esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 2 Fundamentals of AMI
**Clinics and outpatient facilities:** AMI deployed in clinics or outpatient facilities can streamline healthcare by minimizing clinical wait times [13]. Smart clinic environments utilize IoT sensors and AI algorithms to manage patient appointments, optimize waiting times, and personalize care pathways based on individual patient needs and preferences. 3. **Smart home:** Smart home monitoring plays a pivotal role in determining the efficiency rate. For instance, daily activity monitoring could benefit healthy adults as well as children, individuals with disabilities, and the elderly. Moreover, these environments are effective when dealing with situations such as the COVID-19 pandemic [14]. Constant AMI-based monitoring systems can assist in daily living activities, monitor health conditions, and provide personalized care and support services. 4. **Rehabilitation centers:** Smart rehabilitation could combine physical and cognitive activities to provide better rehabilitating facilities that are less boring while still being very effective in patient recovery. Moreover, AMI could aid in the treatment of  
Figure 1: General architecture of AMI assisted living. individuals with Acquired Brain Injury (ABI) [15] by employing specialized devices to regulate patients' movements and certain physiological responses, such as changes in heart rate, throughout the rehabilitation process. 5. **Mobile healthcare units:** With the advancement of the Internet of Vehicles, it has become possible to integrate AMI into mobile healthcare units such as ambulances. This allows for the delivery of medical services and critical care interventions while on the move. Smart ambulances [16] can make real-time decisions based on traffic conditions or hospital loading conditions, helping to minimize travel time and select the best hospital for a specific medical emergency.
## 4 Algorithm Design
### Barotmetric Sensor Zero Drift Height Error Compensation
#### 4.2.1 Modeling and Training of BP Neural Networks  
A BP (back propagation) neural network is a multi-layer feed-forward neural network trained according to the error back propagation algorithm, which is one of the most widely used neural network models. A BP network can learn and store many input-output pattern mapping relationships without revealing the mathematical equations describing such mapping relationships beforehand, and it can classify arbitrarily complex patterns and has excellent multi-dimensional function mapping ability.  
A typical BP neural network topology consists of input, hidden, and output layers, as shown below.  
As shown in Figure 10, \(X_{1}\) to \(X_{i}\) are the data in the input layer, which are the inputs of information and transmit the data to the subsequent hidden layer; \(\omega_{ij}\) are the weights of the input layer neurons transmitting the data to the hidden layer neurons; \(\omega_{ik}\) are the weights of the hidden layer neurons transmitting the data to the output layer neurons; and \(Y_{1}\) are the activation functions of the output data hidden layer and output layer using tangigmoid and purelin functions, respectively. The hidden layer can be single or multi-layer; theoretically, if there are enough hidden neurons, the BP neural network can approximate any nonlinear mapping.  
According to the previous data, it can be seen that the data input to the BP neural network contains two-dimensional feature vectors of time data and temperature data, the input layer of the neural network is set to be two neurons, the barometric pressure value under the current time is predicted by the neural network to compensate for the temperature and the zero drift, the output value of the neural network is set to be the barometric pressure value, and the output layer of the neural network is set to be one neuron. Since the number of neurons in the hidden layer is very important for the final training results of the neural network, and an inappropriate number of neurons in the hidden layer will produce a large error in the training results of the neural network, the reference value is, therefore, usually calculated by the empirical Formula (8), and then, the number of neurons is finally determined by the experimental method.  
\[h=\sqrt{a+b}+c \tag{8}\]  
where \(h\) is the calculated reference value, \(a\) and \(b\) are the number of neurons in the input layer and the number of neurons in the output layer, respectively, and \(c\) is an adjustable parameter in the range of 2-10.  
Figure 10: Topology of BP neural network.
