esa-sceva/satcom-chunk-collection huggingface dataset:
# A Middleware Solution for Wireless IoT Applications in Sparse Smart Cities
## 3 The RAMP Middleware for Wireless IoT Applications
RAMP nodes cooperate at the middleware layer to dispatch packets, with no need to modify routing tables at the operating-system level, thus achieving the degree of dynamicity needed in sparse Smart City scenarios [2; 3; 36];
* it should efficiently manage network failures and partitioning that have to be considered as a frequent event in the sparse Smart City scenarios. To this purpose, we have significantly extended our RAMP middleware by designing and implementing the RAMP Opportunistic Networking (RON) component, transparently supporting the efficient management of packets in case of the destination node is temporarily unreachable. In case of packet dispatching failure, RON persists packets and then automatically retries to transmit them to the destination in case of link connectivity modifications (even duplicating packets to maximize packet delivery probabilities), that is, since a node moved from one location to another providing access to new IP subnets or the Single-hop Connectivity Layer has modified the configuration of a network interface. API at the Multi-hop Networking Overlay Layer can allow to tune the behavior of nodes to achieve a proper tradeoff among resource sharing to support packet management and resource preservation to avoid too much overhead (additional details on our implementation in Section 5). Finally, the Coordination Wireless IoT Application Layer provides tools to simplify the development of sensor and controller nodes. This layer hides the complexity of node coordination, such as by enabling the transparent dispatching of commands and updated software components (additional insights in Sections 6 and 7 respectively) among sensors and coordinators related to the same Wireless IoT Application. At the same time, it supports the tuning of packet management to achieve a proper tradeoff among packet delivery probabilities and cost of packet management. For instance, when sending a message, sensor nodes can specify a proper expiry value after which messages should be discarded even if not yet correctly delivered to the destination by the Multi-hop Networking Overlay Layer. From an architectural point of view, it exploits primitives provided by the layer below to support the dispatching of messages with end-to-end visibility by adopting four different semantics:  
* one-to-one, to send packets from a sender to a given receiver, for example, to send a document via a file sharing application;
* one-to-any, to send packets from a sender to any node of a set of receivers, for example, to send sensed data to one of the available sinks in a sparse Smart City environment;
* one-to-many, to send packets from a sender to every node of a set of receivers, for example, to send a message to a group of a chat application;one-to-all, to send a packet to every node of the network, for example, an alert message in case of fire hazard.
# A Construction Method for a Dynamic Weighted Protein Network Using Multi-Level Embedding
## 2 Dynamic Weighted Protein Network Construction  
Figure 1 shows the critical workflow of DWPNMLE, which consists of three steps: adjacency matrix construction, feature matrix construction, and dynamic weighted network generation through multi-level embedding.  
Adjacency matrix construction. Firstly, the protein activity cycles are computed using a dynamic threshold function. Then, the interactions between different proteins are measured by the connection probability (CP), and if it exceeds the threshold, an edge is added. Finally, the resulting collection of protein subnetworks at different time points is represented by the adjacency matrices \(A=\{A_{1},A_{2},\ldots,A_{n}\}\).  
Feature matrix construction. After the first step is completed and the adjacency matrices composed of multiple protein subnetworks are obtained, each protein within the subnetworks is annotated using GO Slim information, followed by conducting one-hot encoding. This process constructs multiple feature matrices \(X=\{X_{1},X_{2},\ldots,X_{n}\}\) that represent the GO Slim annotations of the protein subnetworks.  
Dynamic weighted network generation by multi-level embedding. Each adjacency and feature matrix is input into VGAE for the first embedding. After learning the low-dimensional representations of the nodes, a node attribute matrix collection \(Z=\{Z_{1},Z_{2},\ldots,Z_{n}\}\) is generated to represent the node attribute information in each protein subnetwork. Then, each attribute and adjacency matrix are input into DNAE for the second embedding, generating multiple low-dimensional embeddings \(B_{0},B_{1},\ldots,B_{n}\). These embeddings are concatenated in series to construct a weighted PPIN by cosine similarity.  
Figure 1: Process of the DWPNMLE method.
