esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### VNF Placement and Virtual Network Embedding  
Slices are chains of VNFs running on logical/physical resources to meet the service requirements [23]. The essence of NS resource allocation is to determine a feasible path for the deployment of VNUs and virtual links onto network infrastructures. There are significant efforts in VNF placement and virtual network embedding [24; 25], which can be instructive and helpful in NS deployment.  
Virtual resources allocation can be divided into two sub-problems: virtual node mapping and virtual link mapping, which can be solved in an isolated way or a coordinated way [5]. By employing a graph neural network-based algorithm, [PERSON] et al. [26] propose a topology-aware VNF embedding method aiming at minimizing the resource consumption. To deal with the objective of jointly minimizing mapping cost and maximizing utilization of physical links, [PERSON] et al. [27] introduce scalable algorithms for VNF chaining and placement. [PERSON] et al. [28] study dynamic VNF placement algorithms and introduce an Elastic Virtual Network Function Placement (EVNFP) problem aiming at minimizing operational costs in providing VNF services. Although the topic of VNF placement and virtual network embedding algorithms are well-investigated in the context of SC deployment, the extension to improve diverse QoS of tenants in terms of throughput, latency or reliability is not explicit. The problem statement must not be limited to the optimization of traditional connectivity, resource utilization or provision cost of infrastructure resources.  
In this regard, [PERSON] et al. [29] consider the latency as a constraint and convert VNF placement and chaining model into a multi-constrained routing problem. [PERSON] et al. [30] guarantee the link propagation delay for the placement problem of VNF instances and optimize operating expense (node and link resource utilization level). Targeting to achieve network-level requirements as well, such as resource consumption, the consideration of services requirements in these works merely is limited to the constraint level, rather than determining the optimal-latency path for embedding VNFs.
# Blockchain Integration in the Era of Industrial Metaverse
## 5 Blockchain Integration in Metaverse
### Technical Framework for the Blockchain in Metaverse
#### 5.1.1 Data Acquisition  
Data acquisition is a significant process in the ecosystem of the metaverse. Whenever a user makes financial transactions, it is inevitable that sensitive user information, such as bank and credit card information, will be shared. Beyond the financial aspect, the users also share other types of sensitive information, which are prerequisites for the creation of their digital avatars, namely biometric data and hand gestures [60]. Furthermore, data acquisition contributes towards the training of AI and ML algorithms that can facilitate a variety of applications, such as the creation of digital products, the implementation of automated recommendation systems, the support of decision-making, and metaverse marketing [61]. As a result, enormous sets of heterogeneous data will be produced in the metaverse [62]. One of the tools used in the metaverse for data collection will be web forms filled out by the users. Bots will obtain users' social security numbers and other necessary authentication information, including personal information [63]. Additionally, a high-definition camera will gather data regarding the physical characteristics of the metaverse users. Finally, the utilization of XR devices will be advantageous to the users in regards to the exploration of the metaverse. The utilization of such devices will also be expanded for compiling data regarding user behavior [64].  
Challenges of Data Acquisition in the Metaverse  
The metaverse will produce vast amounts of unstructured, real-time data through decentralized applications. Acquiring the vast amounts of data being generated poses a difficult challenge. Building metaverse applications such as recommender systems will make data assurance or integrity crucial [65]. The quality of the data may also be impacted by the acquisition of duplicate and inaccurate data [66].  
Blockchain Contribution  
The adoption of blockchain technology will make it simpler for applications such as social networking to acquire real data in the metaverse. The distributed ledger of the  
Figure 14: Blockchain for technical aspects in metaverse.  
metaverse will make it possible to trace data in the metaverse and validate transaction records. Data acquisition is therefore resistant to attacks because the majority of nodes in the ledger must consent before any changes to the data in the metaverse can be made [67]. All gathered data in the metaverse are validated using a blockchain-specific process that is driven by consensus mechanisms [68]. The probability of producing a duplicate block is almost zero, ensuring that there will be no duplication during the data acquisition process. The data obtained through blockchain-enabled acquisition systems in the metaverse will be accurate because every block in the blockchain has been authorized [69; 70].
