esa-sceva/satcom-chunk-collection huggingface dataset:
# Clustering of Heart Failure Phenotypes in Johannesburg Using Unsupervised Machine Learning
## 1 Introduction  
Unsupervised machine learning algorithms autonomously learn patterns in a dataset without human guidance or input. For example, clustering is an unsupervised machine learning technique that groups data points with similar attributes. A wide spectrum of algorithms are used to cluster data using dimension reduction, partitioning, hierarchical, density-based, and model-based methods [1]. Unsupervised machine algorithms may reveal risk factors in patients with complex clinical syndromes such as heart failure, a clinical syndrome characterised by a constellation of symptoms and signs of fluid overload, mainly in patients with underlying cardiovascular disease. The global prevalence and incidence of heart failure continue to rise to the extent that heart failure has been declared a global pandemic [2]. Therefore, innovative strategies that may assist in improving the management of heart failure patients should be sought.  
The current approach to the diagnosis, risk stratification, management, and prognosis of heart failure is fundamentally determined by a single imaging parameter, the left ventricular ejection fraction (LVEF) [3]. The LVEF is a measurement that estimates the fraction of blood volume ejected by the heart's left ventricle with each contraction [4]. Among a wide range of non-invasive cardiac imaging modalities, the echocardiogram is one of the widely applied techniques for measuring the LVEF. However, the results obtained are generally influenced by the experience of the echocardiogram operator and the patient's body habitus. In addition, the echocardiography machine is not readily accessible in most hospitals in low-and-middle-income countries (LMICs). Therefore, its use is mainly restricted to the private sector and state-owned hospitals affiliated with academic institutions.  
A recent systematic review identified 81 articles reporting the utility of both supervised and unsupervised machine learning algorithms in heart failure. Of the articles reviewed, 11 (13%) studies used classification or clustering algorithms, and 25 (31%) used machine learning for the detection of the onset of heart failure, 18 (21%) for predicting rehospitalisation, 20 (26%) for predicting mortality, and the rest (6%) for predicting outcomes and response to implantable cardiac devices [5]. Another systematic review identified 27 studies using unsupervised machine learning for heart failure subtype definition [6]. However, there are limited or no papers published from LMICs describing heart failure phenotypes using machine learning algorithms. This study aims to describe baseline demographic and clinical characteristics of heart failure patients hospitalised in a tertiary-level hospital in Johannesburg, South Africa. Furthermore, this study describes the utility of unsupervised machine learning in clustering heart failure patients using a constellation of clinical parameters, instead of a single imaging parameter, the LVEF.
## 3 CFD Modelling as Digital Twin
### VoF Equations  
To accurately represent the interaction between air and water, a bidirectional coupling approach, such as the Homogeneous Volume of Fluid (HVoF) method, is commonly employed in multiphase flow modelling. This method effectively captures the interaction between the two distinct phases (air and water). It allows for the calculation of mass concentrations of air and water in each computational cell within the geometric domain as a composite fluid (mixture of air and water). This approach has allowed for the detailed visualisation of the separation between air pockets and the water phase in models developed by many researchers. Most CFD models base the analysis of two-phase flows on an interface capture, which is the principle of HVoF [38]. This method employs a phase fraction coefficient, which quantifies the proportion of water within each finite cell or volume in a CFD model. The coefficient ranges from 0 to 1, where \(\alpha_{w}=1.0\) indicates a cell fully occupied by water, while \(\alpha_{w}=0.0\) signifies a cell filled with air. Intermediate values represent a mixture of both phases within the cell. Figure 4 shows an example of the distribution of phase fraction values in a moving two-phase flow.  
Based on the phase fraction information (\(\alpha_{w}\)), the value of the mixed density (\(\rho_{m}\)) and mixed dynamic viscosity (\(\mu_{m}\)) are calculated for each cell or finite volume using the phase fraction, as shown in Equations (1) and (2).  
\[\rho_{m}=\rho_{w}\alpha_{w}+\rho_{a}(1-\alpha_{w}) \tag{1}\]  
\[\mu_{m}=\mu_{w}\alpha_{w}+\mu_{a}(1-\alpha_{w}) \tag{2}\]  
Figure 4: Phase fraction scheme in a CFD mesh.  