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Operation of Wind Turbines
#### 3.2.5 Integration with Smart Grids  
Artificial intelligence plays a very important role in the integration of wind farms into smart grids, where the stability and real-time control of the grid are key aspects [65]. The application of artificial intelligence can enable the processing of a huge amount of data from sensors and IoT devices in real time, allowing continuous monitoring of the grid status and immediate adjustment to maintain stability. For example, artificial intelligence can recognize fluctuations in electricity supply and demand and adjust the output of wind turbines accordingly [66].  
AI can also predict potential instabilities in the grid before they occur. By analyzing historical data and current grid conditions, AI models can predict situations that could lead to instability, such as a sudden drop in wind speed or an unexpected increase in electricity demand. This capability gives grid operators the ability to take preventative measures in time to mitigate or avoid instability problems. Although traditional control systems can also deal with the variability and fluctuations of wind energy, control systems based on artificial intelligence can do so more efficiently, especially with a greater penetration of wind farms in the power grid [67].  
Another important opportunity presented by the use of AI is the rapid detection and isolation of faults within the grid to prevent cascading failures that could lead to widespread outages. For example, if AI predicts that a transformer is likely to fail, operators can request maintenance before the failure occurs, preventing outages and reducing maintenance costs.  
Another useful application of AI is optimizing the use of energy storage systems to balance the grid. When wind power production is high, surplus energy can be stored in energy storage systems (e.g., batteries, hydrogen or reversible hydropower plants) [68]. In times when no wind energy is generated (too little or too strong wind), AI can activate the use of stored energy from energy storage systems to maintain a stable power supply [66].  
By providing insights and recommendations based on real-time data and predictive analyses, AI helps operators make decisions to maintain grid stability [67].  
However, it should be noted that the complexity of artificial intelligence algorithms requires the creation of a clear legal framework to ensure that these technologies are used fairly and transparently and that they are not abused.  
Economically, AI improves operational efficiency and helps to reduce costs for energy suppliers and consumers by optimizing energy generation, distribution and use [69], which should lead to less price volatility and potentially lower electricity prices.  
In terms of environmental impact, artificial intelligence contributes to achieving climate goals by improving the integration and utilization of renewable energy sources, leading to a reduction in greenhouse gas emissions.
# Feasibility Test and Analysis for Heavy Haul Freight Using WDPS Based on MIMO-OFDM
## 2 System Configuration
### WDPS Configuration for Distributed Control of Heavy Haul Freight
#### 2.3.2 WDPS Protocol  
The master locomotive and the slave locomotive must communicate the control command information of the vehicle as shown in the Table 1 through the CCU. Therefore, WDPS requires an electrical interface with the CCU, braking unit, and master controller, and newly defined communication protocols between the CCU and the braking unit. In this paper, only the information types necessary for the operation are described.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 4 Hardware Implementation  
Based on the method proposed in Section 3, this paper designs a hardware architecture for an efficient data exchange engine on-chip in an SAR imaging processor. The architecture adopts the control mode of modular register addressing. Through the combination of superscalar pipeline buffer module and dual-channel DDR3 access control module, the frequent exchange of massive data in the real-time processing of SAR imaging is optimized and the real-time processing efficiency is improved.
# Aging-Friendly Design Research: Knowledge Graph Construction for Elderly Advantage Applications
## 4 \"Elderly Advantages Knowledge Graph\" Construction
### Elderly Advantages and Design Application Case Studies
#### 4.1.3 Demand Data Processing
practical for discrete data. Therefore, we employed the arithmetic mean method to calculate the weight vector. The judgment matrix was column-normalized as follows:  
\[\overline{a}_{ij}=a_{ij}/\sum_{i=1}^{n}a_{ij}(i,j=1,2,\ldots,n)= \begin{pmatrix}0. 0833&0.0833&0.0909&0.0667\\ 0.5000&0.5000&0.5455&0.4000\\ 0.2500&0.2500&0.2727&0.4000\\ 0.1667&0.1667&0.0909&0.1333\end{pmatrix}  \tag{3}\]  
The rows of the column-normalized judgment matrices were summed as follows:  
\[\overline{W_{i}}=\sum_{j=1}^{n}\overline{a}_{ij}(j=1,2,\ldots,n)= \begin{pmatrix} 0.3242&1.9455&1.1727&0.5576\end{pmatrix} ^{T} \tag{4}\]  
The weights of the needs were calculated:  
\[W_{i}=\overline{W_{i}}/\sum_{i=1}^{n}\overline{w}_{i}(i=1,2,\ldots,n)= \begin{pmatrix} 0.0811&0.4864&0.2932&0.1394\end{pmatrix} ^{T} \tag{5}\]  
The largest eigenvalue was calculated:  
\[\lambda_{\max}=\sum_{i=1}^{n}\frac{(AW)_{i}}{nw_{i}}=\frac{1}{4}\times \begin{pmatrix} 0.3295&1.9773\\ 0.0811&0.4864&1.1977\\ \end{pmatrix} =0.5614\\ \tag{6}\]  
The consistency was verified:  
\[CI=(\lambda_{\max}-n)/(n-1)=(4.0608-4)/(4-1)=0.0203 \tag{7}\]  
\[CR=CI/RI=0.0203\div 0.89=0.023 \tag{8}\]  
The CI value of this fourth-order judgment matrix was determined to be 0.0203, while the RI value of the fourth-order matrix was found to be 0.890 in the table. The CR value was calculated as 0.023, which is less than 0.1 according to Formula (8), indicating that the judgment matrix passed the consistency test. Following the above steps, the weights of the needs in the sub-criteria layer were calculated, the weight values of all judgment matrices were derived, and the comprehensive weights of each need in the sub-criteria layer were computed. The specific results are presented in Table 6.  
