esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Cloud Based E-Government from the Perspective of Developing Countries
## 3 Cloud Based E-Government Framework
### Cloud Based G2G E-Government Framework Stages and Assimilation Process
#### 4: Identify the cloud service model  
At this stage, appropriate cloud service model needs to be chosen for the selected government services based on the needs. The service model is selected based on the services that are being migrated to the cloud. The proposed framework is used when there is already an electronic government system but could not achieve the goal because of the limited information communication technology infrastructure. Individual productivity software procurement for government department such as word processing could be replaced by software as a service (SaaS). However most of the application and system are custom made to meet individual government's need, the most used cloud service in e-government is infrastructure as a service (IaaS). Legacy system, government enterprise application those have dependencies with the operating system requires infrastructure as a service (IaaS).
## 2 Results and Discussion
### Effect of Microwave Pretreatment on the Free Phytosterols and Tocpherols Content of Oil
With an initial moisture level of 13-15%, the tocopherols content in oil from treated rapeseed increased to maximum at 4-5 min MW exposure period. Total tocopherols in oil from MW treated rapeseed increased to 3.09-6.06 mg/100 g, which was 6.50-14.02% higher than the control. The lipoprotein membrane damages may contribute to the release of tocopherols and improving their content in the extracted oil. However, it can be inferred from the reported results [18; 20; 24] that the tocopherols could probably decompose with relatively long microwave pretreatments. It can be inferred that the tocopherols content in extracted oil from treated peanut possibly decreased with MW pretreatment longer than 5 min.
## 3 Methodology
### Visualization and Localization of TB Manifestations
#### 3.3.1 Occlusion Sensitivity  
Occlusion sensitivity (OS) evaluates the sensitivity of the trained model to occlusion. It perturbs different portions of the image with an occlusion mask, typically a gray square mask, and scores the probability of the classification for each perturbed region [25]. Here, the probability score of occlusion of any image region was used to highlight the indicative regions and localize TB lesions. During OS map generation, the occlusion mask was slid through the image, and the probability score of the expected class for each mask was measured. If a region containing TB lesions was occluded, then the classifier should produce a probability lower than a threshold; this falling probability indicates that TB lesions are located in the region of the occlusion. By performing this procedure over the entire image, TB lesions can be detected and localized. Visualization and localization using OS are shown in Figure 4. The colors of the heat map have the following meanings: red = very high lesion, orange = high lesion, yellow = moderate lesion, cyan = low lesion, and blue = no lesion.
## 6 Conclusions  
The impulsive signal induced by bearing failure always includes transient and fluctuation features. However, the existing work on fluctuation research and application is insufficient. Therefore, this paper reports FE to represent the coexistence of transient and fluctuation inside faulty impulsive signatures and further proposes FE guided-VMD method. Compared with the traditional method, it overcomes the dependence on prior knowledge, such as frequency structure and the disadvantages of existing parameter selection methods, while maximizing the fault information more effectively. Moreover, we also study the noise elimination of the optimal mode and propose a new neighborhood statistical de-noising (NSD) method based on the SGWT. Simulation and practical verification show that this method can be well applied to this kind of sparse oscillation signal de-noising problem.  
Figure 14: Test signal and decomposition results by FE-guided VMD.  
Figure 15: (**a**) De-noising signal by NSD. (**b**) Local envelope spectrum.  
**Author Contributions:** Formal analysis, H.Z.; Investigation, X.Y.; Writing--review & editing, H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Key-Area Research and Development Program of Guangdong Province (grant number 2020B090927002) and the National Natural Science Foundation of China (grant number 51975462), and the project of National Natural Science Funds of Shaanxi Province (grant number 2021 JM-017).  
**Conflicts of Interest:** The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
# Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks
## 3 Materials and Methods
### Classification  
We split the dataset into three parts, 70% for training data, 15% for validation, and 15% for testing. We trained all models for 300 epochs using early stopping with a patience of 100 epochs using the categorical cross-entropy loss function and Adam optimizer. We used [PERSON] (2016) and Tensorflow libraries Keras (2016). Table 3 shows the different model architectures that we used in this work.  
