esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Experimental Results and Discussions  
Figure 6 shows the experimental result of the PCLG vortex beam's CSD amplitude and phase with medium coherence width (\(\sigma_{\mathrm{g}}=0.4\omega_{0}\)), TC \(=-2\), and different cylindrical lenses angles. When the cylindrical lenses angle was changed, the CSD amplitude and phase differed. Stripes were observed in the amplitude, and their quantity is expressed as N = 2 \(\left|\left|\right|\right|+1\). As predicted from the simulation (Figure 4), dumbbell-like phase patterns were observed, particularly at 30\({}^{\circ}\) (Figure 7b,f). The number of \"dumbbell pieces and handles\" obeyed N = 2 \(\left|\left|\right|\right|+1\). Compared with the amplitude distribution, the TC magnitude can be determined more intuitively from the phase patterns.  
Finally, we experimentally measured the CSD amplitude and phase of the PCLG vortex beam with \(l\) = +1 and \(l\) = +2. The coherence width was \(\sigma_{\mathrm{g}}=0.4\omega_{0}\) and the cylindrical lenses angle was 30\({}^{\circ}\). Figure 8 shows that the relationship between the number of stripes and the TC can be expressed as N = 2 \(\left|\left|\right|\right|+1\), which is consistent with simulation results shown in Figure 5. In the phase distribution, the number of 'dumbbell piece and handle' in the phase pattern also obeyed N = 2 \(\left|\left|\right|\right|+1\) for different TCs. Quantitative relationships  
Figure 6: Schematic illustration of the experimental CSD measurement of a partially coherent Laguerre–Gaussian vortex beam. BE, beam expander; L1–L3, thin lenses; RGGD, rotating ground-glass disk; CA, circular aperture; SLM1, transmissive spatial light modulator; SLM2, reflective phase-only spatial light modulator; RM, reflecting mirror; GAF, Gaussian amplitude filter; BS, beam splitter; CL1, CL2, cylindrical lenses; CCD1, CCD2, charge-coupled devices.  
Figure 7: Experimental results for the CSD amplitude and phase distribution with medium coherence width (\(\sigma_{\mathrm{g}}=0.4\omega_{0}\)), TC \(=-2\), and different included angles of cylindrical lenses. CSD amplitude distributions for angle equal to (**a**) 15\({}^{\circ}\), (**b**) 30\({}^{\circ}\), (**c**) 45\({}^{\circ}\) and (**d**) 60\({}^{\circ}\). The arrows mark the positions of the stripes in the CSD amplitude. CSD phase distributions for angle equal to (**e**) 15\({}^{\circ}\), (**f**) 30\({}^{\circ}\), (**g**) 45\({}^{\circ}\) and (**h**) 60\({}^{\circ}\). The arrows mark the positions of the “dumbbell pieces and handles” in the CSD phase.  
based on the CSD amplitude or phase distributions allow one to determine the magnitude and sign of the TC in the PCLG vortex beam.
## 1 Introduction
### Contribution of the Article  
The article not only includes and discusses the literature review and current state of the art in depth, but it also encourages the reader to choose a specific approach by outlining the many possibilities that have been used in the past. The article has the following novelties:  
1. Firstly, an optimal controller comprising of LQR is used in Full State Feedback configuration, LQR with a Luenberger Observer and LQG with Kalman Filter for noise attenuation enhancing the stability of the RUAVs. Our methodology is novel in a sense that it does not only tune the feedback gain by genetic algorithm, but also the gains of the Kalman filter which outperforms the \"GA-Hybrid\" approach;
2. Secondly, Metaheuristic Algorithms, namely PSO and GA, are used to optimize the selecting controller gains;
3. Finally, a Reinforcement Learning foundation is outlined, whereby an agent could be used to replace other control structures.  
The remainder of this paper is structured as follows: Section 2 covers an extensive review of the state-of-the-art literature. The methodology of each state-of-the-art technique is discussed in Section 3. Section 3.3 is used to explain the current controller structures of Optimal controllers. The fitness function defined in Section 3.4 sets the evaluation criteria for the Section 3.5 (Genetic Algorithm). Tuning both the feedback gains and Kalman filter by a fully data-driven method is novel. Section 4 defines the simulation environment for each controller used. Section 5 reviews the results obtained from each controller tested, with Section 6 covering the future scope of research where foundational knowledge is outlined for using a Reinforcement Learning method using an agent as a potential controller for the RUAV. Finally, Section 7 provides a conclusion to this work. The summary of this article is depicted in the Figure 1, where the overall summary of each respective section is shown.
