esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 3 Simulation Platform Design  
A benchmark LV installation for an industrial customer is modeled, featuring a six-pulse controlled rectifier as the primary component. The platform is designed for flexibility in configuring parameters that influence voltage notch waveforms, including short-circuit power at the PCC, commutation reactance, firing angle, snubber circuit, and the rectifier's rated power. The simulation platform also considers additional components typical in industrial LV networks, such as a capacitor bank (non-detuned or detuned) for power factor correction and aggregated linear loads. The upstream network is modeled to account for background voltage unbalance and distortion.  
Figure 3 illustrates the benchmark LV installation implemented in Simulink. This installation provides a comprehensive framework for analyzing the key parameters influencing voltage notch waveforms. The detailed analysis and design of the main components and parameters are presented in the following sections.  
Figure 3: Benchmark LV installation implemented in Simulink for voltage notch simulation.  
Figure 2: Waveforms of a six-pulse controlled rectifier under different commutation conditions. The graphs display (from top to bottom): three-phase line currents, phase-to-neutral voltages, phase-to-phase voltages, and DC output voltage. (**a**) Ideal conditions: Commutation occurs smoothly without voltage notches. (**b**) Non-ideal conditions: Commutation leads to voltage notches in the waveforms.
## 2 Materials and Methods
### Data Analysis  
#### 2.6.1 Young's Modulus Estimation  
Once the data acquisition of the SWE measurement was completed, the requisite parameters were exported for data processing through MATLAB script (Mathworks, Natick, MA, USA). The boundary of the FDS muscle was outlined, and then a 10 \(\times\) 30 mm\({}^{2}\) region of interest (ROI) was defined within the muscle layer. The elasticity map was reconstructed, and the shear wave velocity (\(V\)s) was extracted from the map.  
Afterwards, the shear wave velocity (\(Vs\)) was used for muscle elasticity calculation. As \(Vs\) is proportional to the shear modulus of the muscle (\(\mu\); in a unit of kPa), \(\mu\) can be calculated by the equation as follows:  
\[\mu=\rho Vs^{2} \tag{1}\]  
where \(\rho\) is the density of muscle (1000 kg/m\({}^{3}\)). Regarding skeletal muscle being heterogeneous and/or anisotropic material, the \(Vs\) can be basically used as a reliable surrogate for the stiffness of the tissue [63; 64].  
However, previous studies have revealed that muscle shear modulus (\(\mu\)) has a strong linear relationship with [PERSON]'s modulus (\(E\); in a unit of kPa) when the scanning plane is alighted parallel to the muscle fibre direction [14]. Subsequently, the muscle is supposed to be isotropic, uniform and has quasi-incompressible characteristics, so \(\mu\) is directly related to \(E\). Therefore, \(E\) can be obtained from the equation as follows:  
\[E=3\mu \tag{2}\]  
or  
\[E=3\rho Vs^{2} \tag{3}\]  
Consequently, the value of \(E\) could be used for estimating the stiffness of the FDS muscle in this study [19; 21; 65].
# Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
## 5 Experimental Setup
### Training Procedure  
Each experiment runs for 200 training epochs, with 1 epoch being completed when the student has been updated based on all 100,000 transitions in the replay memory \(D\). Transitions are sampled in random order from \(D\) in sets of mini-batches with size 64. After each epoch, the student is evaluated for 50 episodes while collecting the average return, with the action selection being stochastic or deterministic, based on which performs best for this teacher model (see Table 1). The oldest 10% of the transitions in \(D\) are then also replaced by new environment interactions after each epoch. These environment  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Network** & **Ant-v3** & **HalfCheetah-v3** \\ \hline SAC Stochastic & 4682 \(\pm\) 1218 & 9010 \(\pm\) 113 \\ SAC Deterministic & 1797 \(\pm\) 993 & 8494 \(\pm\) 186 \\ PPO Deterministic & 1227 \(\pm\) 483 & 5735 \(\pm\) 723 \\ PPO Stochastic & 1111 \(\pm\) 453 & 5553 \(\pm\) 791 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average return and standard deviation for our PPO and SAC teachers on the chosen environments using either stochastic or deterministic action selection.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Network** & **Ant-v3** & **HalfCheetah-v3** \\ \hline Student 6 (\(\mu\) only) & 16,008 & 9862 \\ Student 6 (\(\mu\) and \(\sigma\)) & 16,528 & 10,252 \\ SAC Teacher & 98,576 & 73,484 \\ PPO Teacher & 23,312 & 142,348 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of parameters in our student networks, SAC, and PPO teacher.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Network ID** & **Layers** & **Neurons per Layer** & **Parameters** \\ \hline
1 & 2 & 16 & 492 \\
2 & 2 & 32 & 972 \\
3 & 3 & 32 & 2028 \\
4 & 4 & 32 & 3084 \\
5 & 3 & 64 & 6092 \\
6 & 4 & 64 & 10,252 \\
7 & 6 & 64 & 18,572 \\
8 & 4 & 128 & 36,876 \\
9 & 3 & 256 & 73,484 \\
10 & 4 & 256 & 139,276 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Architectures used for students with varying levels of compression.  
