esa-sceva/satcom-chunk-collection huggingface dataset:
_Appl. Sci._**2015**, \(5\), 1715-1734; doi:10.3390/app5041715  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Electromagnetic Acoustic Transducers Applied to High Temperature Plates for Potential Use in the Solar Thermal Industry +  
Footnote †: footnotemark:  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1,2, [PERSON] 1, [PERSON] 1,2,*, [PERSON] 1 and [PERSON] 1**  
Footnote 1: footnotemark:  
* Brunel Innovation Centre (BIC), Brunel University, Cambridge CB21 2 AL, UK; E-Mails: [EMAIL_ADDRESS] (L.C.); [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (V.K.); [EMAIL_ADDRESS] (W.B.); [EMAIL_ADDRESS] (C.S.)
* TWI Ltd., Granta Park, Great Abington, Cambridge CB21 6 AL, UK
* This paper is an extended version of paper published in the 6 th International Conference on Emerging Technologies in Non-destructive Testing (ETNDT6), Brussels, Belgium, 27-29 May 2015.
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] (M.G.), [EMAIL_ADDRESS] (T.-H.G.); Tel.: +44-778-745-5554.  
Academic Editor: [PERSON]  
_Received: 24 September 2015 / Accepted: 4 December 2015 / Published: 11 December 2015_  
**Abstract:** Concentrated Solar Plants (CSPs) are used in solar thermal industry for collecting and converting sunlight into electricity. Parabolic trough CSPs are the most widely used type of CSP and an absorber tube is an essential part of them. The hostile operating environment of the absorber tubes, such as high temperatures (400-550 \({}^{\circ}\)C), contraction/expansion, and vibrations, may lead them to suffer from creep, thermo-mechanical fatigue, and hot corrosion. Hence, their condition monitoring is of crucial importance and a very challenging task as well. Electromagnetic Acoustic Transducers (EMATs) are a promising, non-contact technology of transducers that has the potential to be used for the inspection of large structures at high temperatures by exciting Guided Waves. In this paper, a study regarding the potential use of EMATs in this application and their performance at high temperature is presented. A Periodic Permanent Magnet (PPM) EMAT with a racetrack coil, designed to excite Shear Horizontal waves (SH\({}_{0}\)), has been theoretically and experimentally evaluated at both room and high temperatures.  
**Keywords:** Electromagnetic Acoustic Transducer (EMAT); guided waves; solar thermal plants; non-destructive testing; high temperatures
## 1 Introduction
Since the receiving terminal in the shadow area can only receive the optical power of some indoor light sources, the existence of a half shadow can affect the uniformity of indoor illumination and the distribution of the signal-to-noise ratio, making it difficult for users in different locations to obtain similar lighting effects and communication quality. Addressing the shadow effect of indoor human obstacles, this paper establishes two different types of shadow occlusion models for the indoor VLC channel and then analyzes their influence on the indoor VLC system as a function of the mean square errors of illuminance and signal-to-noise ratio as well as the interrupt probability. The purpose of this paper was to systematically analyze the shadow effect of obstacles on the performance of indoor VLC systems so as to deeply characterize their mechanism.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### 3.3 Model's Performance Monitoring (MPM)  
As patterns in the incoming data change dramatically over time, the model will not be able to perform as expected (which is referred to as model staleness) and must be re-trained. Therefore, having a close look at the model's performance helps in preventing decreasing effectiveness of models. The metrics summarized in Table 4 point out various problems which have to be evaluated before and during deployment for production.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Metric** & **Remarks** \\ \hline Data schema & This metric aims to check whether the data schema in the inference stage is identical to what was there during training. It is an important measure of data integrity. By assuring such referential integrity, a certain degree of data quality, as well as assumptions on data completeness can be observed [15]. \\ \hline Data consistency & The metric checks if the data encountered in production is within the expected range. It could also be used for catching data type or format errors. By comparing the amount of records to previously known states of the dataset, another consistency factor can be measured [15]. \\ \hline Data quality & The metrics checks if the quality of data is similar to what the model was trained on. If the quality is changed, then that will have a direct effect on the model’s performance. Therefore, the different data quality dimensions must be assured in every stage of the data lifecycle [17]. \\ \hline Data drift & The metric aims to examine changes in the distribution of data. It checks whether or not the input data’s statistical structure (changes in input feature space) has changed over time. \\ \hline Outliers & Many algorithms are sensitive to outliers and perform poorly on such data points. Outliers could bring additional knowledge about the use case and therefore should be investigated in detail. When comparing the incoming information to a previously known reference or mathematical rule, the validity of data can be measured [15]. \\ \hline Training/Serving skew & The aim is to check if there is a mismatch of data between data acquired for training and what is received in production. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metrics for input data monitoring.
