esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption
## 3 Results
### Memory Consumption  
The algorithm presented in this work has a different memory consumption than the traditional method. Next, you can see in Figure 4 the memory saving process when using the proposed method against the traditional one. In addition, Table 3 shows the memory used for all the cases employed.  
The minimum improvement in memory consumption is over 50%, although most comparisons range from over 60% to almost 80%. This significant improvement in the bytes required for storing the network structure and weights shows a clear significance that is especially beneficial as the MLP grows in complexity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **One Hidden Layer** & **Two Hidden Layers** & **Three Hidden Layers** \\ \hline Iris & 4, 5, 3 & 4, 5, 7, 3 & 4, 4, 5, 5, 3 \\ Cancer & 9, 7, 1 & 9, 7, 3, 1 & 9, 12, 8, 4, 1 \\ Ionosphere & 34, 9, 1 & 34, 9, 4, 1 & 34, 12, 8, 4, 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Topology of the networks used for the experimental results. The numbers indicate the amount of neurons on each layer. The first and last values represent the input and output layers, respectively, and the middle numbers are the hidden layers.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 1 Introduction
### Organization of This Paper  
The remaining paper is arranged, as follows. Section 2 describes a structure for smart health-care, comprising of communication technologies, network types, services, applications, requirements, and characteristics. Section 3 presents scenarios for 5G network and its requirements. Section 4 presents technology trends to achieve the requirements in the 5G network. Section 5 presents open issues and challenges. Finally, Section 6 presents conclusion.
# Quantum Image Steganography Schemes for Data Hiding: A Survey
## 3 Quantum Image Steganography Schemes
### Least Significant Qubit  
The least significant qubit (LSBq) is one of the most common techniques for applying quantum image steganography [25]. Inspired by the LSB method, LSBq explains the process of hiding data by substituting the least significant qubits of an image using the qubits of the confidential data [4]. Studies used LSB and LSBq methods to implement quantum steganography, with each showing different approaches to enhance this method by applying additional protocols and algorithms to improve the steganography representation and security. However, there are also recent studies using primarily the LSBq scheme to create novel quantum steganography approaches.  
For instance, in 2018, [PERSON] et al. [5] achieved high security and efficiency by primarily using the LSBq approach. In this scheme, the embedded qubits were placed into the corresponding channel by a specified modulo value rather than in order, which, as a result, enhanced the steganography's security. Moreover, [PERSON] reduced the time complexity of the algorithm by directly embedding the covert message's qubits into the least significant qubit of the carrier image instead of comparing them as a step before exchanging them. The scheme used quantum multi-wavelength images (QRMW) [9] to implement steganography, which allowed for adding different wavelengths for each modulo value to hide larger scales of the secret message and increasing the scheme's security. By implementing the previous changes, the mentioned method presented a better performance compared to older works from the same domain.  
Figure 2: A simple 2 \(\times\) 2 MCQI image representation. Adapted from [28].  
A more recent approach by [PERSON] et al. [6] created steganographic images using LSB by embedding the confidential image qubits as well as each of the two threshold values into the 16 pixels' least significant bits found at the edges of a grayscale image [7], where the same two thresholds are used to extract the embedded message by filtering out the cover image. [PERSON] used the image model NEQR [7] for their scheme, which specializes in using qubits to hold information of grayscale values as well as each qubit's position. With the use of the NEQR model [7] and a novel LSB embedding scheme [6], the results of this method have shown a large increase in performance speed compared to the alternatives. Moreover, to improve this method, [PERSON] suggested that the existence of an operation that resets the qubit in the future could further enhance its process.
# An AUV-Aided Cross-Layer Mobile Data Gathering Protocol for Underwater Sensor Networks +
###### Abstract  
Underwater sensor networks (UWSNs) have recently attracted much attention due to their ability to discover and monitor the aquatic environment. However, acoustic communication has posed some significant challenges, such as high propagation delay, low available bandwidth, and high bit error rate. Therefore, proposing a cross-layer protocol is of high importance to the field to integrate different communication functionalities (i.e, an interaction between data link layer and network layer) to interact in a more reliable and flexible manner to overcome the consequences of applying acoustic signals. In this paper, a novel Cross-Layer Mobile Data gathering (CLMD) scheme for Underwater Sensor Networks (UWSNs) is presented to improve the performance by providing the interaction between the MAC and routing layers. In CLMD, an Autonomous Underwater Vehicle (AUV) is used to periodically visit a group of clusters which are responsible for data collection from members. The communications are managed by using a distributed cross-layer solution to enhance network performance in terms of packet delivery and energy saving. The cluster heads are replaced with other candidate members at the end of each operational phase to prolong the network lifetime. The effectiveness of CLMD is verified through an extensive simulation study which reveals the performance improvement in the energy-saving, network lifetime, and packet delivery ratio with varying number of nodes. The effects of MAC protocols are also studied by studying the network performance under various MAC protocols in terms of packet delivery ratio, goodput, and energy consumption with varying density of nodes.  
