esa-sceva/satcom-chunk-collection huggingface dataset:
# The Future of the Human-Machine Interface (HMI) in Society 5.0
## 5 Human-Centric Manufacturing (HCM)
### Contribution of Human Digital Twins  
Human digital twins have the potential to revolutionize the way we understand and manage human health, wellbeing, and performance. Here are some of the potential applications and benefits of human digital twins [55]:  
* Proxy (virtual agent) meetings between digital twins: digital twins with the personality and characteristics of individual people can react as if they were the real people in response to approaches from others in cyberspace [56];
* Creating a personal/virtual agent to work on your behalf: you can extend the range of human activity from the real world to cyber space using digital twin computing [57];  
Figure 10: Human digital twins.  
* Enabling dialogue that would be impossible in the real world: digital twins can also be used to communicate with people who do not currently exist, such as the deceased, to gain knowledge and experience [58];
* Using digital twins as an interface: creation of a derivative of your own digital twin endowed with abilities you do not possess [15];
* Enabling dialogue: language skills can be fabricated by exchanging or merging the abilities of your own Digital Twin with someone else's [15];
* Personalized healthcare: Human digital twins can be used to develop personalized treatment plans based on an individual's unique characteristics and responses to different stimuli. By simulating and predicting how an individual will respond to different medications or therapies, healthcare providers can optimize treatments and improve outcomes [25];
* Disease prevention: Human digital twins can be used to monitor an individual's health and detect early signs of disease or illness. By analyzing data from wearable devices and other sources, the twin can identify patterns and anomalies that may indicate a potential health problem [59];
* Sports performance optimization: Human digital twins can be used to optimize sports performance by simulating and predicting how an individual will respond to different training regimens and environmental conditions. This can help athletes and coaches develop more effective training plans and prevent injuries [60];
* Workplace safety: Human digital twins can be used to improve workplace safety by simulating and predicting how an individual will respond to different work environments and hazards. This can help to identify and mitigate potential risks before they occur [61];
* Social science research: Human digital twins can be used to study human behavior and social dynamics in a controlled environment. By simulating and predicting how individuals will interact with each other under different conditions, researchers can gain insights into complex social phenomena [62].  
In summary, human digital twins have the potential to revolutionize our understanding of human health, wellbeing, and performance, and to improve outcomes in a wide range of domains.
## 3 Methodology
### Prediction Using GP
#### 3.2.5 ELM  
In 2004 [PERSON] introduces the mathematical model of ELM. The ELM's basic principle is based on a single hidden layer feed-forward neural network (SLFN) (Figure 6). Because of its improved generality, simplicity, and efficient forecasting nature, the ELM has been employed in a variety of application areas [84].  
Figure 6: ELM Architecture.  
The basic premise of ELM is as follows: Given \(N\) as the number of hidden units, \(K\) as the number of training samples, and the activation function \(f(\) \()\) in the hidden units, the output of the ELM \(o_{m}\) for the \(m\)th training sample is depicted in Equation (19) as:  
\[o_{m}=\sum_{i=1}^{N}\beta_{i}f(w_{k},b_{i},x_{m})\ \ m=1,\ldots,K \tag{19}\]  
where \(b_{i}\) is the hidden neurons' bias factor, \(x_{m}\) denotes the number of inputs, \(\beta_{i}\) denotes the output weight vectors and \(w_{k}\) denotes input weight vectors. The sigmoid function is used as an activation function. The sigmoid function's output is essentially in a range of 1 to 0. To determine the output weights, the linear equation (Equation (20)) is employed.  