###### Abstract  
There is a growing interest in sustainable energy sources for energy demand growth of power industries. To align the demand and the consumption of electrical energy, thermal energy storage appears as an efficient method. In the summer days, by using a cold storage system like ice storage, peaks of the energy usage shift to low-load hours of midnights. Here, we investigate the charging process (namely solidification) numerically in an ice-on-coil thermal energy storage configuration, where ice is formed around the coil or tube to store the chilled energy. The considered ice storage system is a shell and tube configuration, with three kinds of tubes including a U-shaped tube, a coil tube with an inner return line, and a coil tube with an outer return line. Advanced 3D unsteady simulations are achieved to determine the effects of tube type and position of the ice storage (horizontal or vertical) on the solidification process. Results indicate that using a coil tube speeds up the ice formation, as compared with the simple U-shaped tube. The coil tube with an outer return line exhibits a better performance (more produced ice), as compared with the coil tube with an inner return line. After 16 h of solidification, the coil tube with the outer return line has about 1.057% and 1.32% lower liquid fraction in comparison with the coil tube with the inner return line and U-shaped tube, respectively, for both positions (vertical and horizontal).  
ice storage system; heat exchanger; numerical modeling; ice-on-coil tube; solidification +
Footnote †: journal: Applied Sciences
###### Abstract  
The exterior location of a user can be accurately determined using a global positioning system (GPS). However, accurately locating objects indoors poses challenges due to signal penetration limitations within buildings. In this study, an MLP with stochastic gradient descent (SGD) among artificial neural networks (ANNs) and signal strength indicator (RSSI) data received from a Zigbee sensor are used to estimate the indoor location of an object. Four fixed nodes (FNs) were placed at the corners of an unobstructed area measuring 3 m in both length and width. Within this designated space, mobile nodes (MNs) captured position data and received RSSI values from the nodes to establish a comprehensive database. To enhance the precision of our results, we used a data augmentation approach which effectively expanded the pool of selected cells. We also divided the area into sectors using an ANN to increase the estimation accuracy, focusing on selecting sectors that had measurements. To enhance both accuracy and computational speed in selecting coordinates, we used B-spline surface equations. This method, which is similar to using a lookup table, brought noticeable benefits: for indoor locations, the error margin decreased below the threshold of sensor hardware tolerance as the number of segmentation steps increased. By comparing our proposed deep learning methodology with the traditional fingerprinting technique that utilizes a progressive segmentation algorithm, we verified the accuracy and cost-effectiveness of our method. It is expected that this research will facilitate the development of practical indoor location-based services that can estimate accurate indoor locations with minimal data.  
Zigbee; RSSI; progressive subdivision algorithm; artificial neural network; B-spline equation
# Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels
## 4 Simulation Results and Experimental Verification  
### Simulation Results  
In order to verify the performance of the MATE algorithm in a complex HF channel environment, this paper designs simulation experiments based on the Watterson channel model. The transmitted signal is a set of randomly generated 48 integers ([0, 8)), modulated by 8 PSK, with a center frequency of 12.12 MHz and a bandwidth of 2.4 kHz, and the received signal sampling rate is 30 MHz.  
The multipath effect and Doppler shift were simulated in the experiments, with the number of paths set to 2 and a delay difference of 10 ms. To evaluate the robustness of the algorithms, 140 sets of HF data were randomly generated, with the signal-to-noise ratio set to 0 dB, and the maximum Doppler shift varied randomly between 0 Hz and 20 Hz. The delay of the first path was set to 0, and the power was randomly distributed between 0 dB and \(-\)4 dB; the delay of the second path varied randomly between 0.0001 s and 0.002 s, and the power was randomly distributed between \(-\)5 dB and \(-\)9 dB.  
Figure 6 illustrates the time- and frequency-domain comparison of the first set of transmitted and received signals. The transmitted signals (Figure 6a,c) have clear 8 PSK modulation characteristics, while the received signals (Figure 6b,d) exhibit obvious multipath fading and frequency-shift characteristics due to the processing of the Watterson channel model, verifying the complex interference effect of the HF channel on the signals.  