# Extracellular Vesicles for Regenerative Medicine Applications
## 4 Clinical Development of EVs  
### Companies Developing EV Products  
Despite the relatively early stage of EV therapy and diagnostics, EV technology has generated considerable traction in the pharmaceutical industry over the past decade. For example, more than 2000 patents related to EVs (Google Patents, Query in June 2022) have been filed since 2010. The companies involved in the development of EV products representsmall- to medium-sized enterprises. These companies are generally spin-offs from larger companies and the majority are currently in the early process of raising funds, developing technology, and working toward becoming leaders in the growing market (Table 1). In addition, several multinational pharmaceutical companies such as Pfizer, AstraZeneca, Roche, and Merk are also expanding their research and development portfolios to focus on EV therapies. Although no leaders have established market dominance in this growing industry, several companies have been making considerable progress.  
Codiak Bioscience (Cambridge, MA, USA) is a company dedicated to EV-based diagnostics and therapeutics. Specifically, the company is interested in using cell culture-derived EVs for immune activation in the treatment of cancer. Codiak Biosciences filed 11 patents for exosome manufacturing and purification in 2021 and has experienced an exponential growth in revenue over the last few years, reaching a total of $22.9 million in 2021 (Pitchbook, June 2022). Evox Therapeutics (Oxford, UK) has developed modified EVs for drug delivery with a special interest in siRNA and mRNA delivery for protein replacement or gene therapy. This company has accrued $155 million in venture capital support. ExoCoBio (Seoul, South Korea) has developed EV-based therapies for cosmetic and skin regeneration applications and has submitted five patents related to this technology in 2021. Other companies like Aegle Therapeutics (Woburn, MA, USA), Panacea Pharmaceuticals (New Delhi, India), and Exopharm (Melbourne, Australia) are in early funding stages and are following similar strategies to the companies mentioned above through an approach to protecting intellectual property and evaluating various potential applications for their own EV technology [151].
## 2 Persisters of Foodborne Pathogens
### Persister Types and Mechanisms of Formation  
Slow growth/no-growth is a clear characteristic of persisters, and conditions that lead to bacterial growth arrest strongly promote persister formation. Growth arrest occurs in energy-limited conditions. [PERSON] et al. [52] discovered that stationary phase _S. aureus_ cells, which displayed an intracellular ATP drop, showed a 325-fold increase in persister formation, and a 100-1000 times higher level of antibiotic tolerance compared to their exponential phase counterparts. Similarly, [PERSON] et al. [53] demonstrated that a low level of ATP is the underlying explanation of _E. coli_ persister production. However, [PERSON] et al. [54] reported that _S. aureus_ persisters formed in a stationary phase are associated with low membrane potential and ATP depletion is merely an indirect outcome. In addition, nutritional deficiency inside macrophages is regarded as a cause of intramacrophage _Salmonella_ persister formation [55]. Long-term starvation results in sporulation in spore-forming _B. cereus_[56]. Except for energy limitation, artificial/stress-induced growth arrest also leads to a higher persister level. Deficiency of heme leads to higher _S. aureus_ persister levels under energy stress and antibiotic treatment, which may be due to the important role of heme in the respiration chain and energy production [57]. Moreover, signal pathways depending on guanosine pentaphosphate/tetraphosphate (ppGpp) and Toxin-antitoxin systems (TA systems) lead to growth arrest due to various metabolism-halting mechanisms (summarized in Sections 2.3.1 and 2.3.2), making them the most important molecular mechanisms of persister formation. [PERSON] et al. [58] argued that slow growth, irrespective of the way this is induced, is the ultimate indicator of persister formation, at least in an \(S\). Typhimurium population. In general, even though the underlying explanation of growth arrest-induced persisters is still confusing, growth arrest is not only one of the most distinctive features, but also a controversial inducer of persisters. It should be noted that the reduction of growth and metabolic activity is not a sufficient trigger for persister formation as not all \"slow-growing cells\" show persistence [48]. In the next paragraphs, we will discuss the details of the different ways to develop a persister state.