interactions are either student-driven or teacher-driven, based on the current configuration of the experiment. Each experiment configuration is repeated for five independent runs, resulting in the mean and corresponding standard deviations of the return and entropy at each epoch discussed in Section 6.  
We compute the entropy of the action distribution predicted by our students during testing to more tangibly evaluate how well the stochasticity of the teacher is maintained as part of the distillation process. Since these are continuous univariate normal distributions, we compute this using:  
\[H(x)=\frac{1}{2}\log(2\pi\sigma^{2})+\frac{1}{2} \tag{9}\]  
For a random variable \(x\sim N(\mu,\sigma)\), i.e., the action prediction. This entropy is then averaged over all steps in a trajectory.
## 2 Materials and Methods
### Synthesis of CMSC  
The carboxymethylylation of cellulose to produce CMSC involved three steps: Alkali cellulose formation, carboxymethylylation, and purification. The alkali cellulose was produced by adding cellulose (5 g) in isopropanol (100 mL) followed by drop-wise addition of 10 mL of 25% (\(w/v\)) sodium hydroxide solution with the resultant mixture stirred at 200 rpm for an hour using an automated stirrer. During alkylation, 3 g of sodium monochloroacetate (MCA) was added to the mixture which was further stirred at 45 \({}^{\circ}\)C for 3 h at 200 rpm. The reaction mixture was then cooled and filtered, and the product was suspended in 300 mL of methanol and stirred using a mechanical stirrer for 12 h to remove any undesired impurities. The suspension was then neutralized with glacial acetic acid, and washed three times with 70% (\(v/v\)) ethanol and finally with 95% (\(v/v\)) ethanol, filtered, and dried at 60 \({}^{\circ}\)C till constant weight [25]. Approximately, 5 g of synthesized CMSC was then dissolved in 100 mL of distilled water at 80 \({}^{\circ}\)C with constant stirring for 10 min. The mixture was then centrifuged for 1 min at 4000 rpm. The dissolved CMSC was reprecipitated in 100 mL acetone. Recovered CMSC was filtered and washed several times with ethanol and dried at 60 \({}^{\circ}\)C until constant weight. The resultant solid was ground and sieved through 150 \(\upmu\)m and was kept in a desiccator for characterization.
## 3 Method
### Application: Contact between Two Bodies  
The contact is analyzed by following the unilateral equations described in the Sognirini problem (contact between a rigid and an elastic body) and the Winkler-Westergaard problem (contact between  
Figure 3: General flowchart of the proposed IGA-BEM.  
elastic bodies). One of these equations, Equation (23), establishes that the normal displacements of one body, with respects to the other, are less than or equal to zero; therefore, there is no penetration between both bodies.  
\[u(x)\cdot n|_{\Gamma}\leq 0 \tag{23}\]  
If the contact is analyzed with the proposed modified IGA-BEM, there would be two bodies that share a common area that, after applying a load, reach equilibrium, as observed in Equations (24) and (25):  
\[\begin{array}{rl}u_{i}(p)&+\int_{\Gamma_{1}}T_{ij}(p,Q)u_{i}(Q)dS+\int_{ \Gamma_{AB}}T_{ij}(p,Q)u_{i}(Q)dS\\ &=\int_{\Gamma_{1}}t_{i}(Q)U_{ij}(p,Q)dS+\int_{\Gamma_{AB}}t_{i}(Q)U_{ij}(p,Q)dS \end{array} \tag{24}\]  
\[\begin{array}{rl}u_{i}(p)+&\int_{\Gamma_{2}}T_{ij}(p,Q)u_{i}(Q)dS+\int_{ \Gamma_{AB}}T_{ij}(p,Q)u_{i}(Q)dS\\ &=\int_{\Gamma_{2}}t_{i}(Q)U_{ij}(p,Q)dS+\int_{\Gamma_{AB}}t_{i}(Q)U_{ij}(p,Q)dS \end{array} \tag{25}\]  
\(\Gamma_{1}\) and \(\Gamma_{2}\) represent the boundaries of body 1 and body 2, respectively, that are not in contact. \(\Gamma_{AB}\) represents the region of the common boundary between the two bodies, that is, the contact zone, (Figure 4). Since this region is unknown, an optimization algorithm will be applied to find it. The flowchart of the contact methodology can be seen in Figure 5.  