# A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets
## 5 Discussion  
Most previous studies on collision risk have focused on numerical evaluations, lacking discussion on areas with imminent collision risks. This paper applies Petri nets to discretize airport operations, presenting the collision risks across different areas and using color mapping for intuitive visualization. This method helps apron controllers better manage the safety of apron operations by allowing them to more effectively identify aircraft with collision risks and their corresponding potential areas. Additionally, the paper offers an approach for re-planning aircraft taxi routes with the goal of reducing collision risks. This concept can serve as a reference for future studies on path planning and provide valuable insights for apron personnel when directing aircraft taxiing.  
During aircraft operations, human factors can also impact the performance, as the different working conditions of pilots may affect the aircraft's operational status. Therefore, future research could incorporate the influence of human factors into the existing model  
Figure 17: Aircraft 2 route planning diagram.  
Figure 18: Two day comparison diagram of aircraft path planning to reduce collision risk.  
to make it more comprehensive. Additionally, specialized vehicles play a significant role in apron operations. These vehicles not only operate around the apron but also perform tasks near aircraft, leading to potential collision risks between vehicles and aircraft. Future studies could extend the current model to include the collision risks posed by specialized vehicles. Moreover, since the starting and ending points of an aircraft's taxiing route are not random but determined by specific requirements related to airport arrangements and airline needs, future models could consider these actual requirements during route planning to reduce collision risks, thereby simulating more realistic scenarios.
# Raman Spectra of ZrS\({}_{2}\) and ZrSe\({}_{2}\) from Bulk to Atomically Thin Layers
## 2 Materials and Methods  
Large single crystals of ZrS\({}_{2}\) and ZrSe\({}_{2}\) were grown by chemical vapor transport (CVT) and characterized by powder X-ray diffraction and energy-dispersive X-ray spectroscopy (see Appendix A for further information).  
Figure 1: Representation of the crystal structure of ZrX\({}_{2}\). Chalcogen atoms are represented in yellow and zirconium atoms in green. (**a**) Lateral view of a unit cell; (**b**) \(c\)-axis view of four unit cells; (**c**) extended view of the crystal structure where the layered structure can be appreciated. The out-of-plane Raman mode A\({}_{\rm 1g}\) and the in-plane E\({}_{\rm g}\) one are sketched. The gray arrows represent the relative movement of the chalcogen atoms while the zirconium one remains at rest.  
Thin layers of ZrX\({}_{2}\) were obtained by mechanical exfoliation and deposited over a silicon substrate with 285 nm of thermally grown SiO\({}_{2}\) (NOVA Electronic Materials LLC, Flower Mound, TX, USA). Exfoliation process was performed using an adhesive plastic film of 80 \(\upmu\)m thick from Ultron Systems (Ultron Systems, Inc., Moorpark, CA, USA). A plastic film piece was used to peel a freshly cleaved bulk ZrX\({}_{2}\) crystal and the peeling process was repeated to obtain thinner flakes. Flakes were then deposited over a Si/SiO\({}_{2}\) substrate (root mean squared surface roughness \(\approx\) 0.171 nm) by attaching the adhesive piece over the surface and applying a gentle pressure. Before flake deposition, substrates were cleaned by sonication in a H\({}_{2}\)O:NH\({}_{4}\)OH:H\({}_{2}\)O\({}_{2}\) (2:1:1) solution and rinsed in water. Finally, the adhesive piece was slowly removed from the sample and thin nanosheets were found and inspected over the substrate by optical microscopy, atomic force microscopy (AFM), and Raman spectroscopy. Exfoliation and measurements were performed in ambient conditions, while samples were kept under a vacuum in the meantime.  
Optical images were obtained with a NIKON Eclipse LV-100 Optical microscope and AFM images were performed with a Nanoscope IVa Multimode Scanning Probe Microscope (Bruker, Karlsruhe, Germany) in tapping mode.  