Phase fraction values \(\alpha_{w}\) change as a function of time and space in each cell so that the spatiotemporal variation of values of \(\alpha_{w}\) is defined by the molecule transport equation for the HVoF method, which is shown in Equation (3).  
\[\frac{\partial\alpha_{w}}{\partial t}+\
abla\cdot(\alpha_{w}\mathbf{u})+\
abla \cdot((1-\alpha_{w})\alpha_{w}u_{r})=0 \tag{3}\]  
where \(u_{r}\) corresponds to the relative velocity between two phases (air-water), which is physically represented as the difference between the individual velocities of each phase at a specific point in the geometric domain of a CFD model. The mixed density and dynamic viscosity principle solves a single set of fluid equations for the CFD model, replacing \(\rho_{m}\) and \(\mu_{m}\) in Equations (4) and (5). The molecule transport equation allows for the analysis of the discrete gas phase (bubbles) and the water phase, depending on the value of \(\alpha_{w}\).
## 4 Experimental Results
### Electrical Energy Consumption Profiles  
Electrical consumption was measured using an energy meter in a 24 h cycle. In the single air curtain case, energy consumption was 30.9 kWh, and in the case of the two-layer air curtain, it was 25.2 kWh per 24 h. The difference can be seen in the graphs presented in Figures 15 and 16. It can be seen from the graph obtained for the single air curtain (Figure 15) that the compressor works with a short off-cycle, but the compressor works almost continuously. The stops
## 1 Introduction
Current methods tried to analyze changes in spectral response, spectral fractions and indices to separate degraded and intact forests from remote sensing [22]. Classification approaches, such as decision tree [19], machine learning (random forest, support vector machine) [3; 23; 24] have been developed to map the forest degradation level in recent years. But the impact of climate to forest degradation was not considered in the classification approaches in most studies, which was also one of the reasons leading to forest degradation [15; 17]. Therefore, developing an applicable method which takes into account the influence of climate may increase the precision in forest degradation detecting from remote sensing. The aims of this paper are (i) to set up the indicator system to estimate the forest degradation and to analyze the time series of the indicators from MODIS and Landsat images, (ii) to develop a method from multiple indicators to detect the forest degradation in Three-North Forest Shelterbelt in China, (iii) to validate the results by using the field data and the high-resolution Landsat ETM+ images. This paper would provide a quick and applicable approach for monitoring regional forest degradation.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
###### Abstract  
Wireless networks, such as mobile ad hoc networks (MANETs) or wireless sensor networks (WSNs), usually suffer link failures and topology changes, due to the mobility of wireless nodes, the insufficient power of wireless nodes, the unstable state of wireless links, and unpredictable environmental interference. If any link or node failure occurs, a lack of fault-tolerant mechanisms may lead to the interruption of active communication between the source and destination nodes. Multipath routing protocols have been proposed to solve the problem. In this paper, we propose a novel multipath routing approach called node-disjoint multipath routing (NDMR) for discovering and establishing two node-disjoint paths between a source and a destination in a wireless network. The proposed approach uses the request-reply mechanism to find the node-disjoint paths. However, it discards duplicates of request messages instead of re-broadcasting them as occurs in several existing approaches. Additionally, NDMR uses a two-way handshake or three-way handshake process for discovering the node-disjoint paths. If the destination can find two node-disjoint paths in the first route discovery, it uses the two-way handshake process. Otherwise, it applies the three-way handshake process. By decreasing the number of steps in the route discovery process, NDMR reduces the route discovery time achieved by existing techniques. These advantages make NDMR very useful for applications that demand high availability and fault tolerance in MANETs and WSNs. The performance of NDMR has been analyzed, evaluated, and compared with that of several existing approaches. Various simulations were conducted to validate the analyzed results. The analyzed and simulated results show that NDMR significantly reduces the control overhead as well as the time required, by existing approaches, for route discovery.  