The first stage of the flowchart consists of executing the complete diagram of Figure 3. The second stage is to find the intersection between the two bodies (points \(A\) and \(B\)). If these elements had the same Young's modulus and the applied force is large enough, the nature of the IGA-BEM would create penetration between both bodies. This situation is not real; what physically happens is that these elements are deformed without penetration. The deformation value depends on the applied force and the materials properties.  
The third stage is to modify the control points, using the PSO algorithm to create new body geometries with the same area as the originals but fixing the contact area. To ensure that the contact area is correct, the same load is reapplied. If the contact zone changes, then the cycle is repeated; if it does not change, or the difference between one iteration and another is minimal, then the contact algorithm is stopped. The details of the PSO algorithm are described in the next section.  
Figure 4: Contact zone between bodies.
## 2 Materials and Methods
### Data Preprocessing  
All DICOM images underwent anonymization and conversion to PNG format following region-of-interest extraction. Ground truth segmentation masks were generated through manual annotation by a radiologist with over five years of experience in abdominal imaging. Our preprocessing pipeline included the following:  
* Resampling images to an isotropic resolution of \(1\times 1\times 1\) mm\({}^{3}\);
* Cropping volumes around the adrenal regions;
* Normalizing pixel intensities using z-score normalization;
* Implementing data augmentation through random rotations (\(\pm 15^{\circ}\)), horizontal and vertical flips, contrast-limited adaptive histogram equalization (CLAHE), and brightness variation (\(\pm 20\%\)).  
Images underwent standardized preprocessing steps to ensure consistency across the dataset. Z-score normalization was applied using the formula \((\Pi-\mu)/\sigma\), where I represents the original image intensity, \(\mu\) represents the mean intensity of the image, and \(\sigma\) represents the standard deviation. For data augmentation, random rotations were limited to \(\pm 15^{\circ}\) to maintain anatomical plausibility. CLAHE was applied with a clip limit of 3.0 and a tile grid size of 8 \(\times\) 8 pixels. Brightness variations were implemented by adjusting pixel values by a random factor within \(\pm 20\%\) of the original value.  
The dataset was stratified and divided into training (70%), validation (15%), and testing (15%) subsets, ensuring balanced representation of gland laterality and scanner types.
## 3 Test Results and Analysis
### Strain  
Figure 13a illustrates the layout of the column root strain gauges for each specimen. Vertical strain gauge one and horizontal strain gauge two are deployed at 50 mm above the top surface of the base to measure specimen deformation. The strain variation patterns of the six specimens are essentially consistent. Figure 13b through Figure 13g show horizontal force-strain curves of typical specimens CFST-SR, CFST-DT, and CFST-MC.  
Figure 13: Lateral load versus steel strain relations. (**a**)positions of strain gauges on the bottom surface of steel tube; (**b**) Strain1 (CFST-SR); (**c**) Strain2 (CFST-SR); (**d**) Strain1 (CFST-DT); (**e**) Strain2 (CFST-DT); (**f**) Strain1 (CFST-MC); and (**g**) Strain2 (CFST-MC).  
The specimen horizontal force-strain curves show that at the initial stage of loading, the column roots are under alternating tension and compression in the vertical direction. As the lateral drift gradually increases, the specimen root is always in a compression state in the vertical direction. The vertical strains of specimen CFST-DT and specimen CFST-MC do not reach the yield strain in the tensile direction. Specim CFST-SR has both a deployed horizontal and vertical stiffeners, which changes the stress state at the strain measurement point for the external steel tube. Therefore, this specimen reaches the yield strain in the tensile direction. The horizontal strains of all specimens are always in the tensile state and have reached the yield strain.