For training, we optionally added online augmentation of Gaussian noise to the input keypoints with a mean of zero and standard deviation of 30 cm. This produces the effect of randomly varying the keypoints from the detected position simulating different ways to perform a sign. The noise is added at every input during training, generating different values on every iteration. This approach also helps to reduce overfitting and improve generalization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & \begin{tabular}{c} **Layer 1** \\ **Units** \\ \end{tabular} & \
\begin{tabular}{c} **Layer 2** \\ **Units** \\ \end{tabular} &
\begin{tabular}{c} **Parameters** \\ **(Thousands)** \\ \end{tabular} \\ \hline \multirow{6}{*}{RNN} & 32 & 16 & 8.782 \\  & 64 & 32 & 21.118 \\  & 128 & 64 & 56.542 \\  & 256 & 128 & 170.398 \\  & 512 & 256 & 570.142 \\  & 1024 & 512 & 2057.758 \\ \hline \multirow{6}{*}{LSTM} & 32 & 16 & 33.60 \\  & 64 & 32 & 81.502 \\ \cline{1-1}  & 128 & 64 & 220.318 \\ \cline{1-1}  & 256 & 128 & 669.982 \\ \cline{1-1}  & 512 & 256 & 2257.438 \\ \cline{1-1}  & 1024 & 512 & 8184.862 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model variations used for classification.  
Figure 5 shows the validation accuracy during training for the models with 32 units on the first layer and 16 units on the second layer.
## 3 Models Overview
### Model Evaluation  
The predictive model's accuracy was evaluated using the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and relative percent deviation (RPD). The R\({}^{2}\) value represents the goodness of fit of the model and ranges from 0 to 1. A value closer to 1 indicates a more accurate simulation of the corresponding model. The RMSE represents the dispersion of the model, with a smaller RMSE indicating a better fit and a larger RMSE  
Figure 3: Algorithm flowchart of MPARF.  
indicating a poorer fit. The RPD represents the reliability of the model. When the RPD is less than 1.5, the model is considered unreliable; when the RPD is between 1.5 and 2.0, the model is considered relatively reliable; and when the RPD is greater than 2.0, the model is considered highly reliable and suitable for model interpretation [24]. In summary, a good predictive model will have an R\({}^{2}\) value closer to 1, a smaller RMSE, and a larger RPD. The theoretical formula is as follows:  
\[\mathrm{R}^{2}=1-\frac{\sum_{\mathrm{i}=1}^{\mathrm{n}}\left(\mathrm{y_{i}}- \widetilde{\mathrm{y_{i}}}\right)^{2}}{\sum_{\mathrm{i}=1}^{\mathrm{n}}\left( \mathrm{y_{i}}-\overline{\mathrm{y}}\right)^{2}}, \tag{9}\]  
\[\mathrm{RMSE}=\sqrt{\frac{1}{\mathrm{n}}\sum_{\mathrm{i}=1}^{\mathrm{n}}\left( \mathrm{y_{i}}-\widetilde{\mathrm{y_{i}}}\right)^{2}}, \tag{10}\]  
\[\mathrm{RPD}=\frac{\mathrm{SD}}{\mathrm{RMSE}}, \tag{11}\]  
where \(\mathrm{y_{i}}\) is the measured value, \(\widetilde{\mathrm{y_{i}}}\) is the model prediction, \(\overline{\mathrm{y}}\) is the mean value of the sample, n is the number of samples, and SD is the standard deviation of the analyzed samples.
## 5 The Effects of RF Exposure on Humans  
The International Agency for Research on Cancer (IARC) at the WHO evaluated the carcinogenic risk for humans from prolonged exposure to RF, naming the risk as \"possible\" (2B) [90, 91]. However, recent evidence of long-term exposure studies on tumors might suggest endorsing a stricter set of criteria to raise the risk up to group 1 according to the IARC classification. The USA Federal Communications Commission's (FCC's) regulations for conducting environmental reviews under the National Environmental Policy Act (NEPA) recently issued a final document for regulating safety, economical and social aspects of the RF usage and the compliance with its limits, including SAR.  
To date, RF exposure remains a crucial issue among the general public as numerous subjects are reporting that they suffer from hypersensitivity to exposure [92, 93]. Scientists are trying to answer all calls for specific categories' interaction with environmental or professional RF exposure. Geographical location [90, 94, 95], occupational groups [96, 97], sections of the population [98] such as children and youngsters [99, 100] or pregnant women [101] are only several elements that have been investigated in a cohort of voluntary subjects participating in research or clinical studies, or considered in informative epidemiologic meta-analysis [102] surveys. Moreover, the impacts of diagnostic devices such as magnetic resonance scanners that emit RF were analyzed [103]. Given the delicate matter, it is not infrequent to find comments and rebuttals between different research groups. The vast diversity of protocols and the different quest of researchers together with the ever-increasing alert on these issues make the systematic review of all of the new findings virtually unfeasible. However, among recent studies, those on voluntary cohorts are perhaps the most interesting. We selected a few relevant examples on the investigation of how RF might impinge on everyday life of selected segments of the populations. In particular, a study that investigated mobile phone use in youngsters is quite alarming. [PERSON] [104] found that one hour of continuous use of the mobile phone immediately caused hearing dysfunction.  