Raman spectra were acquired with a micro-Raman (model XploRA ONE from Horiba, Kyoto, Japan) with a grating of 2400 gr/mm, slit of 50 \(\upmu\)m, and hole of 500 \(\upmu\)m. The employed wavelengths were 532 nm, 638 nm, and 785 nm. The power density of the laser used for spectra measured at 532 nm was 5.25 mW/\(\upmu\)m\({}^{2}\) (bulk crystals) and 170 \(\upmu\)W/\(\upmu\)m\({}^{2}\) (thin-layers), for spectra measured at 638 nm it was 7.58 mW/\(\upmu\)m\({}^{2}\) (bulk crystals) and 410 \(\upmu\)W/\(\upmu\)m\({}^{2}\) (thin-layers), and for those spectra measured at 785 nm it was 7.2 mW/\(\upmu\)m\({}^{2}\) (bulk crystals).
## 2 Methodology  
In this study, 753 EL images of multi-crystalline silicon (mc-Si) aluminum and back surface (Al-BSF) modules are used (\(\sim\)46,000 cell images). The modules are from different PV arrays installed in various locations across the United Kingdom (Oxford-shire, Norfolk, Hampshire, and Somerset).  
The EL images were acquired using a modified complementary metal-oxide-semiconductor (CMOS) camera (Nikon D750) that was modified by replacing the embedded infrared filter with a daylight filter (850-1700 nm). The images were acquired outdoors one hour before sunset, at approximately 17:00 (during August-September 2016), with a tripod holding the camera perpendicular to the module at a distance of 2-3 m. The exposure time ranged between 5 and 10 s, while the bias current was fixed at 5A.  
Two experts in EL-based PV diagnostics then classified the cell images into three classes: (i) cracks (Mode B and C), (ii) finger failures and micro-cracks (Mode A), and (iii) no failures. Examples of the three different classes are presented in Figure 1. Finger failures and micro-cracks (Mode A), cracks (Mode B and C) are combined since the failures look similar in terms of structure, length, and intensity. More importantly, they have a similar effect on the module's output power. Cells that contain more than one type of fault are labeled according to the more severe defect (i) \(>\) (ii) \(>\) (iii). The following image processing and machine learning part will be discussed in the section below.
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 2 Background
### Routing Protocol for Low-Power and Lossy Networks (RPL)
#### 2.1.3 Sybil Attack  
A Sybil Attack is a type of attack in which a malicious actor creates multiple fake identities or nodes in a network to gain control and manipulate the network. This type of  
Figure 1: RPL DODAG with N Nodes from aaaa::1 to aaaa::N.  
attack is particularly dangerous in peer-to-peer networks and distributed systems where trust is critical. In the context of RPL, a Sybil attack involves a malicious node creating multiple fake identities or nodes that can participate in the network and influence the routing process. These fake nodes can cause routing loops, interfere with routing decisions, and lead to network congestion, which can ultimately result in denial of service (DoS) attacks [11].
## 5 Analysis of Recent Smart Campus and University Deployments
### Communications Technologies
 & **Data rate** & **Main Features** & **Typical Smart Campus Applications** \\ \hline ANT\(\ast\) & 2.4 GHz & 30 m & 20.3 bit/s & Very low power consumption, up to 65,533 nodes & Health and sport performance monitoring \\ \hline Bluetooth S\(\,\)LE & 2.4 GHz & \(<\)400 m & 1360.3 bit/s & Low power (batteries last from days to weeks) & User flow monitoring, guidance, positioning, telemetry \\ \hline DASEP/ISO 18000-7 & 315-915 MHz & \(<\)10 km & 27.8 bit/s & Very low power (batteries last from months to years) & Item, vehicle and user tracking \\ \hline HF RFID & 3-30 MHz (135.6 MHz) & a few meters & \(<\)60.3 bit/s & Low cost, in general it needs no batteries & User and asset identification for access control and payments \\ \hline IQRF & 866 MHz & hundreds of meters & 100.3 bit/s & Low power, long range & IoT applications \\ \hline LF RFID & 30-300 KHz (125 KHz) & \(<\)10 cm & \(<\)60.3 bit/s & Low cost, it needs no batteries & Access control systems, asset tracking \\ \hline NB-IoT & LTE frequencies & \(<\)35 km & \(<\)250.3 bit/s & Low power, long range & IoT applications \\ \hline NFC & 13.56 MHz & \(<\)20 cm & 42.4 bit/s & Low cost, it needs no batteries & User access control and payments \\ \hline LoRa, LoRaWAN & Different Industrial Scientific & kilometers & 0.25\(-\)50.3 bit/s & Low consumption, long range & IoT applications \\ \hline SigFox & 866-902 MHz & 50 km & 100.3 bit/s & It makes uses of private cellular networks & IoT and M2M applications \\ \hline UHF RFID & 30 MHz-3 GHz & tens of meters & \(<\)60.3 bit/s & Low cost, it usually needs no batteries & Asset tracking, logistics, vehicle access control \\ \hline Weightless-P & Licenenenenenenenenenenenenenenenenenenenenenenenenenen