# A Survey on Emerging Blockchain Technology Platforms for Securing the Internet of Things
## 3 Blockchain Platforms
### Ethereum Blockchain  
Ethereum, introduced in late 2015 by [PERSON], is one of the most distinguished blockchain platforms and frameworks for developing decentralised applications (DApps). The enhancement of security in IoT systems and the necessity for decentralised IoT systems resulted in the integration of Ethereum into IoT ecosystems. This integration was evident in various research studies, showcasing its utilisation across industries like Supply Chain, Healthcare, Transportation, Finance, and Agriculture. Ethereum's robust security stems from its carefully chosen components, including the consensus algorithm, smart contracts, encryption methods, and decentralised network. The consensus algorithm, transitioning to Proof of Stake with Ethereum 2.0, ensures transaction validation and network security. Smart contracts, which are self-executing agreements coded into the blockchain, enhance security by automating contract enforcement. Strong encryption methods like public-key cryptography safeguard data privacy. The decentralised network structure, powered by globally distributed nodes, eliminates single points of failure, bolstering security and resilience against potential threats. These elements collectively contribute to Ethereum's reputation as a secure platform for decentralised applications and digital transactions.  
Numerous investigations and initiatives have delved into the application of the Ethereum blockchain in bolstering the security of Internet of Things (IoT) systems. The essence of these endeavours lies in utilising a decentralised, unalterable distributed ledger technology to facilitate secure and openly accessible data management for IoT gadgets. This strategy cultivates a state of affairs devoid of reliance on trust within the IoT environment, promoting transparency and guaranteeing data integrity, culminating in a more robust and dependable communication network among devices. [PERSON] et al., [PERSON] et al. and [PERSON] et al. [39; 40; 41; 42] are examples of work that tend toward harnessing the security benefit of IoT-Ethereum integration. [PERSON] et al. [39] used this integration to secure vehicle communication through their proposed decentralised cloud computing platform. [PERSON] et al. [43] adopted this integration to solve a major problem in the vehicle rental system. Other works such as [44; 45; 46] experimented on ethereum integration with IoT systems. The modification and addition of technologies were also discussed in some works such as [47], whose work adopted PoA for consensus in place of PoS/PoW. Fog computing and Interplanetary File systems were also introduced in [48] and [49], respectively.
# 5G-Based Smart Healthcare and Mobile Network Security: Combating Fake Base Stations
## 3 Fake Base Stations
### Threats to Smart Healthcare Networks  
Fake base stations pose a significant threat to smart healthcare networks, jeopardizing the confidentiality, integrity, and availability (the CIA triad model) of sensitive healthcare data and services.  
* Confidentiality:  
A compromised access control and authentication mechanism may allow unauthorized access to healthcare data through fake base stations, leading to the exposure of sensitive patient information. Poorly implemented encryption or its absence may expose system information and user data to eavesdropping, thus infringing on patients' privacy.
* Integrity:  
Fake base stations that act as a MitM can intercept and modify information transmitted between devices and legitimate base stations, compromising the integrity of smart healthcare data.
* Availability:  
Hacked base stations or fake BSs can cause DoS attacks, severing the connection of health sensors and devices to the authentic network and rendering crucial healthcare services and data unavailable to patients and healthcare providers.  
Figure 2 illustrates the security threats posed by fake base stations within smart healthcare networks, underscoring the urgency for effective countermeasures to ensure the  
integrity and privacy of patients' data and to maintain the uninterrupted availability of essential healthcare services.
# Fourier Domain Adaptation for the Identification of Grape Leaf Diseases
## 2 Materials and Methods
### Methods  
#### 2.2.1 Motivation and Intuition  
In recent years, much of the ongoing work in grape leaf disease identification has relied heavily on training within the same dataset. Nonetheless, real-world agricultural scenarios present certain challenges: (1) securing a significant volume of grape leaf disease data is arduous, particularly in the confines of a single grape planting base. (2) Model generalization, even when trained on identical datasets, necessitates enhancement. This entails that despite optimal training, the model may not deliver satisfactory outcomes when applied to datasets with diverse environmental contexts.  
During our quest to enhance model generalization, we uncovered the potential of domain adaptation. This technique has demonstrated success across various domains in improving model generalization. However, certain methodologies rely on sensor-derived features to address the statistical distribution shift. Unfortunately, merely matching means and covariances falls short in consistently aligning distinct domains. Moreover, some methods, although effective, entail intricate computational processes that are not particularly user-friendly for practical agricultural applications.  