General physiological state is also under investigation. For example, [PERSON] _et al._[105] show that repeated exposure to GMS cell phone 900 MHz does not interfere with non-linear dynamics of heart-rate variations in healthy volunteers.  
Regarding brain stimulation, many experiments were carried out to establish interference of RF and EEG in relation to cognitive functions [106] as well as with sleep [107, 108]. Recently, a group of Japanese youngsters underwent a test to evaluate the effect on EEG of mobile phone emission for three hours prior to sleep. Both subjective (headache, dizziness) or objective evaluation parameters (EEG spectra) did not differ in the placebo-exposed group [109].
# Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation
## 3 Performance Analysis
### Secrecy Capacity  
Next, we calculate the mutual information \(I_{n}(Y_{E};X)\) recovered by eavesdroppers. Without loss of generality, we assume that 0 and 1 are equally distributed, and that the transmissions over each subchannel are statistically independent. The mutual information can be derived as [26]:  
\[\begin{split} I_{n}(Y_{E};X)&=H_{n}(Y_{E})-H_{n}(Y_ {E}|X)\\ &=1+p_{e}\log_{2}p_{e}+(1-p_{e})\log_{2}(1-p_{e}),\end{split} \tag{31}\]where \(H_{n}(\cdot)\) represents the entropy operation, \(p_{e}\) represents the BER of eavesdroppers, \(X\) represents the transmitted information, and \(Y_{E}\) represents the information retrieved by eavesdroppers.  
Then, we derive the information leakage \(L\) expression as [26]:  
\[L=\frac{1}{N}\sum_{n=0}^{N-1}I_{n}(Y_{E};X). \tag{32}\]  
Finally, the expression of secrecy capacity \(C_{secrecy}\) can be derived as:  
\[C_{secrecy}=\frac{1}{N}\sum_{n=0}^{N-1}I_{n}(Y_{L};X)-L, \tag{33}\]  
where \(I_{n}(Y_{L};X)\) still represents mutual information, but it represents mutual information of legitimate users, \(Y_{L}\) represents the information retrieved by legitimate users, and \(p_{b}\) represents the legitimate users' BER.
## 2 Materials and Methods
### Data Analysis
#### 2.3.2 Mann-Kendall Non-Parametric Trend Analysis Applied on RESTREND  
The Mann-Kendall (MK) trend, a robust non-parametric statistical method, was used to investigate RESTREND in the study area. The MK's coefficient calculates the consistency of the increasing or decreasing trend and is often used in environmental studies [8]. MK trend analysis was applied to test the magnitude and significance of the slope to determine whether LD in the district was influenced by human activities or rainfall. Theli-Sen (Sen) slope estimates the magnitude of the residual trend and is insensitive to outliers [20]. The MK test is calculated with a statistic \(S\) formula:  
\[S=\sum_{k=1}^{N-1}\cdot\sum_{k=1}^{N}sgn\big{(}X_{k}-X_{j}\big{)} \tag{2}\]  
where: \(N\) is the numerical data points, \(X_{k}\) and \(X_{j}\) are the values at time \(k\) and \(j\) (\(k>j\)), respectively, \(sgn\) represents sign [52].  
The Sen's slope estimator determined the magnitude of the trend in NDVI or rainfall data. The Sen's slope test calculates the linear rate of change and the intercepts using the following formula:  
\[ST_{m}=\frac{X_{k}-X_{j}}{k-j}for\ (1<j<k<n) \tag{3}\]  
where: \(ST=\) slope, \(m=\) median, \(n=\) number of data points and \(k\), \(j=\) indices.  
\[p_{t}=X_{t}-r*t \tag{4}\]  
where: \(r=\) median STk (median from all slopes) with intercepts computed for every time steps \(t\) and \(p\) is the intercept.  
Sen's slope estimates the magnitude of increase or decrease in NDVI and rainfall per year. The MK significance test was used to test the statistical consistency of the Sen's slope trend at a 95% confidence interval (\(p\) = 0.05) [20]. The MK trend test is widely used in the literature and is less sensitive to outliers, missing values and irregular data distribution [53].