underwater sensor networks; cross-layer routing; MAC algorithms; distributed clustering approach; autonomous underwater vehicles +
Footnote †: 28 July 2020; Accepted: 20 August 2020; Published: 26 August 2020
# Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness
## 2 Related Works
### Inpainting in Medical Field  
#### 2.2.1 Traditional Approach  
The use of inpainting techniques is emerging in medical image analysis since local deformations in medical modalities are common because of various factors such as metallic implants or specular reflections during the image captures. The completion of such missing or distorted regions is important to enhance post-processing tasks such as segmentation or classification. Traditional approaches for medical image inpainting focus on interpolation, non-local means, diffusion techniques, and texture synthesis [19; 20; 21; 22; 23]. However, the conventional methods are confined to a single image and they do not learn from images with similar features.
## 2 Hybrid DC Transmission System  
### Structure of the Hybrid DC Transmission System  
For large-scale and long-distance transmission from wind power bases that are more than 1000 km away from the load center, LCC-HVDC is still a more appropriate choice. However, this method has defects, such as the risk of commutation failure and more loss in reactive power. In contrast, VSC-HVDC transmission not only avoids the problem of commutation failure but also realizes independent and rapid control of active and reactive power. Additionally, the transmission line possesses a certain capacity for reactive support, allowing for the dynamic compensation of reactive power in the AC system.  
To fully exploit the high voltage and large capacity transmission capability of the LCC converter station and the flexible control of the VSC converter station, a hybrid LCC-VSC DC transmission system is considered [25]. Through appropriate control strategies, power coordination control is achieved to mitigate the impact of wind power fluctuations on system stability. The four-machine, two-area model is used as the basis [26] in which the VSC-MTDC system is embedded, and the wind farm is connected in Area 1, where G1 and G2 are hydroelectric units. The wind power units in Area 1 are connected to the same AC bus as the hydroelectric units in the local grid, and active power is transmitted to Areas 2 and 3 through DC lines.  
The wind farms are equipped with a certain percentage of hydroelectric turbine FM capacity to smooth out fluctuations in wind power output. As shown in Figure 1.
## 3 Results and Discussion
### Adsorption Experiment of High-Fluorine Uranium-Containing Wastewater
(**b**) The removal efficiency of U(VI) and F\({}^{-}\) (**c**) in high-fluoride uranium-containing wastewater with different uranium concentrations by MS/CID/ZrO\({}_{2}\). (**d**) Adsorption mechanism diagram of MS/CID/ZrO\({}_{2}\). capacity for F\({}^{-}\) was 107.98 mg/L. The adsorption of U(VI) and F\({}^{-}\) by the MS/CID/ZrO\({}_{2}\) can be attributed to the interactions between surface functional groups, such as hydroxyl (-OH), Zr-OH, -NH, and -NH\({}_{2}\), and U(VI). These groups exhibited electrostatic attraction and complexation with U(VI), and their active sites demonstrated high adsorption capacity for various uranium hydrolysis products, based on previous reports [43, 44]. The adsorption of F\({}^{-}\) by MS/CID/ZrO\({}_{2}\) is primarily due to the in situ biomineralization formation of ZrO\({}_{2}\) nanoparticles. In acidic solutions, the positively charged surface sites attract negatively charged F\({}^{-}\) through electrostatic interactions. Previous studies have shown that one ZrO\({}_{2}\) molecule can bind with up to five F\({}^{-}\), significantly enhancing the adsorbent capacity of materials for fluoride removal [45, 46].
