esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Question Results
### First Research Question
#### 4.1.1 Topic Cluster
The reviews that belong to this cluster emphasise DT characteristics [19], DT applications [18; 19], and the role of specific technology in DT creation [1]. The analysis of these publications indicates that in 2019, the primary research focus was to create a common base of DT through unified definitions, characteristics, and applications, whereas in 2021, the focus was more on the application of advanced technology to DT. **Cluster 2: Industry 4.0. ** Industry 4.0 is a buzz word in the manufacturing industry (blue cluster in Figure 8). Certain technologies applied to enhance Industry 4.0 are cloud computing, big data, and cyberphysical systems (CPS). To achieve industry 4.0, [PERSON] et al. [12] proposed a DT shop floor (DTS) with four components, (a) physical shop floor, (b) virtual shop floor, (c) shop-floor service system, and (d) shop-floor DT data. Following the track of [PERSON], [PERSON] et al. [7] proposed a conceptual model of Cyber-physical Production Systems (CPPS) which is based on DT for job scheduling. This system converges to physical and virtual spaces similar to those in reference [12]. However, the proposed model has (a) a physical layer, (b) network layer, (c) database layer, (d) model layer, and (e) application layer. In 2021, [PERSON] et al. [28] advanced the concept of DT using automatic model generation from the data. This increases the fidelity of the DT. Compared to [7; 12], [PERSON] et al. [28] focused on one of the characteristics of DT instead of the total architecture. [PERSON] et al. 's work contributes to the domain of Industry 4.0 with a robust DT. This cluster deals with several architectures and characteristics of the DT for Industry 4.0. Additionally, this cluster focuses on the convergence of physical and virtual spaces. **Cluster 3: Smart manufacturing. ** This cluster is shown in green in Figure 8. A study [6] by [PERSON] contributed to this cluster. The author of this publication used big data for preventive maintenance, in which the cloud environment plays an important role in data processing. Another contributing document is by [PERSON] et al. [12], in which the authors proposed a conceptual DT shop floor (DTS) to enhance smart manufacturing. In this case, cloud computing, cyberphysical systems (CPS), and data models play important roles. Both [6; 12] implement smart manufacturing through cutting-edge technologies. **Cluster 4: Data models. ** This cluster is coloured purple (Figure 8) and includes author keywords: data models, deep learning, and reinforcement learning. Deep learning and reinforcement learning were the most common keywords. The publication by [PERSON] et al. [29] in 2019 helped compensate for data errors in the DT model with the help of reinforcement learning (RL). This work helped increase DT fidelity. However, another study [30] used deep learning for data analytics, that is, fault diagnosis, instead of data error compensation. In 2020, [PERSON] et al. [31] used machine learning for fault prediction following this theme.
## 1 Introduction
[8] have shown that pointing as a lightweight form of gestural interaction is reliable, achieving a recognition rate of 96% in the lab. [PERSON] [9] presented hand gestures to steer infotainment systems. [PERSON] et al. [10] have shown that multimodal air gestures have advantages over conventional touch-based systems in navigating menus in the vehicle in terms of safety but with more extended task completion and mental workload. [PERSON] et al. [11] have provided the first standardization of the in-car gesture interaction space. [PERSON] et al. [12] have presented a HUD that was indirectly manipulated by pointing gestures. Although there are several studies conducted on natural interaction between drivers and their vehicles, they have identified a few drawbacks with the new interface. That may depend on people not being ready to use such advanced touchless interfaces without specific force feedback  
Figure 1: Panasonic AR HUD showcased at the Las Vegas 2020 CES [5]. or because the graphic user interface (GUI) is not designed according to specific guidelines following the interaction device. This paper presents an interaction system designed for shared autonomous vehicles. The suggested system utilizes the car's windshield as a display surface for interactive infotainment and real-time data about the surrounding environment. Integrating two gesture-based interfaces, both providing force feedback to the user's hand, is a crucial aspect of the system. One device involves simulating feedback via ultrasonic waves, while the other provides a physical surface for interaction. Through an extensive study involving twenty subjects, user experience facilitated by these interfaces was analyzed and compared. The research objective is to assess whether notable variations in the ease of use and effectiveness of standard and advanced gesture-based devices exist. The research delved into the difficulties of managing the advanced features of self-driving cars, which may rely on the windshield as a primary display. This necessitates interactive systems that can seamlessly navigate through diverse types of information, which will be achieved by detecting potential limitations and comprehending how people interact with such systems when encountering them for the first time. The results reveal the potential of the suggested system to revolutionize user engagement and interaction in autonomous transportation. It demonstrates how the system can significantly enhance the overall experience. The structure of the paper is as follows: Section 2 outlines the GUI development, the implemented interaction gestures, and the driving simulator utilized for testing; Section 3 provides a comprehensive overview of the test campaign procedure; Section 4 presents the preliminary insights gained from the test results. Finally, Sections 5 and 6 discuss and draw conclusions based on the findings.