## 2 Description and Challenges of a Customized Biomedical Implantable Device
### Research Challenges in Implantable Devices
#### 2.2.2 The Encapsulation of the System  
Bio-compatibility of the final device is a main barrier and challenge for the spread of implantable sensors. The encapsulation has to satisfy different properties, especially with regard to its lifetime. For instance, it has to be biocompatible and have a low dielectric constant [77], as well as being conformal and resistant. Implantation of synthetic medical devices generates an immediate and complex material-related inflammatory response, such as blood and tissue incompatibility and bio-fouling [78]. Biofouling of the sensor membrane is an important cause of sensor dysfunction [79]. Therefore, the design of implantable BioMEMS devices must reduce this immune impact, minimize bio-fouling, reduce the physical effect of the implant on the surrounding tissues and reduce the degree of cell adhesion achieved by the implanted device. To avoid these adverse physiological effects, the implanted devices must be packaged with bio-compatible materials. However, bio-compatible materials might not always be compatible with the device requirements [78]. Currently, common and widely used materials in implanted biomedical devices with high compatibility are polyethylene glycol (PEG), polydimethylsiloxane (PDMS), PTMO (poly tetramethylen oxide [78] and parylene-C [80]. Polymer coatings are used for glucose sensors as they reduce the diffusion of interferences to the sensor while simultaneously balancing glucose and oxygen diffusion to enable an adequate glucose response. They are durable, inert, and capable of tolerating harsh environments produced by the FBR. Commonly evaluated polymers are, Nafion, polyurethane, polyethylene glycol (PEG), and hydrogels. Nafion is a perfluorosulfonic acid-based polymer that has been implemented as a bio-compatible coating. Polyurethane (PU) has been used extensively as an outer membrane to act as a bio-compatible interface with the surrounding host tissue. Surface passivation with polyethylene glycol (PEG) has been a widely studied strategy for resisting bio-fouling [81]. Hydrogels have a modulus similar to subcutaneous tissue and absorb water readily allowing easy diffusion of analytes to a sensor. _In vitro_ analysis in an osmotic glucose sensor evaluated identified 15 potential candidate materials which are shown in Table 2 below [79].
# RBF Neural Network Based Backstepping Control for an Electrohydraulic Elastic Manipulator
## 2 Electrohydraulic Elastic Manipulator Dynamics
### Dynamics of the Electrohydraulic Servo System  
The EHSs consists of a servo-valve and a linear hydraulic actuator, which is powered by a gear pump as shown in Figure 1. The relationship between the input and the spool displacement is presented as follows:  
\[x_{s}=k_{s}u \tag{1}\]  
where \(x_{s}\) is the spool displacement of the servo-valve (m); \(k_{s}\) is the spool displacement gain (m/V); and \(u\) is the input voltage of the servo-valve (V).  
**Remark 1**.: _In many EHSs, valve dynamics are faster than the dynamics of the other parts of the system. Additionally, the servo-valves are usually controlled by valve drivers. So, valve dynamics can be ignored without significant sacrifice of control performance._  
The actuator dynamics can be described as [26]  
\[\frac{V_{1}}{\beta}\dot{P}_{1}=-A_{1}\dot{x}_{c}-C_{\text{leak}}(P_{1}-P_{2})+ Q_{1} \tag{2}\]  
\[\frac{V_{2}}{\beta}\dot{P}_{2}=A_{2}\dot{x}_{c}+C_{\text{leak}}(P_{1}-P_{2})- Q_{2} \tag{3}\]  
where \(V_{i}=V_{0i}+(-1)^{i-1}A_{i}x_{c}\), \((i=1,2)\) is total control volume of the \(i\)th chamber; \(x_{c}\) is displacement of the cylinder; \(V_{0i}(i=1,2)\) are initial volume of two chambers; \(A_{1}\) and \(A_{2}\) are the rod area of the cylinder and the head-side area of the cylinder, respectively; \(\beta\) is effective bulk modulus; \(C_{\text{leak}}\) is coefficient of the internal leakage; \(Q_{1}\) is supplied flow rate to the forward chamber; and \(Q_{2}\) is return flow rate to the return chamber. \(Q_{i}(i=1,2)\) are related to the spool displacement of the servo-valve \(x_{s}\)[26].  