\[\beta=H^{\ddagger}Y \tag{20}\]  
where \(H\) denotes the output matrix of the hidden layer, \(H^{\ddagger}\) the Moore-Penrose generalized inverse [85] of \(H\), and \(Y\) denotes the ELM output targets. In Equation (21), Equation (20) is written as:  
\[H\beta=Y \tag{21}\]  
Equation (22) can be used to define \(H\), \(\beta\), \(Y\) as follows:  
\[H=\begin{bmatrix}p(x_{1})\\ \vdots\\ p(x_{K})\end{bmatrix}=\begin{bmatrix}f(w_{1},b_{1},x_{1})&\cdots&f(w_{N},b_{N}, x_{1})\\ \vdots&\cdots&\vdots\\ f(w_{1},b_{1},x_{j})&\cdots&f(w_{N},b_{N},x_{j})\end{bmatrix}_{N\times K}, \beta=\begin{bmatrix}\beta_{1}^{Y}\\ \vdots\\ \beta_{N}^{Y}\end{bmatrix}\text{ and }Y=\begin{bmatrix}y_{1}^{Y}\\ \vdots\\ y_{K}^{Y}\end{bmatrix} \tag{22}\]  
In this case, the hidden layer's feature mapping is \(p(x)\). \(H\) is the ELM's output.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 3 Synthesis of Molecularly Imprinted Polymers
### Sol-Gel Polymerization  
Sol-gel synthesis occurs by dissolving a metal oxide precursor (M(OR)n) in a low molecular weight solvent medium using a catalyst (acid, base, or ions such as F\({}^{-}\)) followed by a hydrolysis and polycondensation step [24]. Sol-gel is a simple, manageable, and cost-effective method allows production of different types of nanomaterials or modification of polymeric surfaces. Sol-gel polymers involving the use of silica-based materials can produce imprinted selective cavities with a longer lifetime with strong and stable structures besides the high porosity and capacious nature of silica. The method is environmentally friendly, as water or ethanol are used as solvents, and performed at room temperature [25].  
Other methods of polymerization may involve ultrasonic and microwave radiation. The advantages of these methods include rapid polymerization process, controlled polymer size, simple process conditions, and a high yield, but they are still of limited application compared to other, previously mentioned methods [26; 27].
## 3 Results and Discussion  
### HMM Training and Validation Results  
We trained HMM models using 70% of the hand-labeled data using \(k\)-fold cross-validation method to optimize the training output. As mentioned in Section 2.2.3, due to the imbalance in the data, we use a 80%-20% split for the training and validation of the HMM, respectively, to avoid missing observations and behaviors when training the HMM.  
We present the accuracies for all folds with different \(k\)-fold values as a boxplot in Figure 9. The boxplot shows the distribution of the accuracy for different \(k\)-fold values and the scatter plot overlaid to represent the individual accuracy of each fold within the \(k\)-fold that generated an HMM model. From the plot, we can see that the ranges of the accuracies across all \(k\)-folds were between 90.35% to 97.77%, and the average accuracies across all folds were around 93%. The highest accuracy occurred when \(k=9\) and Fold = 6. We chose the optimized transition and emission matrices from this fold for evaluation against the hold-out test set.  
Figure 8: HMM design using \(k\)-fold cross-validation.
# Research on Image Denoising in Edge Detection Based on Wavelet Transform
## 1 Introduction
Edge detection is performed on the images before and after denoising, and the results are compared to show the importance of wavelet transform denoising indirectly. To further illustrate the advantages of the wavelet transform, both wavelet transform and Gaussian filtering are used for denoising, and then, the edge detections are carried out on the denoised results. Their corresponding Pratt quality factors show that using the wavelet transform is better at removing noise interference while retaining edge information.
# Distributed Edge Computing to Assist Ultra-Low-Latency VANET Applications
## 4 Latency-Aware Offloading Algorithm for Multilevel MEC VANET System  
In this part, we develop an offloading algorithm for the proposed VANET system. The algorithm manages and controls the offloading of computation tasks from vehicles to the distributed edge servers, in a way that achieves higher latency efficiency. The algorithm is based on the general offloading algorithm developed in [38], for multilevel MEC based systems.  
The proposed offloading algorithm increases the interactivity of vehicles with the surrounding devices, i.e., V2X. This provides a novel scheme for innovation of service provision in vehicular systems. The proposed algorithm assumes that each OBU is empowered with a decision engine that decides the offloading process and thus, implements the proposed algorithm.  
The proposed offloading algorithm considers the quality of service (QoS) latency for each computing task [39]. The task should be handled within the QoS latency, either execute the task locally on the vehicle or offload it to the nearby edge server connected to RSU or the cellular base station.