## 4 Background Study
### Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Technique/Method** & **Model** & **Results** \\ \hline \multirow{4}{*}{[29]} & 3D patch-based mi-GAN & \multirow{4}{*}{mi-GAN + DenseNet} & SSIM: 0.943, Multi-class Accuracy: 76.67\%, pMCI vs. sMCI Accuracy: 78.45\% \\  & & multiple adjacent slice & \\  & reconstruction using & & \\  & WGAN-GP + \(\ell\)1 loss and & & \\  & self-attention & & \\ \hline \multirow{4}{*}{[30]} & Generative Adversarial & \multirow{4}{*}{GAN + FCN} & Improved AD classification with \\  & Network (GAN), Fully & & accuracy increases up to 5.5\%. SNR, \\  & Convolutional Network & & BRISQUE, and NIQE metrics showed \\  & (FCN) & & significant image quality improvements. \\ \hline \multirow{4}{*}{[32]} & TPA-GAN for PET & \multirow{4}{*}{TPA-GAN + PT-DCN} & AD vs. CN: ACC 90.7\%, SEN 91.2\%, SPE 90.3\%, F1 90.9\%, AUC 0.95; pMCI vs. \\  & classification & & sMCI: ACC 85.2\%, AUC 0.89 \\ \hline \multirow{4}{*}{[33]} & THS-GAN: Tensor-train & \multirow{4}{*}{THS-GAN} & AD vs. NC: AUC 95.92\%, Acc 95.92\%; 3 CI vs. NC: AUC 88.72\%, Acc 89.29\%; 3D-DenseNet & AD vs. MCI: AUC 85.35\%, Acc 85.71\% \\ \hline \multirow{4}{*}{[34]} & CT-GAN with & \multirow{4}{*}{GAN + Transformer with Bi-Attention} & AD vs. NC: Acc = 94.44\%, Sen = 93.33\%, \\  & Cross-Modal Transformer & & Spe = 95.24\%/LMCI vs. NC: Acc = 93.55\%, Sen = 90.0\%, Spe = 95.24\%/EMCI \\  & and Bi-Attention & & vs. NC: Acc = 92.68\%, Sen = 90.48\%, Spe = 95.0\% \\ \hline \multirow{4}{*}{[35]} & WGANGP-DTL & \multirow{4}{*}{WGANGP + Inception v3 + DBN} & Accuracy: 99.70\% Sensitivity: 99.09\% \\  & (Wasserstein GAN with & & \\  & Gradient Penalty + Deep & & \\  & Transfer Learning using & & \\  & Inception v3 and DBN) & & \\ \hline \multirow{4}{*}{[36]} & BPGAN (3D BicycleGAN & \multirow{4}{*}{3D BicycleGAN (BPGAN)} & Dataset-A: MAE = 0.0318, PSNR = 26.92, \\  & with Multiple Convolution & & \\  & U-Net, Hybrid Loss) & & \\  & & & \\  & & & \\  & & Denoising Diffusion & \\  & & Probabilistic Model & \\  & MRI Imputation) & (DDPM) with modified & & \\  & & U-Net & \\ \hline \multirow{4}{*}{[37]} & Wavelet-guided Denoising & \multirow{4}{*}{Wavelet Diffusion with Wavelet U-Net} & SSIM: 0.8201, PSNR: 27.15, FID: 13.15 \\  & Diffusion Probabilistic & & (\(\times\)4 scale); Recall \(\sim\)90\% (AD vs. NC); \\  & Model (Wavelet Diffusion) & & \\  & & Wavelet U-Net & improved classification performance \\ \hline \multirow{4}{*}{[39]} & CNN + GAN (DCGAN to & \multirow{4}{*}{CNN + DCGAN (data augmentation)} & Accuracy: 96\% (with GAN), 69\% \\  & augment data; CNN for & augmentation) & (without GAN); classification across 4 \\ \cline{1-1}  & & & AD stages \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._
## 3 Proposed Joint Resource Optimization for NOMA-Enabled Vehicular Networks
### Proposed Optimal Resource Optimization
#### 3.2.1 Formulation (Phase A): Association, Channel Assignment and NOMA-OMA Switching
On the other hand, some channels are to serve two users each, enabling NOMA. Such dynamic switching between OMA and NOMA is allowed especially by the constraint Equation (1). In the proposed resource optimization approach, OMA (orthogonal multiple access) and NOMA (non-orthogonal multiple access) modes are dynamically selected based on the number of vehicle users (VUs) assigned to each channel. OMA mode: When only one VU is assigned to a channel, the system uses OMA mode, providing exclusive access to avoid interference. NOMA mode: When two VUs share a channel, NOMA mode is activated, differentiating users by assigning higher power to the VU with weaker channel gain. This allows simultaneous use of the same frequency through Successive Interference Cancellation (SIC). The binary decision variable in Phase A controls OMA-NOMA switching, maximizing channel use while meeting each VU's quality of service (QoS) requirements. In summary, the optimization problem P. (10) (i.e., Equations (10a)-(10i)) given below is the complete formulation of the Phase A solution we