\[Q_{1}=k_{q}\alpha x_{s}\sqrt{\Delta P_{1}},\quad\Delta P_{1}=\left\{\begin{array} []{ll}(P_{S}-P_{1}),&x_{s}\geq 0\\ (P_{1}-P_{T}),&x_{s}<0\end{array}\right. \tag{4}\]  
\[Q_{2}=k_{q}\alpha x_{s}\sqrt{\Delta P_{2}},\quad\Delta P_{2}=\left\{\begin{array} []{ll}(P_{2}-P_{T}),&x_{s}\geq 0\\ (P_{S}-P_{2}),&x_{s}<0\end{array}\right. \tag{5}\]  
Figure 1: Electrohydraulic elastic manipulator.  
where \(k_{q}\) is flow gain coefficient of the servo valve; \(\omega\) is the servo valve area gradient; \(P_{S}\) is supply pressure of the EHSs; \(P_{T}\) is the tank pressure; and \(P_{1}\) and \(P_{2}\) are the pressure in the forward and return chambers, respectively.  
In the cylinder actuator, the force can be calculated as follows:  
\[F_{c}=A_{1}P_{1}-A_{2}P_{2} \tag{6}\]
# Survey of Blockchain-Based Applications for IoT
## 12 Industry-Specific Applications of Blockchains in the IoT
### The Industrial IoT (IIoT)  
In industrial environments, IoT sensors track equipment performance and production metrics. Blockchains can enhance the IIoT by ensuring tamper-proof data logging, enabling predictive maintenance, and creating audit trials for compliance. In addition, blockchains can facilitate secure and private data-sharing with data analytics providers. Using encryption and permissioned access mechanisms, a blockchain ensures that sensitive industrial data are accessible only to authorized entities. Analytics providers can access anonymized or aggregated data stored in the blockchain, thereby preserving privacy and enabling valuable insights. This secure framework encourages collaboration between industrial operators and analytics firms, leading to improved decision-making and operational efficiency.  
NFTs offer a novel way to monetize and distribute data. Industrial data such as equipment performance metrics or production insights can be tokenized as NFTs and sold over the blockchain. This approach enables data owners to monetize valuable datasets while maintaining transparency and control. By purchasing NFTs, analytics providers can access high-quality datasets to enhance their models and machine-learning algorithms, resulting in better outcomes in predictive maintenance, quality control, and supply chain optimization.  
Smart contracts play a critical role in automating industrial functions and optimizing costs. For instance, smart contracts can dynamically update based on real-life electricity rates obtained from oracles. This allows industrial systems to shift energy-intensive tasks to times when electricity rates are lower, thereby significantly reducing operational costs. By automating such energy-optimization processes, blockchain-based systems can enhance the efficiency and sustainability of industrial environments.
# Enhanced Steganography for High Dynamic Range Images with Improved Security and Capacity
## 1 Introduction
### Main Contributions  
This paper presents an improved steganography scheme for HDR images that are encoded with the RGBE format. The scheme builds upon the distortion-free steganography scheme proposed by [PERSON] et al., which is thoroughly analyzed in this paper. Although [PERSON] et al.'s scheme exhibits imperceptible changes in pixel differences before and after embedding, the scheme's embeddable pixels are scarce for general HDR images. This is due to the low capacity of the scheme and the disruption of number distribution during the embedding process, making stego-images vulnerable to statistical analysis attacks.  
To address these limitations, the proposed scheme employs pre-processing to convert the original pixels into embeddable pixels, thereby increasing the number of embeddable pixels. Additionally, post-processing is designed to eliminate abnormal number distributions by incorporating random numbers and addressing potential security vulnerabilities arising from statistical analysis. The primary contribution of this study lies in the proposed scheme, which not only builds upon the strengths of [PERSON] et al.'s scheme but also significantly improves the embedding capacity of HDR images. Simultaneously, the proposed scheme ensures the preservation of visual quality and reinforces security against statistical analysis attacks. The experimental result shows that the capacity increases 10 times without visual distortion.  
The rest of this paper is organized as follows. Section 2 briefly introduces the related work including [PERSON] et al.'s scheme and its potential weaknesses. The enhanced steganography scheme is proposed in Section 3. Experimental results and discussions are shown in Section 4, which is followed by the conclusion in Section 5.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 5 Network Architectures
### Feedforward Neural Networks  
Feedforward neural networks (FNN) are used in several ASC algorithms. [PERSON] et al. used an FNN architecture to concatenate features from an NMF decomposition and a constant-Q transform of the audio signal [61]. [PERSON] et al. combined an FNN with multiple Gaussian mixture model (GMM) classifiers to model the individual acoustic scenes [62].