Figure 5: Block diagram of the loop filter.  
Figures 7 and \(8\) demonstrate the initialization results of the MATE algorithm. The acquisition results show that the method is able to initially estimate the carrier frequency of the multipath signal and the phase of the modulating signal to achieve the initialization of the parameters of the MATE algorithm.  
Figure 8: Corresponding values for acquisition frequency.  
Figure 6: Signal comparison; (**a**) transmitted signal time domain; (**b**) received signal time domain; (**c**) frequency domain of the transmitted signal; (**d**) frequency domain of the received signal.  
Figure 7: Acquisition results.  
Figures 9 and 10 show the delay estimation results and their error analysis of the MATE algorithm and the traditional generalized cross-correlation algorithm (GCC), respectively. The experimental results show that the average delay estimation error of the MATE algorithm represents a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. This result fully proves the superiority of the MATE algorithm in high-precision delay estimation in the complex HF channel environment and provides a more accurate and effective solution to the delay estimation problem in the field of HF communication.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 3 Preliminaries
### Swarm Learning  
As the state-of-the-art BCFL, discarding the centralized server, swarm learning (SL) shares local gradients through the swarm network, and independently builds models on local data at the swarm edge nodes. SL ensures the confidentiality, privacy, and security of sensitive data with the help of blockchain. Unlike clients in traditional FL, edge nodes not only perform training tasks, but also execute transactions. The workflow of SL is as follows. A new edge node is first registered to the smart contract (SM) pre-deployed on the blockchain. Then, the swarm edge nodes perform a new training round, including downloading the global model and performing localized training. And then, all swarm edge nodes commit the local gradients to the SM through the swarm network. Finally, the SM aggregates all local gradients to create an updated model. Suppose there are \(K\) parties \(\{P_{1},P_{2},\ldots,P_{K}\}\) that constitute a collaborative group, in which each party \(P_{k}\) holds its local dataset \(D_{k}\) (\(k=1,2,3,\ldots,K\)). \(D=\{D_{1},D_{2},\ldots,D_{K}\}\) denotes the joint dataset. Without disclosing their local sensitive data, \(K\) parties aim to cooperatively train a global general classifier \(f(\mathbf{x};\mathbf{w})\) from the complete dataset \(D\). As illustrated in Figure 1b, in \(t\)th iteration, the party \(P_{k}\) downloads the global model \(\mathbf{w}_{t-1}\) from the blockchain and trains the local model based on the local dataset \(D_{k}\). Equation (1) describes the objective function optimized by training:
# Vertical Correlation and Array Gain Analysis for Vertical Line Array in Deep Water
## 1 Introduction
Under the Gaussian White noise assumption in deep water, AG of the VLA in the range-independent and weak range-dependent waveguide disturbed by the random internal waves are estimated [15]. Moreover, the modelling method for the ambient noise field caused by the wind is proposed, the AG of the VLA in the non-isotropic noise field affected by wind-driven ambient noise is analyzed [16]. Some researches take SG and NG into account separately and discuss their impact on AG. With a lossy boundary, [PERSON] found that the SG is expressed as a superposition of normal modes and the NG is represented as a linear sum of plane waves. In this case, AG of a broadside VLA deployed in shallow sea channel was analyzed [17]. [PERSON] et al. discussed the spatial gain of the VLA laid in the reliable acoustic path and the direct-arrival zone, respectively. It also demonstrates that when the signal is in the reliable acoustic path, higher array gain can be obtained [18]. In this paper, based an experiment in the Deep South China Sea, the vertical correlation of the sound field in the direct-arrival zone and shadow zone are analyzed, and the correlation coefficient of the noise field is calculated. From the view of ray interference theory, we discuss the sound rays type and arrival structure to explain the mechanism of the vertical correlation of sound field. Finally, the SG and NG and AG are evaluated based on the correlation coefficients of the signal and noise, respectively. This paper is organized as follows. In Section 2, we introduce the fundamental theory. A simulation calculation is conducted to analyze the effects of correlation coefficient on spatial AG in Section 3. Section 4 discusses the sea trial results. Section 5 summarizes the conclusions.