Needs with a combined weight greater than 0.05 were defined as core needs, while those between 0.01 and 0.05 were defined as general needs, and needs below 0.01 were excluded due to their low importance. * Data collation  
The above data we organized into the SPO triple format and stored in an Excel file. Example data content include <User Needs, Contains, Provide Software Instruction> and <Provide Software Instruction, Attribute: Core Needs, Attribute Value: 0.3367>.
## 2 Syntchitic Aperture Radar  
A synthetic aperture radar (SAR) is a pulsed radar installed on board a space, air, or ground platform. It transmits chirp signals or electromagnetic waves with linear frequency modulation, obtaining a raw or unprocessed signal from the surface of interest through antennas, demodulators, filters, analog-to-digital converters, that is, through the signal conditioning system.
# Assessment of Heavy Metal Distributions in Sand Beaches in the Maltese Islands
## 3 Results and Discussion
### Environmental and Health Risk Assessment  
#### 3.2.1 Health Risk Assessment  
Hazard Quotient (HQ), Hazard Index (HI), Cancer Risk (CR), and Risk Index (RI) values computed for the analysed data are tabulated in Table 10. Non-carcinogenic risks represented by HQ values calculated for adults by ingestion and dermal contact proved not to cause risk to human health, as these lie in the range \(<0.1\). Similar HQ values computed for children are higher than those calculated for adults but still fall under the no risk range. The Hazard Index for adults based on the non-carcinogenic hazard quotient values show a negligible effect for adults with HI = 0.018, and negligible non-carcinogenic effects for children as the resulting HI = 0.074 \(<0.1\). Distributions for the HI for children across the days analysed are illustrated in Figure 4. Similar results were noted for adults. The highest HI were recorded in Ballut Reserve Bay for Cu and Pb, in Marsalform Bay for Mn, and in Riviera Bay for Zn, while approximately equal HI values were reported for Fe across all bays. Different reasons account for such results; tying in with the results obtained from the statistical analysis. Some of the bays that have reported high effects of these heavy metals are located close to industrial zones, while other sources could be more typical of the origin of the rocks. CR values could only be worked out for Pb and Sr. with the results indicating a very low cancer risk for adults since RI for adults and children \(<\)10\({}^{-6}\). This is non-detrimental to the environment and society but is a result which must be further analysed to be able to suggest actions to contain and upkeep such contamination levels on local beaches. Containment actions and awareness of the sources of such pollutants is important to address toxicity on such sand beaches.
## 3 The Proposed DSCA-MC-CR Technique
### Model 2  
Model 2 assumes that the proposed SUs system has a different communication application from the existing privileged PUs system.  
In this model topology, which is designed using OMNeT++ and shown in Figure 3(a), the proposal suggests one fixed PU-TX tower and one fixed or moving PU-RX with channel  
Figure 3: Topology and spectrum representation of model 1. (**a**) Model 1 topology of the proposed DSCA-MC-CR using OMNeT. (**b**) Spectrum representation of model 1.  
gain \(\alpha_{u1}\). In addition, the proposal suggests one fixed SU-TX that is located within the coverage area of the PU-TX for the learning process and seven fixed or moving SU-RXs with channel gain \(\alpha_{uj}\), where \(j\in\{1,2\ldots,7\}\).  