# On Hybrid Energy Utilization in Wireless Sensor Networks
## 2 Problem Formulation and Markov Decision Process
### Optimal Transmission Policy  
Our goal is to find the optimal transmission policy \(\pi^{*}\left(S\right)\), which specifies the optimal action at the state \(S\), to make a tradeoff between the minimization of the expected cost values of dropping packets and using constant energy source. The optimal policy satisfies [PERSON]'s equation as shown in [4]:  
\[V_{\pi^{*}}(S)=\min_{A\in\mathcal{A}}\left(C_{A}(S)+\gamma\sum_{S^{\prime}\in \mathcal{S}}P_{A}(S^{\prime}|S)V_{\pi^{*}}(S^{\prime})\right),S\in\mathcal{S} \tag{5}\]  
where \(\gamma\) is a discount factor, and \(V_{\pi^{*}}\left(S\right)\) represents the expected cost value with respect to the optimal policy \(\pi^{*}\left(S\right)\). The optimal policy that satisfies the [PERSON]'s equation can be found via a well-known value iteration algorithm [23]. At the \(i\)th iteration, we have  
\[V_{i+1}^{A}(S)=C_{A}(S)+\gamma\sum_{S^{\prime}\in\mathcal{S}}P_{A}(S^{\prime} |S)V_{i}(S^{\prime}),S\in\mathcal{S},A\in\mathcal{A} \tag{6}\]  
\[V_{i+1}(S)=\min_{A\in\mathcal{A}}\{V_{i+1}^{A}(S)\},S\in\mathcal{S} \tag{7}\]  
The value of \(V_{0}\left(S\right)\) is initialized as zero for all states \(S\in\mathcal{S}\). The above procedures in (6) and (7) are repeated for several iterations until a stopping criterion is met; i.e., \(\left|V_{i+1}\left(S\right)-V_{i}\left(S\right)\right|\leq\epsilon\), where \(\epsilon\) is a preset threshold.
# Untargeted 1H-NMR Urine Metabolomic Analysis of Preterm Infants with Neonatal Sepsis
###### Abstract
As the outcome of biochemical procedures regulated by proteins derived from genes expression, metabolomics provides the closest relation with the phenotype of an organism, at a specified time frame in correlation with endogenous and exogenous influences. The utilization of metabolomics as a tool for the validation of new biomarkers for early diagnosis or prognosis of pathophysiological conditions is constantly growing. Before any metabolomic analysis, based on the main question to be answered, the process of the analysis has to be determined. A targeted metabolomics approach is selected when the aim is to measure the levels of a particular set of metabolites which are suspected to have a relation with a condition. An untargeted approach aims to detect and assign as many peaks as possible related to metabolites biofluid, tissue or cell extract under study, identifying the metabolic \"fingerprint\" [26]. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two dominant analytical techniques for metabolomic analysis of biofluids. NMR-based analysis offers numerous advantages and could combine chemometrics and basic clinical research [27]. In the field of neonatology, the interest for a prognostic and diagnostic tool for neonatal sepsis is continuously growing. A urine sample, reflecting a holistic view of the metabolism, is considered as the most appropriate biological fluid for analysis of newborns' metabolism via NMR spectroscopy. The impact of neonatal sepsis on newborn metabolism has been in the center of interest the last few years and application of metabolomic studies towards the investigation of a specific profile of septic newborns is on demand, but, to establish metabolites as biomarkers, further analysis must be conducted [28; 29; 30]. The aim of our study is to reveal those metabolites with differentiated levels among the septic neonates hospitalized in the Neonatal Intensive Care Unit (NICU) and control/healthy neonates. Such metabolic alterations could lead to early diagnosis of EOS and LOS. To our knowledge, both medical treatment and parenteral nutrition of NICU hospitalization may induce essential metabolic drifts. For this reason, we chose to investigate the comparison of the sepsis metabolic profile with the metabolic profile of NICU hospitalized neonates without severe comorbidities except prematurity, complementary to healthy neonates, whose care after birth was taken over by their mother.