wireless sensor networks; ad hoc wireless networks; node-disjoint multipath routing; smart communication algorithm; fault-tolerant communication +
Footnote †: journal: Accepted: 20 March 2018; Published: 22 March 2018
## 4 Routing Protocols Using Ant-Like Mobile Agents
### Multi-Agents Based Models  
Using ant-like agents for route discovery is not very effective for MANETs. In order to cope with the frequent changes of the network topology, a large amount of control data must be exchanged as ant-like agents. On the other hand, multi-agents based routing protocols have been proposed [8; 11; 12]. Ant-like agents behave not only as explorers for the route finding, but also as messengers to bring actual message data. They actively update the routing tables in the nodes as they determine the next nodes to visit. Agents can react autonomously in the face of changing circumstances. They are more adaptable, and therefore they are more suitable for MANETs.  
[PERSON] _et al._ proposed a dynamic routing protocol using mobile agents [8]. Each node has a routing table that stores route information for every destination. As in AntNet and AntHocNet, ant-like agents, called _routing agents_ are dispatched to explore the network. The node that a _routing agent_ visits next is selected at the equivalent probability. The _routing agent_ brings its own history of movement and updates the routing table of the node it is visiting. When a predefined wandering time has expired, then the _routing agent_ disappears. When a source node wants to send a message to its destination, the node dispatches a series of _message agents_ that are corresponding data packets. _Message agents_ also determine their next hop autonomously by looking up the routing table at each node. They repeat these looking-up and hopping actions until they reach their destinations. When they reach their destination, they disappear.  
[PERSON] _et al._ improved this model so that the _routing agent_ also brings the time required to travel from the destination to the current node and the number of hops [47]. This history is also stored on the routing table. [PERSON] _et al._ further extended this model so that the network does not lose its effectiveness when the number of nodes changes [48]. [PERSON] _et al._ still further extended this model so that the network can keep its effectiveness in the face of congestion [49]. The effectiveness is achieved by the _routing agents_. They collect and distribute information about network congestion and update the routing table at each node they visit. _Message agents_ move from one node to another node based on this information. The _message agent_ has an evaluation function to select a best route.
## 4 Application
### Visualization: Images and Convolutional Layer
The table summarizes the benchmark performance as well as the prediction performance of the proposed method. All results are measured in area under the curve (AUC). Multiclass AUC values are the average of the AUC values from the 4 different classes, which were calculated using a statistical software package named pROC, which requires the same computation of sensitivity and specificity (these definitions are discussed in Equations (19) and (20)). The average prediction performance for 4-class diagnosis is 89% with 26 million parameters in a variety of different neural networks designs. The average prediction performance for 4-class diagnosis is 97.5% with only 13,000 parameters in the proposed network architecture. \(X^{t}\) based on Equation (6)) in the 1 st Conv. Layer (sized 61 by 61). For COVID-19 patients, the lung area is cloudy and unclear, whereas for the healthy cases, it is clearly visible. This is not a surprising coincidence because the proposed new variable modules, \(X^{t}\), are engineered using Equation (6), which relies on the response variable \(g_{j}\) in the training set. The 61 by 61 images from the proposed algorithm are a direct translation of not only the original pixels but also the response variable. In other words, this visualization presents how the I-score considers image data. Figure 9: A summary of randomly sampled images from the COVID and non-COVID classes (10 each). Panel **A** represents COVID patients and panel B represents non-COVID individuals. The first row plots the original 128 by 128 images. The 1 st Conv. Layer generates \(61\times 61=3721\) new variables. We plot the same 10 images from both classes using these 3721 variables in the second row. We also provide the predicted COVID probabilities on top left corner of each image. The 2 nd Conv. Layer generates \(30\times 30=900\) variables. We plot the same 10 images from both classes using these 900 variables in the third row. We also provide the predicted COVID probabilities in the top left corner of each image assuming only these 900 variables are used as predictors. The plot of the original images for patients infected by COVID-19 has grey and cloudy textures in the chest area, which are due to inflammatory fluid produced when patients exhibit pneumonia-like symptoms. This shaded area (as seen in panel **A**) prevents us from clearly observing the lungs. This is different in panel **B**, where the lung areas are dark and almost black, which means the lung is filled with air (i.e., normal cases). The black/white contrast in the two panels is directly related to the amount of much inflammatory fluid in human lungs, which translates to greyscale on pictures. The same contrast can be seen using the new variables (these are \(X^{t}\) based on Equation (6)) in the 1 st Conv. Layer (sized 61 by 61). For COVID-19 patients, the lung area is cloudy and unclear, whereas for the healthy cases, it is clearly visible.