## 3 Results and Discussion in General
### Discussion in General
Based on the analysis of the results of the parametric study of the energy potential of the individual technical solutions of the envelope panels with integrated energy-active elements, it can be concluded that the increase in heat losses due to the location of the tubes in the structure closer to the exterior for VARIANT II, semi-accumulation heating (TABS system), and VARIANT III, accumulation heating, in relation to VARIANT I, direct heating, negligible, below 1% of the total delivered heat flux, Figure 30.  
The direct heat flux to the heated room is 89.17% for direct heating, VARIANT I, 73.36% for semi-accumulation heating (TABS system), VARIANT II and 58.46% for accumulation heating, VARIANT III of the total delivered heat flux, Figure 30.  
Figure 30: Heat flux analysis for the investigated variants of mathematical-physical perimeter reinforced concrete panels. q\({}_{\text{i}}\)—heat flow towards the interior (W/m\({}^{2}\)), q\({}_{\text{e}}\)—heat flow towards the exterior (W/m\({}^{2}\)), \(\theta_{\text{i}}\)—internal calculation temperature (\({}^{\circ}\)C), \(\theta_{\text{e}}\)—outdoor calculation temperature (\({}^{\circ}\)C), \(\theta_{\text{p}}\)—interior surface temperature (\({}^{\circ}\)C), \(\theta_{\text{p}}\)—exterior surface temperature (\({}^{\circ}\)C), 1–4—layer number. [Author: [PERSON]]. For simplicity, VARIIANT I does not consider heat accumulation for the panel. For the panel design (TABS system), VARIIANT II represents 14.84% and VARIIANT III up to 29.86% heat accumulation of the total delivered heat flux, Figure 30.  
Variants II and III appear promising in heat/cool accumulation with an assumption of lower energy demand (at least 10%) than for low inertia walls. The potential application of the work lies in the presented variants of combined building-energy systems of buildings using renewable energy sources. Compared to fossil fuel-based heat/cooling sources, these technical solutions meet the conditions of energy security, self-sufficiency, and carbon neutrality of European Union buildings. Perimeter panels with integrated energy-active elements represent a progressive alternative to traditional energy systems for heating, cooling, and domestic hot water preparation, in particular, due to their multifunctional application capability from the use of both source and end elements of energy systems. We plan to extend our research to date with dynamic computer simulations to optimize the design and composition of panels with integrated energy-active elements to build experimental buildings. Moreover, we intend to optimize the design of energy (solar) roofs (ESR), ground heat storage (GHS), active thermal protection (ATP), and their cooperation in different modes of operation of energy systems, with an emphasis on the use of renewable energy sources (RES) and waste heat.
## 4 Research Findings
### Agility Constructs
#### 4.1.4 Treatment Agility  
Among the selected papers, three investigated the concept of treatment agility, wherein agility construct transitions from the organizational level to the process-entity level. [PERSON] and [PERSON] [44] introduce the notion of care delivery agility, defining it as the degree to which clinicians exhibit proficiency in clinical diagnosis, enabling agile decision-making. The authors emphasize the critical importance of timely and accurate treatment within the modern healthcare ecosystem, where lives are at stake [44]. Furthermore, treatment agility is regarded as a dynamic capability for healthcare organizations to enhance their competitive advantage, signifying an ability to adapt to volatile, dynamic, and constantly evolving service requirements [48]. In a distinct vein, [PERSON] et al. [69] examined patient agility, characterizing it as a dynamic capability that empowers hospital departments to effectively sense and respond to patient needs. In the context of medical treatment, agility is centered on rapid adaptability and responsiveness to patient requirements.
# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
## 2 Materials and Methods
### Data Flow  
The workflow and the flow of the data acquired in the field are described in the following steps (Figure 2):  
* The sensors are wire-connected to the End-nodes. The nodes request the measurements at certain time intervals, according to their programming, and record the values. The values are encoded in data packets and temporarily saved in the node's memory.
* The data packets follow a certain data flow path. The end-nodes send their data packets directly to the Coordinator via 868 MHz radiofrequency, which processes the information and reforms the messages accordingly, converting them into the appropriate data format.