propose. \[\min_{\mathbf{X},\mathbf{P}} \sum_{b}\sum_{c}\sum_{u}\Bigl{(}p^{h}_{bcu}+p^{l}_{bcu}\Bigr{)}\] (10a) s.t. \[\forall b,c:\quad\sum_{u}x^{h}_{bcu}\leq 1,\quad\sum_{u}x^{l}_{ bcu}\leq 1,\quad\sum_{u}x^{l}_{bcu}\geq\sum_{u}x^{h}_{bcu} \tag{10b}\] \[\forall b:\quad\sum_{c}\sum_{u}\Bigl{(}x^{h}_{bcu}+x^{l}_{bcu} \Bigr{)}\leq\kappa_{b}\] (10c) \[\forall u:\quad\sum_{b}\sum_{c}\Bigl{(}x^{h}_{bcu}+x^{l}_{bcu} \Bigr{)}=1\] (10d) \[\forall b,u:\quad\sum_{c}\Bigl{(}x^{h}_{bcu}+x^{l}_{bcu} \Bigr{)}\leq a_{bu}\] (10e) \[\forall b,c,u:\quad 0\leq p^{h}_{bcu}\leq x^{h}_{bcu}\cdot p^{ \max}_{cth},\quad 0\leq p^{l}_{bcu}\leq x^{l}_{bcu}\cdot p^{\max}_{ch}\] (10f) \[\forall b,c:\quad\sum_{u}\Bigl{(}p^{h}_{bcu}+p^{l}_{bcu}\Bigr{)} \leq p^{\max}_{ch}\] (10g) \[\forall b:\quad\sum_{c}\sum_{u}\Bigl{(}p^{h}_{bcu}+p^{l}_{bcu} \Bigr{)}\leq p^{\max}\] (10h) \[\forall b,c:\quad\sum_{u}g_{bcu}\cdot p^{h}_{bcu}+\rho\Biggl{(}1- \sum_{u}x^{h}_{bcu}\Biggr{)}\geq\eta_{\rm thr}\Biggl{(}\sum_{u}g_{bcu}\cdot p^{ l}_{bcu}+\sigma\Biggr{)},\] \[\min_{\mathbf{X},\mathbf{P}} \sum_{b}\sum_{c}\sum_{u}\Bigl{(}p^{h}_{bcu}+p^{l}_{bcu}\Bigr{)}\]0
# ZIF-75 under Pressure: Negative Linear Compressibility and Pressure-Induced Instability
## 2 First-Principles Methods  
The crystal structure and the behavior of the zeolitic imidazolate framework ZIF-75 [2; 3] (CCDC code HIFVUO and deposition number 254162) under pressure were modeled by employing periodic density functional theory methods using plane-wave-basis sets and pseudopotentials [114]. The CASTEP (Cambridge Serial Total Energy Program) computer program [115], belonging to the Materials Studio program suite [116], was utilized in all the calculations. The specific energy density functional employed in the computations is the Perdew-Burke-Ernzerhof (PBE) functional [117] supplemented with Grimme dispersion corrections [118], as implemented in the CASTEP code. The pseudopotentials utilized for all the atoms in the unit cell of ZIF-75 were standard norm-conserving pseudopotentials [119] provided in the CASTEP code (00 PBE-OP type). The calculations were performed using demanding calculation parameters: a cut-off for the kinetic energy of the plane waves of 1000 eV and a Monkhorst-Pack \(k\)-mesh [120] with a grid density of 0.4 A\({}^{-1}\). The full-geometry optimization, including the atomic positions and unit cell lattice parameters, was performed using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method [121]. All the optimizations, including those of ZIF-75 under pressure, were performed with stringent convergence thresholds in the variation of the total energy, maximum atomic displacement, maximum atomic force, and maximum stress: \(2.5\times 10^{-6}\) eV/atom, \(2.5\times 10^{-4}\) A, 0.005 eV/A, and 0.0025 GPa, respectively. The geometry optimizations were performed using the crystal structure reported by [PERSON] et al. [2] as a starting point, and, as customary, they were performed by relaxing all symmetry constraints for the verification of the adopted crystallographic space symmetry group. Since the unit cell of ZIF-75 is very large, containing 272 atoms, the symmetry-unrestricted full-geometry optimizations are computationally expensive. The X-ray powder-diffraction patterns [122] of ZIF-75 were derived from the computed and experimental crystal structures using software REFLEX included in the Materials Studio program suite [116] using CuK\({}_{\alpha}\) radiation (\(\lambda=1.540598\) A). The elasticity constants, that is, the elements of the elastic tensor of ZIF-75, were determined in the optimized crystal structure using the finite deformation method (FDM) [123]. This technique is highly efficient and reliable for describing the elastic response of materials and has been employed successfully in many previous works concerning uranium-containing materials [109; 110; 111; 112; 113] as well as organic crystals [102; 103; 104] and metal-organic compounds [81; 82; 83; 105; 106; 107; 124]. In particular, the elasticity of a wide variety of MOFs and microporous materials has been studied [81; 82; 83].