# Optimized Analytical-Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use
## 6 The Numerical Model
### Relationship Between \(W(\mathbf{x},t)\) and the Liquid Water Content  
To quantify the volume of liquid and condensate inside the sludge treatment tank, starting from temperature \(T(\mathbf{x},t)\) and moisture \(W(\mathbf{x},t)\), it is necessary to implement an approach that integrates the following steps. Since \(W(\mathbf{x},t)\) represents the moisture content per unit volume of the sludge, to obtain the total volume of initial and residual liquid water in the tank, it will be sufficient to calculate:  
\[V_{\text{water}}^{\text{inizial}}=\int_{\Omega}W_{0}(\mathbf{x})\,d\mathbf{x},\quad V_{\text{water}}^{\text{residue}}(t)=\int_{\Omega}W(\mathbf{x},t)\,d \mathbf{x}, \tag{49}\]  
where \(W_{0}(W_{0}(\mathbf{x})\) is the initial moisture content. During heat treatment, some evaporated water condenses on the inner surfaces of the tank or accumulates as droplets. The volume of condensate can be quantified by balancing the evaporative flux and considering the fraction that turns into condensate. Then, from \(J_{e}(\mathbf{x},t)\), the total evaporated water flux is calculated:  
\[\tilde{V}_{\text{evaporate}}(t)=\int_{\Omega}J_{e}(\mathbf{x},t)\,d\mathbf{x}. \tag{50}\]Assuming that a fraction \(\eta_{t}extcondensate\) of the evaporated volume turns into condensate (depending on environmental and system conditions), the total volume of condensate is given by:  
\[V_{\text{condensate}}(t)=\int_{0}^{t}\eta_{\text{condensate}}\cdot\dot{V}_{ \text{evaporate}}(\tau)\,d\tau. \tag{51}\]  
The coefficient \(\eta_{t}extcondensate\) can be determined experimentally or modeled from the pressure and temperature inside the tank. The total volume of liquid inside the tank, \(V_{\text{liquid}}(t)\), includes the residual liquid in the sludge and the accumulated condensate:  
\[V_{\text{liquid}}(t)=V_{\text{water}}^{\text{residue}}(t)+V_{\text{condensate}}(t). \tag{52}\]  
Integrating the above terms on \(\Omega\) yields, at each time step, \(V_{\text{water}}^{\text{residue}}(t)\), \(\dot{V}_{\text{evaporate}}(t)\), and \(V_{\text{condensate}}(t)\), Obviously, it is essential to specify the flow and temperature conditions at the surface of the tank to model evaporation and condensation correctly.