Our intuition in doing so stems from the observation when image classification maintains a high level of performance; a low level of spectrum (amplitude) can change significantly. No matter whether something in the picture is a plant leaf or a disease, it should not be affected by sensors, light sources, or other factors. Fourier transform is simpler than other methods and they can be registered.  
To handle the case where the model's generalization capability needs to be enhanced and the dataset for grape leaf disease is difficult to obtain, Figure 2 depicts the suggested Fourier transfer's processing flow. Images of three different grape leaf diseases that were captured in a field location serve as the input. They separated the leaves from the captured photos and categorized each sick leaf into severe and general categories. The classification of three CNN identification models for grape leaf diseases was conducted and the outcomes were compared. The target domain data from the grape leaf disease dataset are visualized using the most accurate VGG13 identification finding and then the farthest point sampling method is used to reduce the chance of model success.  
Figure 1: Examples of grape leaf disease image.
# Deep-Sea Underwater Cooperative Operation of
## 2 Related Studies on Cooperative Operations
### Autonomous Underwater Vehicles and Remotely Operated Vehicles  
The cooperative operation of AUVs and ROVs combines the advantages of AUV area detection and ROV fixed-point operation. In July 2017, based on the \"Kexue\" ship, the Exploration 4500 AUV and the Discovery ROV conducted China's first cooperative operational test with AUVs and ROVs in the cold spring area in the northern South China Sea. It created a new model of marine scientific investigation [18]. The AUV independently conducted near-seabed optical surveys. Scientists manipulated the robotic arms of the ROV to collect data on submarine cold springs in situ and perform in situ quantitative analysis. Furthermore, they remotely controlled the AUV to perform optical detection at the position adjacent to the ROV and simultaneously used the camera on the ROV to closely monitor the working status of the AUV in real time.
###### Abstract  
Early detection of the dysfunction of the cardiac autonomic regulation (CAR) may help in reducing cannabis-related cardiovascular imorbidities. The current study examined the occurrence of changes in the CAR activity that is associated with the consumption of bhang, a cannabis-based product. For this purpose, the heart rate variability (HRV) signals of 200 Indian male volunteers, who were categorized into cannabis consumers and non-consumers, were decomposed by Empirical Mode Decomposition (EMD), Discrete Wavelet transform (DWT), and Wavelet Packet Decomposition (WPD) at different levels. The entropy-based parameters were computed from all the decomposed signals. The statistical significance of the parameters was examined using the Mann-Whitney test and \(t\)-test. The results revealed a significant variation in the HRV signals among the two groups. Herein, we proposed the development of machine learning (ML) models for the automatic classification of cannabis consumers and non-consumers. The selection of suitable input parameters for the ML models was performed by employing weight-based parameter ranking and dimension reduction methods. The performance indices of the ML models were compared. The results recommended the Naive Bayes (NB) model developed from WPD processing (level 8, db02 mother wavelet) of the HRV signals as the most suitable ML model for automatic identification of cannabis users.  
cnnabis; cardiac autonomic regulation; HRV signal; signal decomposition; machine learning 4.0/. +
Footnote †: journal: Applied Sciences
## 3 Test Results
### Empirical Relation between Fracture Toughness and Tensile Strength  
To gain fracture toughness \(K_{IC}\) from tensile strength \(\sigma_{t}\) of rock, a relevant empirical relation has been found to be very useful in rock engineering. Much work has confirmed that \(K_{IC}\) as a mechanical parameter of rock is associated with the tensile strength ([PERSON], 1977; [PERSON], 1977). To date, [PERSON]'s empirical formula \(\sigma_{t}=6.88K_{IC}\) derived from the statistics of numerous rock tests is widely used, but it cannot be used for slate ([PERSON], 1977) due to lack of data for slate tests. Based on the test results of carbonaceous slate specimens, we can obtain an empirical relationship between mode I fracture toughness and tensile strength, as shown in Figure 9, which is represented by an approximately linear formulation where its correlation coefficient R\({}^{2}\) is 0.99, indicating that the method of estimating rock \(K_{IC}\) by \(\sigma_{t}\) is highly fittable. The present fitting formulation can be read as  
\[K_{IC}=0.094\sigma_{t}+0.036 \tag{4}\]  
This fracture toughness is very crucial in evaluating the fracture behavior in rock engineering, using fracture mechanics and numerical modeling.  
Figure 8: Box and whisker plot of mode I fracture toughness for different SCB specimens.