## 5 Technology and Case Studies
### Building AAC Mortars
#### 5.2.2 Dry Mixes for Floor Application  
The AAC composition with \(\rm M_{b}\) = 1.2 was chosen for use in such mortars due to its quick strength gain and corrosion resistance. The required properties include high bond strength and low shrinkage and taking into account its work in thin layers [98; 99; 100]. A dry mix design was, % by mass: AAC with \(\rm M_{b}\) = 2.4-20.0, milled sand fr. <0.16-29.9, sand (fr. 0.16/0.63)--49.8, NaLST--0.2, Na-CMC--0.1.  
The properties of the resulting mortar were consistency (flow)--20.4 cm; water retention capacity--98.8%; consistency retention time--45 min; bond strength to the substrate--0.6 MPa; flexural/compressive strength--2.1/7.4, 2.9/13.1, 3.2/ 18.1, 5.1/21.4 after 1, 3, 7, and 28 days, respectively; shrinkage--1.9 mm/m (28 days).  
Shrinkage of the AAC mortar was regulated through its modification by admixtures of polyols and re-dispersible polymer powders without ester bonds. These admixtures, used in conjunction with polyhydrosiloxane, NaLST, and cellulose ether, reduced the shrinkage of the AAC mortar to norm-specified values (\(\leq\)2 mm/m).
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Responsive Web-Based Data Acquisition Platform  
Along with EWIN devices, an IoT Cloud Platform has been developed to manage data acquisition from the RiverCore node environment. This cloud platform allows users to set-up and monitors the incoming data of the multiple deployed sensors.  
The EWIN data acquisition environment is integrated by hardware devices, an MQTT Mosquito Broker [30], and a NoSQL document-based data structure.  
The EWIN dataloggers retrieve information from environmental variables and send it through a 2G/3G cellular network, using AT commands and the MQTT protocol, to a Linux server, which has an MQTT Mosquito Broker installed. This broker receives all the messages published to the topics, while a background Node.js script saves data using a MongoDB [31] document structure.  
While information is saved in a MongoDB database, it can be retrieved using an online monitoring web platform, which listens to WebSocket (\"is a computer communications protocol, providing full-duplex communication channels over a single TCP connection\" [32]) that receives MQTT messages. Although the database was developed in a NoSQL environment, it was represented with a relational structure to make it easier to understand and interpret (Figure 15).  
The backend server has been recently migrated to datacenter-grade hardware, and the platform has been mounted on a fresh install of the operating system. In addition to the main operating modules of the system, some other functions have been added, including the ability to back up the database to an offsite service like Amazon Web Services (\"Amazon's cloud services provider\" [33]), Microsoft Azure (\"Microsoft's cloud services provider\" [34]), or even a secondary physical server.  
The web application consists of two central parts--the public part which shows the last 10 min of sensing and the private part, where persons can see the information in real-time and download the data stored on the server.  
Figure 14 describes the interaction between the main parts of the EWIN network now developed and deployed.  
Figure 13: Sniffer firmware architecture.  
Figure 14: Data flow and technologies used on the data acquisition system.  
Figure 15: Database structure.