## 1 Introduction
An ordinary kriging technique has also been used to generate top sand thickness and seabed surface [19]. The prediction of soil properties from terrain attributes using various forms of the kriging regression analysis technique has been reported to perform well [20; 21]. Kriging has been evaluated as an unbiased and least error-prone optimum reserve estimation method to reduce or avoid ambiguities in plotting geological cross-sections on the basis of engineering geological properties and data from exploratory holes [22]. Among the eight data interpolation techniques available under the spatial analyst tool in the ArcMap software, the inverse distance weighting (IDW) method has been reported to produce more accurate zonation maps. Standard penetration resistance maps for various depths have been generated and compared with the actually measured values for Surfers' Paradise in Australia [23]. The mapping of soil properties is a time consuming and costly process, especially for diverse topographic conditions [24]. The estimations of soil moisture content using ordinary kriging, IDW, kriging with external drift (KED), and spline were compared, and KED has been reported to produce better results [25; 26]. The engineering properties required to correlate with their parent materials irrespective of the diversity in the soil and the parent materials have been reported and are assumed to be applicable for all types of parent materials [27]. Multicriteria decision analysis (MCDA) has been performed using GIS software to develop geotechnical micro-zonation models using various soil properties [28]. The geotechnical database has been processed using GIS to generate soil types and standard penetration resistance maps to ease data interpretation [29]. Most of the soil investigations are being carried out without the use of GIS in this region. The current study on the geotechnically diversified region is the first of its kind. This work is an effort to introduce the integration of GIS-based field explorations and subsequent development of geotechnical maps, which has not been practiced in the region. A correlation between Standard Penetration Test (SPT) N values and index properties of soil was evaluated using artificial neural networks and another correlation between relative compaction and allowable bearing capacity has also been appraised  
The development of a regional geotechnical map provides a quick and easily obtaining insight regarding soil formation and allowable bearing capacity. In addition, this map will help in preliminary studies, feasibility studies, and land use policies.
## 1 Introduction  
Sentiment analysis is a branch of natural language processing (NLP) that is used for classifying opinions. It is widely used in market research, feedback analysis, and election and social media monitoring [1]. There are two lines of thinking when conducting sentiment analysis: the machine learning-based approach and the lexical dictionary methodology [2]. Through machine learning, the process of training is flexible and can be adjusted according to the task's requirements, but it requires labeled data. The lexicon approach uses a predefined list of words associated with sentiments; however, this procedure is not very adaptable and requires a dictionary based on the task [3].  
Studies such as [4; 5; 6; 7] focus on how various emotions affect human performance. The researchers stimulate artificial emotions in the participants to comprehend their effects. Contrary to their work, our research focuses on investigating the emotional aspects of communication in domain-specific short messages (DSSMs) and compares them to domain-independent short messages (DISMs). To achieve this objective, we closely examine the messages exchanged between professionals in the software engineering (SE) domain, with the aim of gaining a deeper understanding of the emotions that underlie their interactions.  
We have formulated the following research questions that we will investigate:  
**RQ1:**_Are DSSMs semantically distant from DISMs?_  
**RQ2:**_Are sentiments expressed differently in each domain?_  
**RQ3:**_Could cross-domain models accurately capture the meaning of DSSM terminology?_We aim to compare DSSMs and DISMs by fine-tuning the Roberta transformer model, which will allow us to evaluate sentiments accurately. To make the approach more reliable, we plan to incorporate data from platforms that are known for their communication styles and community dynamics. Furthermore, we will compare the performance of the various models across single and multi-domains, aiming to determine which models perform best.  
The rest of the paper is organized as follows. In Section 2, we will present an overview of the related work in this field. In Section 3, we justify the datasets that were used and also introduce the proposed methodology. Section 4 briefly describes various methods in the sentiment analysis approach with a focus on the RoBERTa technique. Section 5 presents discussions related to the obtained results. Finally, we conclude in Section 6.
## 2 Performing Filling and Emptying Operations
### Experimental Facilities and Tests
#### 2.1.1 Filling Procedure  
The filling process is analysed for a pipeline inclined at 30\({}^{\circ}\), with a nominal diameter of DN 63 mm (inner diameter of 51.4 mm) and a nominal pressure of PN 16 bar. The system comprises two horizontal pipe sections measuring 2.25 m and 2.05 m and two inclined sections at 30\({}^{\circ}\), each 1.5 m long.  
Four electro-pneumatic ball valves labelled \(BV_{1}\) to \(BV_{4}\) were installed in the system. \(BV_{1}\) and \(BV_{2}\) remained open throughout all the experiments, while \(BV_{3}\) was kept closed for the entire analysis. The start-up process was initiated by opening \(BV_{4}\), which was configured to open within 0.2 s. Georg Fischer (+GF) company (Schaffhausen, Switzerland), supplied the valves.  
