temperatures.  
Former measurements on APPJs for cold plasma spray using thermocouples showed low gas temperatures of \(\leq\)400 K at a working distance of 20 mm from the nozzle exit [5; 7]. While differences between gas and rotational temperatures were one explanation, it should be noted that in our experiment no particles for deposition were added to the jet, and studies showed a great influence of substrates on gas-phase chemistry [13]. As the validity range for electron-density determination was exceeded, it can only be stated that the electron density was lower than the limit of \(5\times 10^{15}\) cm\({}^{-1}\) although this is in agreement with the literature for atmospheric-pressure non-thermal nitrogen jets [31].
## 2 Materials and Methods
### Retinex Algorithm  
Based on Retinex, the original image \(I\) is expressed as:  
\[I(x)=R(x)\cdot L(x) \tag{3}\]  
where \(I(x)\) is the original image, \(R(x)\) is the reflectance component, and \(L(x)\) is the illumination component.  
The purpose of image enhancement based on Retinex is to estimate \(L(x)\) from \(I(x)\) and thereby decompose \(R(x)\). In the meantime, the effect of uneven brightness is eliminated, and the visual effect of the image improves.  
Conventional Retinex has halo effects in areas with large brightness differences. In this paper, [PERSON]'s Retinex [29] is employed, as it is suitable for image enhancement for images with shallow shading or uneven illumination. The reflectance component for the center pixel in the window is expressed as:  
\[R_{c}=R_{0}+\sum_{i=1}^{m}\frac{I_{c}-I_{m-i+1}(x)}{2^{i}} \tag{4}\]  
where \(R_{c}\) is the final reflectance component estimation of the center pixel, and \(R_{0}\) is the largest pixel value. \(I_{c}\) and \(I_{m}\) are the logarithmic values of the center point and selected point, respectively. \(i\) represents the indexes of different points. \(m\) is the total number of selected pixels.
# Synchronization of Analog-Discrete Chaotic Systems for Wireless Sensor Network Design
## 3 Study on Synchronization Noise Immunity
### Discrete-Analog Synchronization  
The second part of this study evaluates the discrete-analog synchronization noise immunity. Figure 14 outlines the experiment in block diagrams. The drive chaos oscillator is the discrete model, while the response oscillator is the LTspice model in one case and the hardware prototype in the other case. Like in the first part, the band-limited white Gaussian noise (B.L. AWGN) is added to the chaotic signals of the drive chaos oscillator. The cut-off frequencies are taken from Table 1.  
Figure 14 demonstrates the implementation of the block diagrams in Figure 14. Figure 15a demonstrates the LTspice model synchronization with discrete model noise immunity. In MATLAB the discrete signals are resampled and scaled; the noise signals of required SNR are also added before exporting to LTspice. For estimating the correlation, all signals are scaled again to the discrete model parameters.  
Figure 14: Chaos oscillator’s LTspice model (**a**), and hardware prototype (**b**) synchronization with discrete model.  
Figure 15: Implementation of the LTspice model (**a**) and hardware prototype (**b**) synchronization with the discrete model noise immunity.  
Figure 15b demonstrates the hardware prototype synchronization with the discrete model. In this case, the signal generator channel of the ADP3450 is used to pass the noisy synchronization signal of the discrete model to the hardware chaos oscillator. The oscilloscope channels are used to record back the chaotic signals of the chaos oscillator. Similarly, the recorded hardware chaotic signals are scaled before estimating correlation with the noised signals of the discrete model.
## 2 Principle and Modeling
### Bayesian Optimization Algorithm  
The Bayesian optimization algorithm is an intelligent optimization algorithm that can effectively reflect the relationship between variables [21]. The principle of Bayesian optimization is to use [PERSON] theorem to estimate the posterior distribution of the objective function and then select the next combination of hyperparameters to be sampled according to the distribution. This method makes full use of the sampling results of the previoussampling points to improve the shape of the objective function and find the global optimal hyperparameter. One must let \(X=x_{1},x_{2},\ldots,x_{n}\) be a set of hyperparameter combinations and \(f(x)\) be the objective function of the hyperparameter \(x\). The principle of Bayesian optimization is to find \(x\), which can be written as  
\[x^{*}=argmaxf(x). \tag{13}\]  
The optimization method consists of two parts, a probabilistic surrogate model and a sampling function.  
According to [PERSON] theorem, the model parameters are updated in the following equation:  
\[p(f|D_{1:t})=\frac{p(D_{1:t}|f)p_{(f)}}{p(D_{1:t})} \tag{14}\]  
where \(D_{1:t}=\left\{(x_{1},f(x_{1})),(x_{2},f(x_{2})),\ldots,(x_{t},f(x_{t}))\right\}\) is the sample set; \(p(D_{1:t}|f)\) is the likelihood distribution of \(y\); \(p(f)\) is the prior probability model of \(f\); \(p(D_{1:t})\) is the marginal likelihood distribution, and \(p(f|D_{1:t})\) is the posterior probability model of \(f\).  