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 3 Strategies Enhancing Remanufacturing
### Remaining Useful Life (RUL)  
The remaining useful life of a product is an estimation of how much useful life the used part or product still has and for how long it can still be used. It can be used to determine whether or not a used part can still withstand another lifecycle [101]. As explained by [41], there are three main factors which need to be checked:  
1. _The physical remaining useful life:_ This is based on the actual state of the product, usually visible by simple observation. Questions which can be raised here include: Can the product still withstand the stress which it has to go through to satisfy its users? Is it safe for the user to keep using that product [102]?
2. _The technical remaining useful life:_ For the technical aspect of RUL, the aspect to take into consideration is whether the main parts of the product are still functioning properly; in order words, whether the product is still able to carry out its assigned function.
3. _The economic remaining useful life:_ This option has to do with profit margins and is special for products that normally generate revenue when they are being used, for example, a food processing machine. The main question asked here is whether the product can still generate its allocated revenue.  
These three factors can be determined through calculations, as shown in detail by [41]. Further predictions using embedded systems have been undertaken by [42; 43; 103].
###### Abstract  
The measurement and control gate, as a new type of measurement and control equipment, has been widely used for water quantity control in irrigation areas. However, there is a lack of methods for calibrating the flow inside the measurement box at present. This paper establishes a flow prediction model based on a back propagation (BP) neural network and its optimization algorithm by using 450 sets of sample data obtained from the indoor gate overflow test and verified the effectiveness and accuracy of the prediction model by using another 205 sets of sample data. The results show that the gate flow prediction model based on a BP neural network and its optimization algorithm has self-adaptability to different flow patterns, and its prediction accuracy is significantly higher than that of the traditional water measurement method. Compared to the unoptimized BP model, the BP model optimized by the genetic algorithm (GA) or particle swarm optimization (PSO) has higher prediction accuracy and better error distribution. Both GA and PSO algorithms can be used to optimize the initial weights and thresholds of the BP flow prediction model. However, by comprehensively analyzing the prediction accuracy, error distribution, and running time, the PSO algorithm has better optimization performance compared to the GA algorithm. The prediction model can provide a reference for flow rate calibration and the anomaly rejection of measurement and control gates in the irrigation area.  
measurement and control gate; flow calculation method; GA-BP; PSO-BP; flow prediction
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 2 Methodology and Foundations
### Methodology  
To address the research questions raised in this paper, we initially define the network-slicing resource management process by considering admission control, resource allocation, resource scheduling, and resource orchestration phases. Then, we classify the existing RL/DRL-based resource management approaches into one of the phases mentioned. Lastly, we provide research directions in the RL/DRL-based network slicing resource management area.  
To select the works presenting network-slicing resource management approaches based on RL and DRL, we introduced search keywords on three electronic databases: SCOPUS, IEEE, and Web of Science. The keywords used: \"Resource management on network slicing\", \"(DRL or RL) and network slicing\", \"(DRL or RL) and 5G\", and \"(DRL or RL) and 5 GB\". Not many publications were found because resource management on network slicing is a relatively new research field. Therefore, an additional search using the following keywords was carried out: \"admission control and (DRL or RL) and network slicing\", \"resource allocation and (DRL or RL) and network slicing\", \"resource Scheduling and (DRL or RL) and network slicing\", and \"resource orchestrating and (DRL or RL) and network slicing\". The searches resulted in 150 works. Titles and abstracts were reviewed to eliminate works with no relation to the area. Furthermore, recent works from nonrecognized conferences and low-impact journals were filtered. As a result, we chose 50 works for a full-text review.  