A hydro-pneumatic tank (\(HT\)) with a capacity of 1 m\({}^{3}\) established the initial hydraulic conditions, with the initial gauge pressure varying between 0.2 and 1.25 bar. These pressures were tested by employing a typical burdon tube pressure gauge. Figure 1 illustrates the experimental setup, highlighting the key features of the installation. The air pocket was positioned within one of the inclined pipes and across both inclined sections with larger volumes to investigate different behaviours. The air pocket sizes ranged from 0.96 to 1.36 m for Types I to IV and from 1.96 to 2.86 m for Types V to VII. At the highest point of the system, the following four scenarios were tested: (i) without an air valve (\(AV\)); (ii) with a D040 air valve (A.R.I.) featuring a 9.375 mm orifice; (iii) with an S050 air valve (A.R.I.) featuring a 3.175 mm orifice; and (iv) with an orifice size of 7 mm. Table 1 shows the seven types of behaviours identified in the filling processes.  
Figure 1: Schematic representation of filling procedures.
## 4 Simulation and Result Analysis
### System Performance Validation
#### 4.3.3 System Channel Estimation and Equalization  
Figure 11a shows the system performance with different equalization methods and different vector lengths. The diversity gain obtained by the ZF method is smaller than the MMSE method. With the increase of SNR, the diversity gain of the ZF method will decrease. On the contrary, the MMSE method can achieve good diversity gains with different vector lengths.  
Figure 11b shows the system performance with the MMSE equalization method and different channel estimation methods. Compared with least squares (LS) and DFT-based least squares (LS-DFT), the system using the OMP channel estimation method can obtain the lowest BER. It is worth noting that  
Figure 10: The relationship between \(P_{ICI}\) and relative velocity of transmitter and receiver.  
Figure 9: The relationship between the vector length and propagation range.  
OMP and LS-DFT have similar BER performance. In the OMP method, the signal recovery and signal denoising is combined. LS-DFT needs to know the noise threshold to achieve better performance.
# Accelerated Testing and Reliability of FDM-Based Structural Electronics
## 3 Results  
During the accelerated tests, the flowing current dropped below 20 mA several times in particular groups of samples. This means that instead of breaking the circuits, a fault with a different operation mechanism occurred. Therefore, the time to the first drop of the current value was used to calculate the MTTF. However, all samples were kept at elevated temperature for about 24 h, and any electrical measurements were made only after this time. Interrupting the test and excluding failed samples could lead to cooling of the chamber and disruption of the test conditions. Table 1 shows the number of samples in each group and the calculated MTTF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Number of Samples** & **Number of Samples** & **Test Time (H), h** & _MTTF, h_ \\
**Name** & **in the Group (D)** & **with Resistors** & **Test Time (H), h** & _MTTF, h_ \\ \hline A & 21 & 6 & 0.67 & 200 \\ B & 14 & 12 & 2 & 400 \\ C & 35 & 12 & 17 & 8000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of accelerated life test of structural electronics circuits. The test was conducted in 344 K (71 \({}^{\circ}\)C), where the assumed use temperature was 294 K (21 \({}^{\circ}\)C).  
Figure 2: Fabrication steps of tested structural electronics samples and their appearance on each step.  
The highest MTTF value was observed for the group with the most samples. With the samples connected in series (where the reliability of the whole series is a multiplication of the reliability of individual members), this indicates that the dominant failure process was the so-called \"infant mortality\" failure [28]. Samples that failed in groups A and B should be detected by the quality check process and eliminated from the production series. A similar conclusion can be drawn from the analysis of the resistance of every sample, as the range of initial value was more than an order of magnitude larger for selected path widths (Figure 3). Even for the best series (2 mm wide traces with resistor), the highest resistance value of the sample was more than three times the value of the sample with the lowest resistance measured. This shows the low repeatability of the fabrication process, which may have affected the spread of the MTTF results. However, despite the described problems, the obtained results are very close to the reliability of systems commonly used in industry: in automotive, the designed target of a lifetime is 10,000 h [29]. With additional effort and the use of the best currently available technologies, with quality control applied, we should achieve this target.