# DDRCN: Deep Deterministic Policy Gradient Recommendation Framework Fused with Deep Cross Networks
## 2 Related Work
### Recommendation Methods with Reinforcement Learning  
Traditional and deep learning-based recommendation frameworks rely on historical data to capture user preferences, but the actual recommendation is a continuous and dynamic decision-making process. Reinforcement learning simulates the ongoing \"exploration-exploitation\" process between the agent (recommendation system) and the environment (user), and finds a balance to obtain the recommendation process with the maximum cumulative benefit. Common reinforcement learning recommendation methods include policy-based methods and value function-based methods.  
Value-based methods in reinforcement learning decide the recommended action by selecting the maximum Q value of the state. GoalRec [17] is a value-based deep reinforcement learning framework that addresses high-variability environments and uncertain reward Settings. FeedRec [18] is a recommendation framework that optimizes user engagement through reinforcement learning. The framework includes a Q-Network and an S-Network, and iteratively trains the two networks to optimize the indicators of user engagement. Policy-based recommendation methods find the optimal policy based on the current state, and outputs the probability distribution of each action. PDQ [19] is a recommendation method based on offline policy learning. The framework executes policy functions offline and introduces a simulation environment to help with policy improvement. OPS2 [20] is a two-stage off-policy gradient recommendation method, including candidate generation and ranking stages.  
The recommendation method based on the value function is unsuitable for handling continuous actions. In contrast, the recommendation method based on policy function cannot evaluate the goodness of the policy. The Actor-Critic network combines the advantages of value function and policy function methods. The dual network's prediction and value estimation of recommendation actions can deal with continuous recommendations and user behaviors. Based on this, we propose a deep deterministic policy gradient recommendation method based on the Actor-Critic framework. The technique can handle large-scale continuous behaviors, is more suitable for recommendation scenarios, and has a faster model convergence rate.
## 2 Operating Principle of the TENG  
The TENG used in this study operates in the Vertical Contact-Separation Mode and consists of one or two dielectric layers and electrodes, generating electricity by utilizing the triboelectric effect and electrostatic induction. In the case where there is only one dielectric layer, the dielectric layer is in direct contact with the electrode, leading to charge transfer. When there are two dielectric layers, charge transfer occurs through the contact between the dielectrics. The fundamental operation of devices with one dielectric layer and those with two dielectric layers is the same. Here, the operation is explained using an example with a single dielectric layer. The operation of the TENG in the Vertical Contact-Separation Mode is illustrated in Figure 1.  
When the dielectric layer and the electrode come into contact due to vibrations or other mechanical forces, charge transfer occurs, resulting in one surface becoming positively charged and the other becoming negatively charged. The quantity and polarity of the generated charges depend on the properties of the dielectric material. After contact, as the dielectric layer and the electrode separate, the opposing positive and negative charges create an electric field in the surrounding space. To neutralize the resulting potential difference, a current flows from one electrode to the other until the potentials of both electrodes equalize. When the dielectric layer and the electrode approach each other again, the potential difference induced by the charge pair decreases, causing the current to flow in the direction opposite to the separation phase. By repeating this process of contact and separation periodically, an alternating current is continuously generated [27].  
The equivalent circuit of the triboelectric nanogenerator is presented in Figure 2.  