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 4 Discussion
### Applications of Flexibility Quantification
#### 4.1.1 Integration of Renewable Energy Resources
First, the knowledge of the locations and levels of flexibility available across the grid can inform grid planners regarding the most appropriate locations and/or penetration levels for renewable generation as well as what energy storage resources are needed and where. Furthermore, information on when demand flexibility might be available can be used in power grid energy dispatch, especially in allocating reserves. The general idea that demand flexibility can play a major role in facilitating renewable energy integration and system planning was cited across studies [1, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 26, 27, 31, 32, 34, 38, 40, 43, 49, 50, 55, 56, 58, 61, 64]. Accurate flexibility models can help turn demand into a semi-dispatchable resource that can alleviate some of the operational uncertainties of renewable resources. This can in turn help improve the seamless integration of these resources with the grid, hence, facilitating decarbonization efforts at all levels [43, 68]. In terms of smart buildings, understanding flexibility potential and cost savings when pairing electrification with flexibility can provide valuable insights to guide the electrification of buildings [30].
## 1 Introduction
This feature, combined with the framework's ability to provide detailed information on the spatial distribution of UHI patterns, enables our method to provide valuable support for urban planners and policy makers in choosing targeted, nature-based heat resilience and heat mitigation solution strategies such as the implementation of green roofs and eco-streets, as well as enabling the evaluation of mitigation measures. The proposed framework was tested on the metropolitan city of Naples, Italy, using land surface temperature (LST) and normalized difference vegetation Index (NDVI) datasets derived from Landsat 8. By clustering these datasets, our integrative approach provides a finer-resolution understanding of urban heat distribution, delivering refined precision in UHI mapping. The paper is organized as follows: Section 2 explores the latest advancements in UHI detection using machine and deep learning methods, highlighting the shift from traditional supervised approaches to unsupervised clustering techniques. Section 3 provides a brief overview of FCM and analyzes the validity indices employed to find the optimal number of clusters. Section 4 introduces the proposed framework and details its functional components. Section 5 presents and discusses the results of the tests executed on the case study. Finally, the conclusions are outlined in Section 6.
###### Abstract  
The present work offers a thorough analysis of the impact of water velocity on phase change material (PCM) melting in a vertical cylindrical container. A detailed quantitative analysis uses sophisticated numerical techniques, namely the ANSYS/FLUENT 16 program, to clarify the complex relationship between enthalpy and porosity during the melting process. The experimental focus is on phase transition materials based on paraffin wax, particularly Rubitherm RT42. This study's primary goal is to evaluate the effects of different water velocities (that is, at velocities of 0.01 m/s, 0.1 m/s, and 1 m/s) on the PCM's melting behavior at a constant temperature of 333 K. This work intends to make a substantial contribution to the development of thermal energy storage systems by investigating new perspectives on PCM behavior under various flow circumstances. The study's key findings highlight the possible ramifications for improving PCM-based thermal energy storage devices by revealing significant differences in melting rates and behavior that correlate to changes in water velocities. Future research is recommended to explore the impact of temperature variations, container geometries, and experimental validation to improve the accuracy and practicality of the results and to advance the creation of sustainable and effective energy storage solutions.  