## 2 Cardiovascular Disease
### Coronary Artery Disease
#### 2.1.6. Current Diagnosis  
There are multiple methods to diagnose and confirm CAD. Diagnosing modalities include electrocardiogram (EKG), echocardiogram (echo), stress test, chest X-rays, blood test, and cardiac intervention [64]. EKG is a standard test that not only assesses electrical activity (heart rate and rhythm) of the heart but also enables the predictions of the quality of the heart muscle. For example, ST-elevation myocardial infarction, or STEMI, is a change in the electrical pattern shown on the EKG, indicating a condition of an ongoing acute coronary syndrome (heart attack) [65]. while Echocardiography is a non-invasive ultrasonic imaging technique of the heart, capable of providing valuable diagnostic information about cardiac haemodynamic and function. It can precisely evaluate the motion of the heart wall, chamber size, heart valves, pericardial cavity, and blood ejection fraction [66; 67]. In addition, a chest X-ray is a painless and quick diagnostic tool of the heart. It is often among the first choices ordered by the physicians. Changes in the structure and size of the heart may indicate HF or valvular disease. Moreover, it can also reveal the presence of an aortic aneurysm or calcium deposits in the heart [68]. The stress test is a method of evaluating the effectiveness of the heart during physical activities. Patients will be asked to walk on treadmills or stationary bikes where health practitioners watch and record heart rhythm and blood pressure. This would guide healthcare providers to diagnose the heart and any abnormal rhythm [69]. Blood sample tests can then validate the diagnosis. For example, an excess of natriuretic peptides and troponin T proteins in the blood indicates a diagnosis of myocardial infarction [70]. Results from these test might indicate CAD and presence of the so-called \"culprit lesion\" within the coronary circulation, necessitating diagnostic cardiac intervention using PCI as necessary.
## 3 Results and Discussion
### Statistical Analysis  
To determine the effective parameters, a screening step was implemented using the fractional factorial design. The ANOVA results are given in Table 4. These results indicate that the parameters of flow rate, melt temperature, holding pressure, and runner diameter were effective due to the lower \(p\)-value of 0.05. The parameters of mold temperature, gate diameter, gate angle, and included angle only slightly impacted the defect area. Furthermore, the meaning of squares, which is obtained by dividing the treatment sum of squares by the degrees of freedom, can be used to determine which factors are significant. The higher the mean square, the more of an effect it has on the results [38; 39]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Experimental Test No.** & **1** & **2** & **3** & **4** \\ \hline \multirow{5}{*}{**C**} & **Flow rate (cm\({}^{3}\)/s)** & 15 & 15 & 25 & 25 \\  & **Melt temperature (\({}^{\circ}\)C)** & 185 & 195 & 185 & 195 \\  & **Mold temperature (\({}^{\circ}\)C)** & 35 & 35 & 35 & 35 \\  & **Holding pressure (MPa)** & 60 & 80 & 60 & 80 \\  & **Runner diameter (mm)** & 10 & 10 & 10 & 10 \\  & **Gate diameter (mm)** & 3.5 & 3.5 & 3.5 & 3.5 \\  & **Gate angle (\({}^{\circ}\))** & 0 & 0 & 0 & 0 \\  & **Included angle (\({}^{\circ}\))** & 45 & 45 & 45 & 45 \\ \hline \multirow{3}{*}{**C**} & **Experimental defect area (mm\({}^{2}\))** & 2108 & 1621 & 2483 & 1696 \\ \cline{2-6}  & **Simulation defect area (mm\({}^{2}\))** & 2212 & 1553 & 2777 & 1674 \\ \cline{1-1}  & **Deviation error** & **4.9\%** & \(-\)4.2\% & 11.8\% & \(-\)1.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Validation of FEA. Figure 7: Simulation result (**a**) and enclosed area of defect in the experimental test (**b**) obtained with the same injection molding process settings (melt temperature = 185 \({}^{\circ}\)C, mold temperature = 35 \({}^{\circ}\)C, flow rate = 25 cm\({}^{3}\)/s, holding pressure = 80 MPa, runner diameter = 10 mm, gate diameter = 3.5 mm, gate angle = 0\({}^{\circ}\), and included angle = 45\({}^{\circ}\)). The DOE intended for the screening step to be executed with all parameters varying between two levels. In the second phase, by performing CCD with an alpha coefficient of 1.4, the number of parameter levels was increased to five (shown in Table 5). This could lead to a better understanding of the parameters' effects on the process outputs. To predict the area of the defect with a regression equation, an ANOVA was implemented on the results of the simulations suggested by the CCD. According to Table 6 and considering the \(p\)-value of the model equal to 0.000, it can be concluded that the model presented by ANOVA has good accuracy in the prediction of the defect area. The effect of all four parameters known to be effective in the previous step has been reaffirmed by the design of experiments carried out by considering more levels. Diagrams related to effective parameters can be seen in Figure 8.