# Performance Analysis of ICA in Sensor Array
## 3 Performance of ICA _vs._ Practical Factors
### Environment Noise and the Condition Number
With Equation (8), the expression can be further simplified as:  
\[\varepsilon_{k}=\left(2\sigma_{\tilde{\mathbf{x}}_{k}}^{2}/\sigma_{\mathbf{v}} ^{2}\right) \tag{17}\]  
and Equation (16) may be transformed as:  
\[\alpha_{12}=\frac{\mathbf{a_{1}^{H}}\mathbf{a_{2}}}{\left(\mathbf{a_{1}^{H}} \mathbf{a_{1}}\right)^{1/2}\left(\mathbf{a_{2}^{H}}\mathbf{a_{2}}\right)^{1/2}} \tag{18}\]  
Combining Equations (8) and (18), we have:  
\[\left|\alpha_{12}\right|=\frac{1}{2}\sqrt{2+2 cos[2\pi d\cdot(\frac{cos\phi_{2 }\cdot cos\varphi_{2}}{\lambda_{2}}-\frac{cos\phi_{1}\cdot cos\varphi_{1}}{ \lambda_{1}})]} \tag{19}\]  
With Equations (13), (15) and (19), there is:  
\[\text{SINRM}_{1}=\varepsilon_{1}\left[\frac{1}{1+\varepsilon_{2}}+\frac{4 \varepsilon_{2}}{1+\varepsilon_{2}}\cdot\frac{1}{cond^{2}(\mathbf{A})}\right] \tag{20}\]  
and when source \(2\) is strong enough (\(\varepsilon_{2}\gg 1\)), Equation (20) can be simplified with approximation as:  
\[\text{SINRM}_{1}\approx\frac{4\varepsilon_{1}}{cond^{2}(\mathbf{A})} \tag{21}\]  
Similar expression can be obtained for \(\text{SINRM}_{2}\). Equations (20) and (21) indicate that the superior limit of the separation performance via ICA algorithms is mainly determined by the SNR at the input of the separator and the condition number of the mixing matrix. Generally speaking, the performance of the algorithms would be more susceptible to the noise with larger condition number of the mixing matrix. According to Equation (13), \(cond(\mathbf{A})\geqslant 2\), and the condition number reaches its minimum, which indicates the weakest singularity of the mixing process, it is straightforward that \(cos[2\pi d\cdot(\frac{cos\phi_{2}\cdot cos\phi_{2}}{\lambda_{2}}-\frac{cos\phi _{1}\cdot cos\phi_{1}}{\lambda_{1}})]=-1\) and \(\left|\alpha_{12}\right|=0\). Then according to Equation (15), the \(\text{SINRM}_{1}\) reaches its maximum and equals \(\varepsilon_{1}\) regardless of \(\varepsilon_{2}\), which indicates that the SINR at the output equalsthe SNR at the input of the separator. Since this is theoretically the best result possible, \(cond(\mathbf{A})=2\) should be the ideal case that is desired in practice. On the contrary, the case that should be avoided is the singular mixtures, meaning the situation where \(cond(\mathbf{A})=\infty\). This is because then the multi-channel ICA problem would degenerate into a single-channel one, which the traditional ICA algorithms cannot handle, and thus, the separation may come to a critical failure in applications.
## 1 Introduction
For the task of the personalized recommendation of recipes, we emphasize the exploration of multiple-level views that are explored to make full use of higher-order structural information in heterogeneous graphs and local collaborative and semantic information to learn higher quality node embedding representations. Specifically, first we regard the full graph as multiple views in two levels, global-level and local-level, as shown in Figure 1. Compared with the traditional contrast learning corruption or dropping to generate a contrast view, this complementary view approach preserves the complete structural information to the maximum extent and makes the learning node representation more fine-grained. In the local-level view, to better capture the local collaborative and semantic information, we use different aggregations to learn the embedding representation of user nodes and recipe nodes containing local structural information in the user-recipe graph and recipe-property graph. In the global-level view, we use a path-aware approach to learn node embedding by passing messages along multiple paths from the network structure view for the purpose of capturing higher-order structural information. Then, we introduce self-supervised cross-view contrastive learning so that the information of multiple views collaboratively supervise each other and learn nodes with high quality final embedding. Finally, the recipes satisfying personalized preferences are matched with users through joint training and model prediction functions to complete the recommendation task. The main contribution of this paper can be as follows:* We emphasize the importance of high-order relational information and complex personalized requirements in recipe recommendation tasks, and introduce the idea of graph comparison learning for recipe recommendation. To our knowledge it is the first attempt on this problem. * We propose multi-level knowledge-aware contrastive learning network (MKCLN), a heterogeneous graph learning model for personalized recipe recommendation. The model leverages the collaborative, semantic, and higher-order structural information of heterogeneous graphs by constructing multiple views at both global and local levels. Self-supervised cross-view contrastive learning is applied to multiple views to obtain comprehensive high-quality node embeddings and personalized recipe recommendations. * We conducted extensive experiments on two different food datasets containing different national preferences, and the results demonstrate the effectiveness and sophistication of MKCLN.