The voltage arising from charging and the electrostatic capacitance between the electrodes are defined as \(V_{OC}(x(t))\) and \(C_{Teng}(x(t))\), respectively. Furthermore, \(V_{OC}(x(t))\) is expressed by Equation (1), and \(C_{Teng}(x(t))\) is given by Equation (2):  
\[V_{OC}(x(t)) =\frac{\sigma x(t)}{\varepsilon_{0}} \tag{1}\] \[C_{Teng}(x(t)) =\frac{S\varepsilon_{0}}{d_{0}+x(t)} \tag{2}\]  
Figure 1: Operation mechanism of the TENG and charge transfer.  
where \(\varepsilon_{0}\) represents the electric constant, and its value is \(8.854\times 10^{-12}\). \(d_{0}\) is the thickness of the dielectric layer, and its unit is mm. \(x(t)\) denotes the distance between the dielectric layers, and its unit is m. \(S\) is the area of the electrode, and its unit is m\({}^{2}\). \(\sigma\) is the surface charge density, and its unit is C/m\({}^{2}\).  
The terminal voltage \(V\) of the TENG is expressed by Equation (3):  
\[V=-\frac{Q}{C_{Teng}(x(t))}+V_{\text{OC}}(x(t)) \tag{3}\]  
where \(Q\) is the moving charge quantity, and its unit is C.
## 4 Concept Development
### Seamless Integration of User Identities and Data Security  
Executing toolchains with several users in different applications and services requires a comprehensive authentication that can be used in the client applications as well as for platform services. This authentication is conceived through generating a reusable signed access key (token) that can be used for secure authentication in different clients, which is why this method is also referred to as SSO [48]. The BIMSWARM SSO is mainly based on the established OAuth2 workflow [49] according to industry standard RFC 6749 [50] and is provided as an authentication service for all products registered on the platform. Because the token is compliant with JSON Web Tokens and RFC8725 [51], applications can decode the specified data record from the token payload. Identification and access management in toolchains and the data store are done using the encoded user ID. In client applications, the encoded user ID can either be mapped to existing users in the application or a new user can be created. The authenticity of the token can be validated using a public key infrastructure provided by the platform, thus creating a trusted environment between applications. In addition, all platform services accept the signed token and can thus provide API access to toolchains, openCDE-compliant data stores and visualization.
# Automatic Selective Encryption of DICOM Images
## 3 The Proposed Approach
### Approach I: Multi-Region Selective Encryption Approach for Mammography DICOM Images
#### 3.1.1 Encryption Cost Estimation  
In this section, the encryption time for different DICOM images sizes using different encryption algorithms (AES and XXTEA) is modeled and analyzed to extract equations that are used as pre-knowledge information to build a cost lookup table for different encryption algorithms, and it will be used to estimate the processing cost of encrypting parts (regions) of the DICOM images.  
A line-fitting tool in Python is used to fit linear equations for encryption time with different key lengths. For example, AES (256-bit) fitted equation is depicted in Figure 4, where its estimated equation is (Y = 1.05 \(\times\) 10\({}^{-8}\)X + 0.0002). In general, it can be noticed that processing time is linearly proportional to the image size, and as expected, large images require more time to be encrypted. Moreover, in comparison with AES processing time, the XXTEA is around two times faster than the AES (128-bit) and AES (256-bit), respectively. One last important factor is the hardware specification; this work is performed using an Ubuntu machine, with an Intel i5 processor and 4 GB RAM.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 4 Simulation Results and Comparative Analysis  
The simulation results of each of the current control techniques will be presented first, followed by a thorough comparative analysis. For analysis of each of the current control techniques, the \(d\)-axis current command was changed from 2 A-6 A at a simulation time of 0.05 s in all cases. This represents the change in reference current, which would be generated from the outer loop with the virtual inertia algorithm in a full implementation. The \(q\)-axis current was kept constant at zero through the simulation. The simulation results for each controller are described first, followed by sections on transient performance and harmonic performance comparison.