The spectrum representation of model 2 is shown in Figure 4, which shows that the PU-RF signal occupies the assigned legal wide band, shown in blue. On the other hand, the SUs base-band signals (i.e., \(SU_{1}\) to \(SU_{7}\)) are shown in yellow. These signals spread in the RF mode due to the orthogonal codes within the encoder process. The SU-RF underlay signals do not affect the PU-RF wide-band signal due to the orthogonality between them.
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 4 Experimental Results and Analysis  
In this paper, the proposed method was applied to two datasets. One was the vibration dataset provided by the Bearing Data Center of Case Western Reserve University (CWRU) [32], and the other was the vibration dataset which was obtained by the CUT-2 platform. Based on both datasets, the proposed method was compared with 3 traditional feature selection methods, which are filter based on variance, wrapper based on recursive feature elimination (RFE), and embedded based on gradient boosting decision tree (GBDT). Performances of these methods were evaluated by the diagnosis accuracy on two popular fault classification models, which are Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).  
The features were calculated both in the time domain and frequency domain. All the features are shown in Table 1.
## 4 Discussion
### Analysis of the DL-Based Segmentation Network  
The segmentation models presented in this work set a baseline performance standard as there have not been any efforts to segment the myocardium in tagged CMR images to the best of our knowledge. Above that, our top-performing model is almost on par with state-of-the-art myocardium segmentation in _cine_ images despite having worse contrast and less distinguishable structures. The winners of the M&Ms challenge present an average DSC of 0.839 for ED slices.  
For the pretraining strategy, we observe that using images close to the domain of the final application improves performance. Additionally, transfer learning vastly improves convergence and thus training efficiency.  
While more commonly used in medical image segmentation, the nnUnet architecture is outperformed by the smaller and more regularized ResNetVAE. This result is likely due to an excess in capacity leading to overfitting the nnUnet on the training dataset. Moreover, qualitative analysis shows that the ResNetVAE architecture produces more anatomically accurate results. A reasonable explanation is that the regularization of the latent space for that architecture better incorporates anatomical constraints upstream of the decoding step.  
As tag lines fade during acquisition, the image contrast varies, making the cardiac structures more or less distinguishable from their surroundings. Our results show that all models perform best between time frames four to seven out of twenty-five. Improvements are needed if full-cycle motion tracking is to be achieved.  
While the difference is relatively small, the trained segmentation models perform better on healthy subjects compared to DMD patients. These patients often have trouble holding their breath during acquisition and their myocardium both contracts and appears differently, which seems to make the segmentation task more difficult. Nevertheless, the models trained in this work seem to generalize across different cardiac diseases. This observation emphasizes the importance of the inclusion of multiple diseases and conditions in the datasets used for training in DL-based CMR applications.
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 2 Isolated Power System Modeling
### Grid-Forming Operation of Power Converters
#### 2.1.1 Virtual Synchronous Machine Control  
VSM control (represented by the \"VSM Control\" block depicted in Figure 1) corresponds to a control feature that intends to emulate the main dynamics of a SM, which is represented in a simplified manner by the swing equation. Therefore, the grid-forming voltage angle/angular frequency is synthetized based on the virtual SM swing equation. Assuming that the angular frequency can be approximated to its nominal value, the corresponding mathematical formulation (in per unit) for the swing equation is given by  
\[2H.\frac{d\Delta\omega(t)}{dt}\ =\ Pm(t)-Pe(t)-D\Delta\omega(t) \tag{1}\]  
where \(\omega\) is the angular velocity of the virtual rotor, H corresponds to the inertia constant, \(D\) is the damping coefficient and \(Pm\) and \(Pe\) are the mechanical and electric output powers, respectively. A block diagram illustrating VSM control is presented in Figure 2, where \(P\) corresponds to the active power measured at the converter point of common coupling (equivalent to \(Pe\)), \(P_{ref}\) corresponds to the active power reference setpoint and \(P_{FR}\) corresponds to the active power modulation given by the virtual frequency governor (described in Section 2.1.2). Note that \(Pm\) is equivalent to the sum of \(P_{ref}\) and \(P_{FR}\). \(\omega\) and \(\theta\) are the outputs of the controller and correspond to the angular frequency and phase angle synthetized by the converter control mechanism, i.e., the frequency and phase calculated by the VSM control loop, where \(\omega_{ref}\) is the reference value of the virtual angular frequency.  