## 3 Probability of Chip Error in Coherent O-QPSK  
In this section, we consider coherent communication and evaluate the chip error probability when there is one interferer with an unknown carrier phase and symbol timing. As the experiments in [16, 18, 19, 20] suggest, we assume that the signal of the interferer is additive, and the probability of error is independent of the number of interferers. As a consequence, we will consider one interfering signal. Under such an assumption, the received O-QPSK modulated signal, in the time interval \(-T_{c}\leq t<T_{c}\), can be written as:  
\[r(t)=\Re\Big{[}\tilde{s}_{C}(t)\exp\Big{(}j(2\pi f_{c}t+\phi_{C})\Big{)}\Big{]} +\Re\Big{[}\tilde{s}_{I}(t)\exp\Big{(}j(2\pi f_{c}t+\phi_{I})\Big{)}\Big{]} \quad-T_{c}\leq t<T_{c} \tag{6}\]  
where \(\tilde{s}_{C}(t)\) and \(\tilde{s}_{I}(t)\) are the complex envelopes of the useful and interferer signals and \(\phi_{C}\) and \(\phi_{I}\) are the carrier phases of the useful and interferer signals, respectively. There is no phase difference between the useful transmitter and the receiver, since we are assuming coherent demodulation. Therefore \(\phi_{C}=0\), but the carrier phase of the interferer, \(\phi_{i}\), is unknown. After some algebra, Equation (6) can be expressed as:  
\[r(t)=\Re\Big{[}\tilde{r}(t)\exp(j2\pi f_{c}t)\Big{]}\quad-T_{c}\leq t<T_{c} \tag{7}\]  
where the complex envelope is:  
\[\tilde{r}(t)=\tilde{s}_{C}(t)+\tilde{s}_{I}(t)\exp(j\phi_{I})\quad-T_{c}\leq t <T_{c} \tag{8}\]  
In the case of coherent demodulation, the I and Q components should be sampled at the instance corresponding to the maximum signal amplitude; hence for a sequence of chips, the I-phase should be sampled at the instants \(t_{I_{n}}=2 nT_{c}\) (see Equation (2)) and the Q-phase at the instants \(t_{Q_{n}}=(2n+1)T_{c}\) (see Equation (3)), where \(n=0,1,2,\ldots\). This results in sampling the I and Q components of the useful signal at the indicated sampling instances in Figure 1a and Figure 3a. Due to the delayed sampling between the I and Q components, we will consider a Q-I constellation plane, where the Q component is delayed by \(T_{c}\) seconds with respect to the I component. We named the resulting constellation plane Q\({}_{d}\)-I to emphasize the delay. In the next two subsections, we derive the CER for the received signal in Equation (8), as a function of SIR, considering two cases: without and with pulse shaping. Thermal noise is neglected in the analysis, as we want to investigate solely the effect of interference on CER. The effect of noise will be considered in Section 5 to compare the analysis with the experimental results.
## 4 Results and Discussion
### Use of Tools Supporting the Improvement of Efficiency in the Analyzed Enterprise
#### 4.5.1 Method 5 WHY  
The analysis of the causes of problems using the 5 WHY method allowed for the accurate identification of sources of extended reliability and other restrictions negatively affecting production efficiency. Thanks to this tool, it was possible to carry out a multi-level recognition of problems and direct activities to their elimination. The following is an example of using the 5 WHY method in practice:  
* Problem: Extended rearranging time for the product A.
* Why? Too time-consuming to machine settings.
* Why? No standardization of procedures.
* Why? There are no clearly defined instructions for operators.
* Why? Insufficient training.
* Why? Inadequate training program.  
The analysis showed that the main problems were inefficient procedures and insufficient preparation of machine operators. Lack of standardization and detailed instructions resulted in various work practices, which extended the process of reliance and reduced performance.