* The Coordinator sends the reformed data packets to the cloud via 4G cellular network.
* An application programming interface (API) was developed to establish the communication of the wireless agricultural network with the cloud server making the datasets immediately available to the user.
* The user can access the database via internet, and download or preview the data in real time (Figure 2).  
Figure 1: System topology and communication.
# Application of Spectroscopy in Food Analysis: Volume II
## 4 Classification Approaches and Spectroscopy for Fraud Detection  
In this section, the works where spectroscopic data have been analyzed using different classifiers for the determination of possible frauds will be summarized. Within this Special Issue, three papers belong to this field. One of them is the work published by [PERSON] et al. [10], where the botanical origin of edible oils was investigated. Oil samples belonging to eight different classes were analyzed by FT-IR and UV-Vis and then classified (according to the botanical origin) by PCA, PLS-DA, cluster analysis, heatmap, random forest analysis, and ANOVA post hoc analysis. Eventually, the authors concluded that PLS-DA, cluster analysis, and random forest achieved the highest predictive ability.  
The other two works, proposed by [PERSON] et al. and [PERSON] et al., exploit IR and NIR spectroscopy for adulteration detection in agro-foods. In the former paper [11], the investigation is focused on a PGI Italian dry bell pepper sold ground and used as a seasoning. The work aims at developing a tool that determines whether a product consists of pure Senise bell pepper or has been adulterated with similar ground foods (for example,paprika). For this purpose, mixtures of Senise bell pepper and paprika (at different concentrations) were prepared with the intent of mimicking adulteration. Eventually, samples were analyzed using FT-IR and FT-NIR spectroscopies; then, multi-block classification models (sequential and orthogonalized partial least squares linear discriminant analysis and sequential and orthogonalized covariance selection linear discriminant analysis) were used to discriminate blends from the pure Senise bell pepper spice. Both procedures achieved successful results in external validation, correctly classifying all the test samples, and confirming the suitability of the proposed strategies.  
[PERSON] et al. [12] investigated the adulteration of rice and developed a NIR-based tool for its detection. On this product, adulteration can be performed by mixing a high-valued cultivar with rice with a lower market value. In particular, blends of high-commercial-value rice with other cheaper ones were prepared and analyzed using NIR spectroscopy. Subsequently, a discriminant classifier, PLS-DA, and a class-modeling one, SIMCA, were applied for the identification of the adulterated mixtures. The outcome of the analysis revealed that, contrary to SIMCA, PLS-DA was suitable for the purpose, achieving a high accuracy on a validation set of samples.
# Advantages of Corrosion-Resistant Overlay Welding on Steel S355J2N
## 2 Materials and Methods  
### Materials  
From a welding perspective, it is very important that welding of structural steels does not necessarily require preheating, due to their low carbon content (C < 0.2%), thus eliminating the risk of hardened weld structures and the associated risk of cracking [19].  
For our experiments, S355J2N steel was used as base material. The characteristics of hot-rolled, unalloyed structural steels are summarized in the EN 10025-2 standard [20]. Steel grades marked with the letter 'S' have low carbon content and contain a maximum of 0.55% silicon. The chemical composition of the S355J2N steel grade, which is important for the research, can be found in Table 1.  
The application of each layer during the overlay welding process was carried out based on the professional recommendations of the Kobe Steel Ltd. (Chuo, Japan) Welding Handbook [17]. For the buffer layer (covering layer), we selected the Voestalpine (Dusseldorf, Germany) austenitic stainless rutile flux-cored wire of type T 23 12 2 L P/E309 LMo.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Steel Name**} & **Steel** & **C** & **Si** & **Mn** & **P** & **S** & **Cu** & **Other** \\  & **Number** & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** \\  & **Max.** & **Max.** & **Max.** & **Max.** & **Max.** & **Max.** & **Max.** & **Max.** \\ \hline \multirow{2}{*}{**S355J2N base metal**} & \multirow{2}{*}{1.0577} & 0.20 & 0.55 & 1.60 & 0.025 & 0.025 & 0.55 & - \\  & & 0.16 & 0.25 & 1.18 & 0.013 & 0.013 & 0.01 & - \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of S355J2N.  