The details of the literature review and the analysis performed is presented in Sections 3-6. Tables 1-5 summarize the works selected to review; the first four works of each table and Sections 3.1, 4.1, 4.2, 5.1 and 6.1 correspond to papers published in journals and conferences with the highest impact factor. In those tables, the works are analyzed according to optimization objective, network focus (i.e., E2E, RAN, CN, Edge), space of states, space of actions, RL/DRL algorithm, DNN structure, exploration-exploitation method, use case (or vertical application), training, dataset, and development.
# Carbon Microstructures Synthesis in Low Temperature Plasma Generated by Microdischarges
## 3 Results
### Carbon Aerosol Produced in the Discharge  
Besides the carbon deposit produced at the surface of electrodes during electrical discharge in a mixture of cyclohexane and argon, aerosol particles, which leave the reactor with the flowing gas, were also generated. The variations in the concentration of particles produced during the discharge for two inter-electrode distances are shown in Figure 24. The particle concentration for discharge currents of \(i\) = 1.4 and \(i\) = 2.4 mA was similar and was higher than that for the discharge current of \(i\) = 1.8 mA. The total particle number concentration for the inter-electrode distance of 3.75 mm was about one order of magnitude higher than for 15 mm.  
The particle size distribution measured during discharge in a mixture of argon and cyclohexane is shown in Figure 25. The particles generated for an inter-electrode distance of 3.75 mm are smaller than those generated for 15 mm. For a discharge current of \(i=2.4\) mA and an inter-electrode distance of 15 mm, the concentration of particles larger than 300 nm was 10 times higher than for same discharge current for lower inter-electrode distances, which could be the effect of a higher concentration of soot precursor radicals. [PERSON] and [PERSON] [60] showed that nanoparticle growth takes place on the outer edge of the plasma column.  
Figure 24: Concentration of particles produced during discharge in a mixture of argon and cyclohexane for two inter-electrode distances of (**a**) 3.75 mm and (**b**) 15 mm for different discharge currents.  
Soot can nucleate and grow through both ion-related and neutral radical mechanisms. Soot formation mostly occurs at the discharge boundary due to the relatively lower temperature than that in the plasma column. For example, for methane plasma, a sudden increase in the soot concentration occurs at temperatures above 1700 K [61]. In cyclohexane plasma, it could be the effect of increasing temperature and the acetylene produced in the discharge, which is known as a precursor of soot. The process of soot particle formation from acetylene, known as the \"Winchester mechanism\" [62], could be due to the following reactions with negative ions [63]:  
\[\mathrm{C_{2}H_{2}+e\to C_{2}H^{-}+H}, \tag{14}\]  
\[\mathrm{C_{2n}H_{2}+C_{2}H\to}\mathrm{C_{2n}+2H^{-}+H_{2n},n=1,2,3\ldots,} \tag{15}\]  
Figure 25: Particle size distribution produced during the discharge in a mixture of argon and cyclohexane, for inter-electrode distances of (**a**) 3.75 mm and (**b**) 15 mm for different discharge currents.  
or  
\[\mathrm{C_{2}H_{2}+e\to H_{2}CC^{-}}, \tag{16}\]  
\[\mathrm{H_{2}CC^{-}+C_{2}H_{2}\to C_{2n+2}+H_{2}^{-}+H_{2n}},\,n=2,3,4\,\ldots\,. \tag{17}\]  
The same reactions could occur for positive ions.  
For both inter-electrode distances, the mechanism should be the same, but the plasma column had a higher diameter when the interelectrode distance was 15 mm than when it was 3.75 mm.
## 3 Results and Discussion
### Comparison of Response Time and Length  
Figure 3a,b show the response times and lengths in a comparison between the chatbots. In terms of the response time, ChatGPT exhibited the fastest average response time of 3.6 s, while Gemini lagged with an average response time of 10.2 s. This disparity can be attributed to ChatGPT's optimized architecture and utilization of pretrained models, enabling it to quickly process and respond to user queries. Gemini, on the other hand, had longer response time due to their less effective architecture and the necessity to train models on new data. ChatGPT represented remarkable efficiency, responding to queries in half time compared to Gemini. When analyzing the response length, ChatGPT exhibited a higher word count generation of an average of 351 words compared to Gemini's word count average of 272, which shows more concise and accurate response generation.