## 2 Methodology
### Methodology for the Assessment of Data Quality
#### 2.1.3 Process Data Capture  
Data for the fuel system process was recorded within four weeks for healthy condition (HC), single-component degradation (SCD), and multi-component degradation (MCD) scenarios between pump speeds of 700 rpm and 950 rpm in intervals of 50 rpm. The SCD process data represent the degradation of individual components (see Table 2) with pressure and flow measurements at \(P_{1},P_{2}.....P_{5}\) and \(f_{1},f_{2}\), respectively. Data logging for each faulty condition scenario started at least 10 min after the process had reached steady-state conditions or when there was a step change in pump speed or a change in the failure condition scenario, with each data file having different sample sizes. Figures 2 and 3 show the distribution of process parameters for a filter-clogging scenario with a gradual closure of DPV1 (see Figure 1) and a water distribution system under the healthy condition, respectively.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Component** & **Fault Emulation** &
\begin{tabular}{c} **Healthy State/Fault Emulation** \\ **Mechanism** \\ \end{tabular} & **Fault Code** \\ \hline Filter & DPV 1 & Fully open/gradually closing & FC1 \\ Pump & DPV 2 & Fully closed/gradually opening & FC2 \\ Valve & DPV 3 & Fully open/gradually closing & FC3 \\ Nozzle & DPV 4 & Fully open/gradually closing & FC4 \\ Pipe & DPV 5 & Fully closed/gradually opening & FC5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Healthy condition operating state of the system’s control valves and associated fault codes.  
Figure 1: Water distribution system testbed schematic.
# Heat Transfer Enhancement of Nanofluids with Non-Spherical Nanoparticles: A Review
## 2 Thermal Conductivity of NSN Nanofluids
### Numerical Predictions and Mechanism Discussion
#### 2.2.1 Interfacial Layer Theory  
Nanofluids are composites with particle additives [55]. The liquid atoms of the base fluid near a nanoparticle will be absorbed into the solid surface of the nanoparticle and form an absorption layer [56]. The interfacial layer between particles and base fluid has a remarkable effect on thermal conductivity. For non-spherical particles, the surface area of the interfacial layer is much larger than that of spherical particles, which can trigger a larger heat flux. [PERSON] et al. [57] presented a model for CNT nanofluids considering the interfacial thermal barrier resistance and found a significant interface effect on TCE. Many models [58; 59; 60] were built by considering the interfacial layer effect. The benefit of this theory is that it considers the effect of particle morphology from the microscopic point of view. [PERSON] et al. [61] proposed a model considering the interfacial layer thickness, particle shape, and Brownian motion. They attributed the anomalous TCE to the particle shape.  
[PERSON] et al. [34] believed that TCE for NSN nanofluids depends on the competition between surface contact area (enhancement) and Kapitza resistance (weakening). The model predicted that TCE would be negative when sphericity is smaller than 0.6 for Al\({}_{2}\)O\({}_{3}\)-EG/water (50/50) nanofluids. [PERSON] et al. [62; 63; 64] developed several EHC models based on the anisotropy analysis of cylindrical nanoparticles. They summed axial and radial direction heat transfer flux of cylindrical nanoparticles whose orientation distribution is uniform. These models are built exquisitely and are challenging to use. The shape of nanoparticles was first incorporated into an AI-based model for thermal conductivity by [PERSON] et al. [35]. The results of the developed CFFNN model revealed that the cylindrical shape of nanoparticles with a large aspect ratio could trigger a rapid heat flow along longer thermally conducting pathways without the need to cross an interparticle boundary or junction point; this is beneficial to the thermal energy transfer in the nanofluids.