The sampling function is the referenced credential for the Bayesian optimization method to obtain the next sample point in the hyperparameter space. In this research, expected improvement (EI) is used as the sampling function, which is expressed as  
\[\left\{\begin{aligned} \alpha(x)&=[\mu(x)-q^{+}] \varnothing(Z)+\sigma(x)\varphi(z)\\ x_{t+1}&=argmaxf(x)\end{aligned}\right. \tag{15}\]  
where \(\alpha(x)\) is the objective function; \(\mu(x)\) is the mean value; \(\sigma(x)\) is the standard deviation value; \(q^{+}\) is the maximum value of the current objective function; \(\varnothing(Z)\) is the cumulative distribution function of the Gaussian distribution; \(\varphi(z)\) is the probability density function of the distribution, and \(x_{t+1}\) is the hyperparameter of the evaluation.
# Sustainable IoT Sensing Applications Development through GraphQL-Based Abstraction Layer
## 6 Results and Discussion
### Results Summary  
In these experiments, we observed a 46.27% increase in delay using REST architecture, when the number of subscribed resources (end-points) is increased from two to three. On three subscribed resources (rest-3R), REST results in an average delay of 481.48 ms, whereas the proposed approach could achieve an average delay of 159.54 ms, i.e., a significant 66.86% reduction in delay. Our experimental results reveal that the average amount of energy consumption increases from 1.04 to 1.53 mJ when the number of subscribed resources (end-points) is increased from two to three using REST-based architecture. However, average energy consumption for the proposed scheme was recorded as 67.97 mJ, i.e., 52.88% and 67.97% reduction as compared to the baseline REST architecture with two and three end-points, respectively.  
Figure 11: Scenario 2: Delay, energy consumption, packet delivery ratio (PDR), and throughput on different Loads.  
Comparative analysis of packet delivery ratio (PDR) and throughput results show that all schemes achieve satisfactory results. There was no significant impact on PDR using REST architecture with the increase in number of end-points from two to three and this is because fewer nodes (Z1 motes) were used in experimental scenarios. There was very little room for improvement in performance for the proposed approach and only 4.16% and 7.5% improvement was observed in PDR and throughput, respectively.  
The proposed approach achieves this performance because it can deliver the same data with a reduced number of round trips, thus saving a significant amount of energy that is highly desirable in IoT networks. This concludes our discussion on experimental results conducted in a controlled testbed environment with a limited number of IoT devices. These results give us the confidence to test the proposed approach in a more congested and challenging setup involving higher number of nodes to further validate its performance.
# Research on Key Parameters and Engineering Experiments of Coal Gangue Slurry Filling Technology
## 6 Industrial Test of Gangue Grouting Filling Technology in Goafs
### The Process and Layout of Grouting Filling  
#### 6.2.1 The Process of the Grouting Filling Test  
The grouting filling test process can be divided into five steps, as shown in Figure 11.  
1. The gangue in the gangue bin of the coal washing plant was transported by automobile to the storage shed.
2. The scraper was used to load the gangue to the first-stage crusher. The crushed gangue entered the second-stage crusher and then was transferred to the third-stage crusher through the belt, and it then entered the screening machine for screening. The finished gangue powder under the screen was transferred to the deferred storage through the well belt, and the unqualified gangue on the screen entered the third-stage crusher through the return belt to continue crushing.
3. The quantitative feeder under the deferred storage and water supply system transported the gangue powder and water to the first-stage mixer quantitatively according to a ratio of 7:3, and it then made the gangue slurry with a 70% concentration.
4. The gangue powder and water were stirred in the first-stage mixer, and the initially qualified gangue slurry overflowed to the second-stage mixer to continue stirring. Finally, the qualified gangue overflowed from the second-stage mixer to the hopper of the filling pump.  
Figure 10: Distribution of gangue slurry in the collapse zone.  
(5) The filling pump transported the gangue slurry in the slurry hopper to the number 22202 goat through the adjacent grouting borehole through the pipeline.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## 4 The Proposed Protocol
### Network Initialization Stage  
The network starts to monitor the patient's health status. In accordance with [23], once network initialization is finished, the coordinator broadcasts an information message, which informs all body sensors of its location. Moreover, all implanted sensors are assigned with unique IDs and notified with the relay nodes' positions by the coordinator. Then, all implanted sensors store the locations and broadcast information packets consisting of the location, energy status, and IDs to the relay nodes. In such a manner, all implanted sensors can give updates of their positions and energy status information to the relay nodes and the coordinator.