## 6 Carboxymethyl Cellulose (CMC)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Structure** & **Chemical** & **Fabrication** & **Bioactive** & **The Test Model** & **Significant Findings** & **Ref.** \\ \hline \hline \multirow{6}{*}{CMC/PEO} & \multirow{6}{*}{Co-blending} & \multirow{6}{*}{Needleless electrospinning} & Plant extracts: & & & \\  & & & Achillea & _S. aureus_ & & \\  & & & millefolium & _E. coli_ & & \\ \cline{3-6}  & & & Needleless & Calendula & Human skin & Acceptable \\  & & & & officials & fibroblasts (SF) & antibacterial effect \\ \cline{3-6}  & & & & Matricaria & Human skin & Good biocompatibility & \\ \cline{3-6}  & & & chamomilla & keratinocytes (HACAT) & & & \\ \hline \hline \multirow{6}{*}{CMC/PAV} & \multirow{6}{*}{Co-blending} & \multirow{6}{*}{Electrospinning} & \multirow{6}{*}{SSD} & \(S\). aureus & & \\  & & & & _E. coli_ & Significant & \\ \cline{3-6}  & & & & _P. aeruginosa_ & antibacterial effects & \\ \cline{3-6}  & & & & & New Zealand & Proper biocompatibility & \\ \cline{3-6}  & & & & white rabbits & Accelerated wound-healing & \\ \cline{3-6}  & & & & & fibroblast-like cells & & \\ \hline \hline \multirow{6}{*}{CMC/PU} & \multirow{6}{*}{Co-blending} & \multirow{6}{*}{_Malora sylestris_} & \multirow{6}{*}{_S. aureus_} & Significant antibacterial and & \\  & & & & _E. coli_ & Cell proliferative effect & \\ \cline{3-6}  & & & extract & Mesenchymal stem cells & Reduced acute and chronic & \\ \cline{3-6}  & & & & & Masenvhral stem cells & Inflammations & \\ \cline{3-6}  & & & & Male Wistar rats & Promoted wound closure & \\ \cline{3-6}  & & & & & Increased collagen & \\ \cline{3-6}  & & & & deposition and & & \\ \cline{3-6}  & & & & neo-vascularization & \\ \hline MMA- & \multirow{2}{*}{\
\begin{tabular}{c} Graft copolymerization \\ \end{tabular} } & \multirow{2}{*}{Casting} & Diclofenac sodium & human skin fibroblasts & \multirow{2}{*}{
\begin{tabular}{c} Insignificant adverse effects \\ on cells \\ \end{tabular} } \\ EHA-CMC & & & Capsaicin & (HFB4) & Accelerated wound-healing \\ PVA & & and EHA & & Gentamicin & & from 21 to 7 days \\ \hline \multirow{2}{*}{CMC/Ag NPs} & \multirow{2}{*}{Co-blending} & \multirow{2}{*}{Electrospinning} & \multirow{2}{*}{Ag NPs} & \(S\). aureus & Uniform distribution of \\  & & & & _E. coli_ & nanoparticles and complete \\ \cline{3-6}  & & & & antibacterial efficiency & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CA-based biomaterials fabricated and applied for wound-healing applications.
## 5 Production of Carotenoids by Haloarchae
### Type, Content and Biosynthesis of Haloarchaeal Carotenoids
More recently, studies carried out in _Haloarcula japonica_ have clearly identified that the genes named _c0507_, _c0506_, and _c0505_ encoded a carotenoid 3,4-desaturase (CrtD), a bifunctional lycopene elongase and 1,2-hydratase (LyeJ), and a C\({}_{50}\) carotenoid 2\",3\"-hydratase (CruF), respectively. The above three carotenoid biosynthetic enzymes catalyse the reactions that convert lycopene to bacterioruberin in _Haloarcula japonica_[103]. Figure 2 compares the biosynthesis of carotenoids in photosynthetic organisms and in haloarchaea (on the basis of the results reported from _Halobacterium_, _Haloarcula_ and preliminary evidence from _Haloferax_ genomic analysis).
# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
###### Abstract  
Many highly contagious infectious diseases, such as COVID-19, monkeypox, chickenpox, influenza, etc., have seriously affected or currently are seriously affecting human health, economic activities, education, sports, and leisure. Many people will be infected or quarantined when an epidemic spreads in specific areas. These people whose activities must be restricted due to the epidemic are represented by targets in the article. Managing targets by using targeted areas is an effective option for slowing the spread. The Centers for Disease Control (CDC) usually determine management strategies by tracking targets in specific areas. A global navigation satellite system (GNSS) that can provide autonomous geospatial positioning of targets by using tiny electronic receivers can assist in recognition. The recognition of targets within a targeted area is a point-in-polygon (PttInPy) problem in computational geometry. Most previous methods used the method of identifying one target at a time, which made them unable to effectively deal with many targets. An earlier method was able to simultaneously recognize several targets but had the problem of the repeated recognition of the same targets. Therefore, we propose a GNSS coordinate recognition algorithm. This algorithm can efficiently recognize a large number of targets within a targeted area, which can provide assistance in epidemic management.  
GNSS; recognition; point-in-polygon; computational geometry; infectious disease; isolate Article  
An Efficient GNSS Coordinate Recognition Algorithm  
Epidemic Management