# INVSID 1.0: An Inverse System Identification Toolbox for MATLAB
## 1 Introduction
Direct system identification-based inversion approaches use the input-output data from the system to be inverted to identify the inverse system directly; however, the system identification cannot be conducted when the system to be inverted is not stable [3]. For signal modeling-based inversion approaches, the input signal, which is to be reconstructed, must be a periodic signal under stationary operating conditions [21, 22]. It should be noted that guaranteeing the stability of obtained solutions is a priority of both direct and indirect inversion approaches. So for some indirect inversion approaches, stability is ensured, but infinite or finite pre-actuation is needed, which cannot be applied well in practice because sometimes the desired output in unknown. In this paper, an entirely different system inversion approach by combining time-domain observer design and frequency-domain subspace identification is proposed. The presented approach can guarantee the stability of obtained system inversion, and simultaneously the proposed approach does not need any pre-actuation. Moreover, the approach can be applied to stable or unstable systems/minimum-phase or nonminimum-phase systems to be inverted, and there is also no requirement for the type of input and output signals. Furthermore, it does not suffer from non-convex or input noise problems. Even though the proposed approach has the above advantages by comparing with other approaches, the current version of the proposed approach can just used for solving the system inversion problem of systems of which the inputs do not have overlapping frequency ranges. In this paper, only single-input single-output systems are considered. To facilitate the use of the proposed system inversion approach by third parties, a MATLAB toolbox implementing the approach is created in this paper after theoretical derivation of the approach. The full name of the MATLAB toolbox is **INV**erse **S**ystem **ID**entification, with the first version abbreviated as INVSID 1.0. The remainder of the paper is organized as follows. In Section 2, the inversion approach is first proposed, and then the corresponding MATLAB codes are generated,based on which the toolbox NIVSID 1.0 is created, followed by Section 3, in which the usage of the toolbox NIVSID 1.0 is validated by both simulation and practical examples. Finally, conclusions and perspectives are given in Section 4.
## 3 Materials and Methods
### BiGRU with Attention + Transformer Embeddings  
Further enhancing the model of BiGRU with attention, the researchers introduced transformer-based embeddings, such as BERT or RoBERTa, as input representations. Transformer embeddings are known for their ability to capture deep contextual semantics through self-attention mechanisms. Integrating these embeddings provides the BiGRU model with a rich, contextually informed starting point, allowing it to leverage both the fine-grained semantic information encoded by transformers and the sentence-level focus introduced by the attention mechanism. This configuration--BiGRU with Attention + transformer embeddings--is expected to bridge the gap between traditional sequential models and state-of-the-art WSD techniques, enabling the model to better handle complex word senses across diverse contexts.  
Figure 5 illustrates the process of transformer embedding generation, showcasing key stages such as tokenization, positional encoding, self-attention, feed-forward layers, and output embeddings. Transformer embeddings are incorporated into the BiGRU model to increase WSD accuracy.  
Embedding Layer: contextual information from surrounding text is captured via pre-trained transformer-based embeddings (like BERT, RoBERTa, or ALBERT) that create dense vector representations of words. BiGRU Encoder: by processing the sequential embeddings in both the forward and backward directions, the Bidirectional Gated Recurrent Unit (BiGRU) maintains contextual relationships from the past and future.  
By dynamically weighing contextual contributions, the attention mechanism (for attention-optimized BiGRU models) aids the model in concentrating on the most pertinent words in a phrase, enhancing disambiguation. Then, a Fully Connected Layer assigns word sense categories to the learnt feature representations, and a Softmax Layer determines the most likely interpretation based on context by computing probability distributions across all potential senses. This hybrid design preserves the sequential processing advantages of BiGRU while utilizing transformers.  
A condensed representation of the input is created by the hidden layer shown in Figure 4. Using this representation, the decoder recreates the original input. Only the compressed feature vector would be available without the decoder, and we would not be able to determine how accurately it replicates the original input. When the task involves converting the hidden feature representation back into a structured or interpretable form, a decoder is required. A decoder might not be required if the feature vector alone is adequate for the task (such as classification).
## 3 System Models
### Pmbm RFs  
The PMBM filter is a combination of two disjointed parts, the Poisson point process (PPP) and the multi-Bernoulli mixture (MBM). The PPP describes the distribution of the targets that exist but are not detected, which is expressed by \(X^{u}\). The MBM describes the mixture distribution of the targets that have been detected at least once, represented by a series of weighted multi-Bernoulli density, expressed as \(X^{d}\).  