phase change material (PCM); melting process; heat transfer; thermal energy storage; velocity effect +
Footnote †: journal: Applied Sciences  
Article  
A Numerical Study of the Effect of Water Speed on the Melting Process of Phase Change Materials Inside a Vertical Cylindrical Container  
[PERSON]  
1 Petroleum Engineering Department, College of Engineering, University of Kerbala, Karbala 56001, Iraq; [EMAIL_ADDRESS] (A.F.K.); [EMAIL_ADDRESS] (F.L.R.); [EMAIL_ADDRESS] (S.A.L.);  
Department of Building Engineering, Energy Systems and Sustainability Science, University of Gavle, 801 76 Gavle, Sweden 1  
Department of Cooling and Air Conditioning Engineering, Imam Ja'afar Al-Sadiq University, Baghdad 10011, Iraq; [EMAIL_ADDRESS]
## 4 Findings
### Other Works
#### 4.7.4 Reputation Systems  
To address the issues of a single point of compromise and failure, a decentralized trust model is proposed [127]. Using past interactions with public fog nodes, this approach allows for maintaining the reputation of network users. The architecture of this model consists of the following components. (1) The fog node has a unique Ethereum address that associates with a list of evaluations that contains information such as generic reputation score, cost of service, storage, and latency. (2) The IoT client provides feedback after every interaction with any fog node, and the credibility factor measures the fairness and honesty using several provided ratings, consistency, and trustworthiness. (3) Five types of smart contracts are used, namely custom reputation, credibility, reputation, node management, and client registration. The authors included a diagram to illustrate the workflow of computing the reputation of fog nodes. The process is discussed in detail between the blockchain platform, off-chain fog nodes, and front-end DApps. This approach is implemented using the Ethereum blockchain to ensure network security, immutability, and validation. Based on the evaluations for common security vulnerabilities, the results show that the smart contracts are safe against them, and there are no major security issues when running software that identifies security vulnerabilities.
## 4 System Design and Goals
### Message Sequence
#### 4.2.7 TC Message State  
This research proposes two conditions that will determine if TC messages should be emitted by an MPR; these are as follows:  
* The availability of symmetric two-hop neighbors between the MPR and its selectors.
* Changes in the OLSR addresses of the MPR selectors.
* Changes in the number of nodes that selected the MPR to advertise their links.
## 1 Introduction
Further, building a machine learning model for oil sheen detection requires a large dataset of visible image data taken right at the water surface. The objective of this study is to develop an image-based Oil Sheen Prediction Neural Network (OS-Net) and video-based real-time oil sheen prediction algorithm (VOS-Net) to automatically monitor oil sheen appearance in an oil sheen screening system (OS-SS). We first collected oil sheen image data in a lab-based test system at the distance and angle required for potential future field embodiments. The lab-based test system recorded videos of oil sheen formation from mobilizing embedded oil in sediments with injected air bubbles or injected water. We extracted images from these videos to develop an image-based OS-Net, a CNN based model to predict the existence of oil sheen on water surface images. To develop a powerful (deeper) and robust OS-Net, we employed skip connection in Residual Blocks [23] to address the vanishing gradient problem in deep neural networks. We evaluated different transfer learning strategies to further improve the performance of OS-Net. Finally, we extended the OS-Net to monitor real-time video of oil sheen formation (VOS-Net) to automatically detect the oil sheen in real time in the OS-SS. The oil sheen prediction algorithm used in this paper is described and experimental results as well as the model performance are discussed. Finally, the advantages and disadvantages of the current model, possible ways to improve the robustness of the algorithm, and other potential applications of this algorithm are described. Our key contributions are: (1) development of OS-Net, a deep convolutional neural network using residual blocks as basic elements and transfer learning to achieve a high test accuracy in oil sheen prediction; (2) exploration of the effectiveness of different strategies of transfer learning, which can provide guidance to other similar limited data tasks; (3) development of the video-based VOS-Net that combines domain knowledge with the machine learning algorithm to improve performance and achieve real-time oil sheen detection from video of transient sheen formation on the water surface; and (4) creation of a new dataset with thousands of images and videos of close-proximity visible light images of oil sheen, which provides data for others to develop close-proximity oil sheen detection models.