Figure 1: Schematic view of the battery energy storage system (BESS) converter control.
# Controlled Drainage Effectiveness in Reducing Nutrient Outflow in Light of Climate Changes
## 3 Results  
### Quality of Model and Data  
For the configured DRAINMOD model, the obtained calibration and validation process RMSE values were 0.054 m and 0.069 m, while, for CRM, they were 2.1% and 2.9%, respectively. The d and EF values for calibration were 0.960 and 0.961; for validation, the value was 0.947. The indicated model quality characteristics obtained for both calibration and validation indicate very high agreement between measured and modeled GWT. The prepared DRAINMOD model can be used to simulate the impact of different CD scenarios for the projected climate change on the dynamics of the GWT and drainage outflow of a drainage facility.  
Using daily historically measured and predicted precipitation data ranked from lowest to highest, a data fit analysis was performed for the selected models. Figure 5 shows the basic quality statistics for selected GFDL, MPI, and UKESM models of SD < 1, RMSE < 0.5, and r > 0.95.
## 2 Literature Review and Methodology
### Traditional Estimation Methods  
One traditional method to calculate SOC and SOH is the open-circuit voltage (OCV) method, which compares the battery's open-circuit voltage with a known OCV vs. SOC graph. However, the OCV method can be challenging to use for real-time application as it requires the battery to be rested for stabilisation before the method can be applied to it, which is not applicable as the Li-ion battery is a non-linear system which is prone to instability [11].  
The coulomb counting (CC) estimates the SOC by integrating battery current over time. For example, 94 Ah corresponds to 100% SOC, 47 Ah to 50%, 23.5 Ah to 25%, and 0 Ah to 0% [12]. The CC method estimates the relative SOC by tracking current flow in and out of the battery, offering real-time and accurate calculations. However, it requires a known initial SOC and can accumulate errors from inaccurate current measurements, which can be mitigated with high-accuracy sensors, though this increases costs. Implementation of the CC method consists of using Equation (3), which consists of the sum of the previous known battery's SOC level, the battery's rated capacity (\(C_{rated}\)), and the integration of the measured battery current (\(I_{b}\)) which is entering or leaving the battery over a given time to calculate changes in the SOC level of the battery [13].  
\[\text{SOC}=\text{SOC}(\text{t}_{0})+\frac{1}{C_{\text{rated}}}\int_{0}^{ \text{t}}\text{I}_{\text{b}}\;\text{dt} \tag{3}\]  
The internal resistance (IR) method consists of measuring the internal resistance of an Li-ion battery. The experiment consists of using a known value resistor, like a 10 ohms  
Figure 1: Equivalent Circuit Model of a Battery.  
resistor that has 5-watt power consumption [14]. Then, measure the no-load voltage of the battery cell using a multi-meter. Then, measure the load voltage of the battery with the 10 ohms resistor connected. Then, measure the voltage drop between the no-load and load voltages of the battery to find the current internal resistance of battery. Issues can include unwanted extra resistance from wires used or the resistor being warmed up, which can affect the accuracy of measured voltage drop [14]. Equation (4) shows that the battery voltage equals the no-load voltage multiplied by the load resistor (R) and total resistance. Using this principle, the internal resistance (r), a component of the total resistance, can be calculated by rearranging the Equation [15].  
\[\mathrm{V_{loaded}}=\mathrm{V_{no\_load}}\left(\frac{\mathrm{R}}{\mathrm{R}+ \mathrm{r}}\right) \tag{4}\]  
It is essential to find the internal resistance (\(R_{b,k}\)) of the battery to estimate the SOH, which is expressed in Equation (5). It compares the known resistance, such as the end-of-life resistance (\(R_{EoL}\)) and the initial internal resistance (\(R_{fresh}\)), with the measured battery's internal resistance (\(R_{b,k}\)) to obtain the SOH of the battery [16].  
\[\mathrm{SOH_{b,k}}=\frac{\mathrm{R_{EoL}-R_{b,k}}}{\mathrm{R_{EoL}-R_{fresh}}} \tag{5}\]