\[X^{u}\cup X^{d}=X,X^{u}\cap X^{d}=\varnothing, \tag{5}\]  
\[p_{k}(X^{u})=e^{-\langle D_{k}(X),1\rangle}\prod_{X\in\textbf{X}^{u}}D_{k}(X) =e^{-\lambda}\prod_{X\in\textbf{X}^{u}}\lambda f_{k}(X), \tag{6}\]  
\[p_{k}(X^{d})=\sum_{\cup_{i\in\textbf{X}^{i}}=\textbf{X}^{d}}\prod_{i\in \mathbb{I}}p_{k}^{i}(X^{i}), \tag{7}\]  
\[p_{k}^{i}(X^{i})=\begin{cases}1-r_{k}^{i}&X^{i}=\varnothing\\ r_{k}^{i}p_{k}^{i}(x)&X^{i}=\{x\}\\ 0&\left|X^{i}\right|\geq 2\end{cases}, \tag{8}\]  
\[p_{k}(X_{k})=\sum_{\textbf{X}^{u}\cup\textbf{X}^{d}=\textbf{X}}p_{k}(X_{k}^{ u})\sum_{j\in\mathbb{J}}w_{k}^{j}p_{k}^{j}(X_{k}^{d})=\sum_{x\in\mathbb{X}^{u} \cup\textbf{X}^{d}=\textbf{X}}e^{-\int D_{k}(X^{u})dx}\prod_{x\in\mathbb{X}^{ u}}D_{k}(X^{u})\sum_{j\in\mathbb{J}}\sum_{\cup_{i\in\mathbb{I}}\textbf{X}^{i}= \textbf{X}^{d}}\prod_{i\in\mathbb{I}}w_{k}^{j,i}r_{k}^{j,i}r_{k}^{j,i}(X_{k}^{ j,i}), \tag{9}\]  
where \(p_{k}^{j,i}(X_{k}^{j,i})\) represents the density of the \(i\)th Bernoulli in the \(j\)th MB, \(\mathbb{I}\) is an index set of independent Bernoulli components, and \(\mathbb{J}\) is an index set for the MBs in the MBM or components of the MBM. \(w_{k}^{j,i}\) is the probability of the \(i\)th Bernoulli component density in the \(j\)th global hypothesis. Then, the density of PMBM can be presented as shown in Equation (9), which is represented entirely by the parameters of \(D_{k}(X^{u})\), \(\left\{w_{k^{\prime}}^{j}\left\{r_{k}^{j,i},p_{k}^{j,i}(X^{j,i})\right\}_{i\in \mathbb{I}^{j}}\right\}_{j\in\mathbb{J}}\). Because the density of PMBM is a prior MTT conjugate, the PMBM density is a prior conjugate with both the prediction and the update, preserving the PMBM form of the density.
# Selection of a PCM for a Vehicle's Rooftop by Multicriteria Decision Methods and Simulation
## 4 Conclusions  
Starting with a list of 15 PCMs, savENRG PCM-HS22P was selected as the best by the utilization of the AHP, VIKOR, COPRAS and TOPSIS multicriteria methods. Furthermore, a Spearman's correlation showed that the methods are consistent, and the selection is optimum.  
Figure 3: Discharge.  
Figure 2: Charging the simulation comparison. (**a**) PCM 0 mm (**b**) PCM 20 mm.  
The simulation showed that the material savENRG PCM-HS22P improves the thermal comfort in the cabin by reducing the internal temperature of the air by 9 \({}^{\circ}\)C in the charging event, and in the discharging simulation it was also showed that the PCM allows to maintain a better temperature inside the cabin by making a differentiation of 4 \({}^{\circ}\)C warmer, both with a 20 mm layer of the selected PCM.  
It is demonstrated that the use of a PCM layer of 20 mm can improve the thermal comfort in the vehicle, reducing the need to use the heater or the air conditioner.  
Conceptualization: J.M.-G. and J.F.N.; methodology: M.C., J.M.-G. and J.F.N.; software: M.C., [PERSON] and J.F.N.; validation: M.C., [PERSON], J.F.N., R.B.S. and E.R.; formal analysis: E.R., J.F.N. and [PERSON]; investigation: [PERSON], J.F.N. and [PERSON]; resources: E.R.; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON], R.B.S. and [PERSON]; visualization: J.F.N.; supervision: [PERSON]; project administration: [PERSON]; funding acquisition: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research takes part of the project 'Selection, characterization and simulation of phase change materials for thermal comfort, cooling and energy storage'. This project is part of the INEDITA call for R&D research projects in the field of energy and materials. This research is part of the project P121819, Parque de Energias Renovables, founded by Universidad International SEK.  
Not applicable.  
The data reported in this research can be found at [[https://drive.google.com/file/d/19](https://drive.google.com/file/d/19) rI2-2r6 mYN0C3 Ymjiegf55 bdqjrB8Z/view7 usp=sharing]([https://drive.google.com/file/d/19](https://drive.google.com/file/d/19) rI2-2r6 mYN0C3 Ymjiegf55 bdqjrB8Z/view7 usp=sharing) (accessed on 15 January 2021).  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.