For the other layers, we used the T 19 12 3 L/E316L type flux-cored wire with D = 1.2 mm (Table 2).  
The filler materials selected for the experiment are chemically resistant and qualified as 'highly suitable for welding'. Thanks to their extremely low carbon content, they cannot be hardened, meaning that no significant hardening is expected in the heat-affected zone (HAZ), and there is no expected significant grain coarsening as well.  
For shielding gas in the welding process, we used CORGON gas (82% Ar + 18% CO\({}_{2}\)) to ensure arc stability, minimize spattering, and provide effective protection against oxidation.
## 5 Results and Discussion
### End of Life (EOL) Prediction
#### 5.1.1 EOL Prediction (First Test)  
The first test involved a cell cycled at 24 \({}^{\circ}\)C using a 2 A discharge current until the voltage reached the EOD voltage of 2.7 V, corresponding to the cell B005 of the dataset. The blue line in Figure 9 depicts the cell capacity of each cycle, the red line is the filtered observation from the particle filter, the green line is the EOL threshold of 20% capacity loss, and the dotted vertical yellow line denotes the real EOL cycle, which is cycle 76 in this case. The predictions were performed at cycles 20, 50, and 70 and a scaled PDF of the possible EOL cycles was obtained. The lightest blue PDF denotes the earliest prediction, whereas the darker one is the most recent prediction. It is proven that the prediction is  
Figure 8: Data fields for charge and discharge cycles.  
Figure 7: Data structure.  
more accurate as the EOL cycle approaches; this prediction error will be analyzed in the later sections of this paper.
## 4 IoMT Authentication Taxonomy
### Authentication Architectures  
Authentication in IoMT systems depends on either a centralized or decentralized architecture. Centralized authentication requires a centralized server to identify and authenticate the system entities. In contrast, the distributed architecture depends on multiple distributed nodes to accomplish the authentication process. Both the centralized and distributed architecture can be a flat or multi-level architecture. The flat architecture means the nodes are authenticated by authentication servers with the same roles. In a multi-level architecture, the authentication process is performed by authentication servers with different roles, according to their level of communication. That implies that the server at the lower level is used for authenticating nodes at the lower level. Figure 9 shows the authentication architectures for IoMT systems.  
Many researchers proposed robust, centralized authentication schemes by authentication servers in cloud-based IoMT systems [25; 44; 45; 46]. They adopted a flat, centralized architecture where all nodes are authenticated through cloud-based authentication servers. Undoubtedly, the centralized server may have a high latency, which is unacceptable for time-sensitive IoMT systems [47; 48]. Therefore, the flat, centralized architecture is not applicable for very large IoMT systems. In some studies [49; 50; 51; 52], the authentication process is delegated to a smart gateway, near the IoMT devices, to identify and authenticate them.  
Figure 8: Authentication levels.  
Figure 9: Authentication architectures.  
Similarly, [PERSON] et al. [53] and [PERSON] et al. [54] adopted the fog computing concept to establish a fog node responsible for authenticating IoMT devices. [PERSON] et al. [55] proposed a user authentication method by decentralized gateways to release the medical devices from authentication activities. A large number of studies [49; 50; 51; 52; 53; 54; 55] are based on a multi-level centralized authentication architecture.  
Multiple nodes are responsible for authenticating other nodes in the distributed authentication architecture. The concept of a distributed authentication architecture mainly depends on the blockchain technology [40; 56; 57; 58]. [PERSON] et al. [57] presented a novel architecture for authentication in a blockchain-based IoMT system using a probability technique. [PERSON] et al. [59] developed an authentication framework for IoMT environment using blockchain technology. It is called blockchain-based authenticated-key management (BAKMP). The framework ensures secure key-agreement for cloud servers, personal servers, and IoMT devices. [PERSON] et al. [57] and [PERSON] et al. [59] used the flat distributed architecture because they adopted one blockchain platform for all of the IoMT nodes. In comparison, [PERSON] et al. [58] proposed an authentication scheme called BEdgeHealth, which relies on a multi-level blockchain architecture. A local blockchain is deployed for authenticating the IoMT under the same cluster, and a global blockchain is deployed for authenticating the IoMT at different clusters.