esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation of the Logarithmic Transimpedance Circuit
On the other hand, the logarithmic transimpedance circuit further reduces its sensibility preventing saturation. In this case, the SNR is strongly affected by the \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Feature/Value** \\ \hline VLC emitter & Standard compliant LED-based traffic light: ELBA 3S1 TL \\ Light source & 5 x High power red LEDs \\ Total LED power & 9W \\ Traffic light irradiance measured at 1 m & 190 \(\upmu\)W/cm\({}^{2}\) \\ LED central wavelength & 642 nm (see Figure 5a) \\ LED spectral width & 40 nm \\ Traffic light half angle & 15\({}^{\circ}\) \\ Traffic light diameter & 200 mm \\ Traffic light height & 200 cm \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the LED traffic light parameters. Figure 5: (a) LED traffic light spectral analysis; (b) Sunlight spectral analysis during the tests. additional shot noise introduced by the parasitic light. Nevertheless, with the help of a proper signal reconditioning algorithm, the data can still be extracted. Based on this experimental evaluation, one can see that the logarithmic transimpedance circuit provides the VLC receiver with improved resilience to noise, making this solution suitable for applications in which the VLC sensor should work in strong parasitic light exposure. As a minor drawback, this solution imposes the usage of an AGC block that compensates the variations of the data signal amplitude. Nevertheless, the AGC block is an essential component in most automotive VLC sensors, because such applications impose that the sensor is able to process signals having a very broad range of powers (i.e., the emitter receiver distance is constantly changing, and thus the power of the received data signal). Figure 6: Comparative experimental evaluation showing the response of the logarithmic (Channel 1—orange) and of the linear (Channel 4—green) transimpedance circuits: (**a**). in dark conditions (no additional background light); (**b**). in high SNR conditions (incident noise irradiance of 200 \(\mu\)W/cm\({}^{2}\)); (**c**). in medium SNR conditions (incident noise irradiance of 1100 \(\mu\)W/cm\({}^{2}\)); and (**d**). in low SNR conditions (incident noise irradiance of 4500 \(\mu\)W/cm\({}^{2}\)). One can see that as the irradiance of the parasitic light is increasing, the logarithmic transimpedance circuit reduces its sensibility in order to prevent the photoelectron saturation. On the other hand, the linear transimpedance circuit is saturated when the irradiance of the incident parasitic light increases above a certain limit. These experimental results confirm the benefits of the logarithmic transimpedance circuit.
## 2 VANETs  
A VANET (vehicular ad hoc network) is a type of mobile ad hoc network (MANET), which can facilitate the exchange of information between vehicles as well as between vehicles and roadside infrastructure. These networks can support V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communication. They are expected to evolve with advancements in autonomous vehicles, smart cities and 5G technology. More sophisticated applications such as platooning, where vehicles travel in a group formation to reduce fuel consumption and improve traffic flow, will be integrated into VANETs. Thus, VANETs represent a crucial advancement in transportation technology, promising significant improvements in safety, efficiency, and convenience for future road networks.  
Key components of VANETs are as follows.  
* Vehicles: The vehicles are equipped with Onboard Units (OBUs) that facilitate communication with other vehicles and roadside units.
* Roadside units (RSUs): The RSUs are fixed units along the road, which provide connectivity to vehicles, serving as gateways to the internet and other networks.
* OBUs: The OBUs are communication devices installed in vehicles, which enable the transmission and reception of data.
* Application Units (AUs): The AUs are part of vehicle's onboard system, which utilize VANET data to operate various applications.  
In VANETs, the V2V communication is the direct communication between vehicles, which is essential for collision avoidance, traffic information dissemination, and cooperative driving. While V2I refers to the communication between vehicles and infrastructure, such as traffic signals or toll booths. This facilitates traffic management, toll collection, and the provision of real-time traffic updates. While VANETs possess many advantages, they have extensive and constantly changing topologies as well as fluctuations in the capacity of wireless links. They are constrained by bandwidth and hard delay constraints. Also, the high mobility, speed, and low density of vehicles lead to brief contact durations. In addition, limited transmission ranges, physical barriers, and interference result in intermittent and disrupted connectivity.  
For the implementation of VANET applications, it is essential to develop appropriate networking mechanisms capable of addressing challenges inherent in vehicular environments [13; 14; 15; 16; 17; 18; 19].  
Mobility pattern recognition is a complex task in VANETs because the driving state of each vehicle is different. For traffic control and accident prevention, the human driver behavior should be analyzed to identify mobility patterns and predict the driver's state [20]. The diver's state is affected by different factors such as the external environment, the car safety device, their driving skill, driver emotions, driver age, transportation information, and so on, as shown in Figure 2.
## 5 Discussion
### Future Research  
The evaluation of the SemDaServ approach revealed the needs for detailing the following aspects: (1) An exemplary agenda for workshops necessary for the Clarify domain knowledge process step. (2) Guidelines on how to tailor the SemDaServ approach to different project types (new development vs CI/CD scenarios) (3) Recommendations on building successful Core Teams (cf. Table 1) and combinations of knowledge carriers (cf. Table 2), especially regarding feasible competence mixes. Future research is planned to address these aspects. Additionally, it is planned to research a model approach to size the data types, frequencies, and reliabilities to support the creation of the data model (process step Derive data needs). Furthermore, experiments measuring how the number of actors, product, and service components impact the overall resources (time, budget, scope of required domain knowledge) needed to realize SemDaServ are planed.
## 3 Experiments
### Multi-Band Signal Generation and Phase Shifting for Multi-Band Phased Array Transmitter
The result confirms the proposed method's ability to maintain uniform multi-band beam steering at the transmitter. When the aperture fill time, \(t_{d1}\), exceeds 1/_f\({}_{s1}\)_, VODL1 is adjusted based on \(\Delta t_{1}\) = _t\({}_{d1}\)_ - [_f\({}_{s1}\)_t\({}_{d1}\)_]_/_f\({}_{s1}\)_. _t\({}_{d1}\)_ is set to 100.9 ps, and the delay, \(\Delta t_{1}\), of VODL1 is adjusted to 10 ps. The result of the phase change is shown in Figure 3g. The phase difference between the experimental and the ideal results is an integer number of 2\(\pi\), which means the multi-band beamforming is still supported.
## 3 Results and Discussion
### Discussion
This fact could force the reduction of power given to modules to avoid damages to the plants or excessive water consumption, hindering the optimal exploitation of LEDs. In addition, it must be noted that, according to previous literature, the employment of a well-tailored LED light recipe is capable of reducing the overall electrical energy consumption of a vertical farm, with respect to conventional artificial light (HPS lamp) by also improving the yield [42; 43], with additional cost savings of about 25% if load shifting techniques are applied [42; 43]. Comparing these results with similar studies, it is interesting to note that different growth environments could strongly change the optimal ratio of LED lights. In fact, according to [PERSON] et al., for hydroponic cultivation of green basil the great majority of Red LEDs should be used to enhance basil growth [44]. As suggested from [PERSON] et al., the basil growing conditions should be optimized for each specific controlled environment, and for this matter statistical methods could be very helpful [21]. Regarding validation, the data reported in Table 9 compares the growth properties predicted from the mathematical models with collected experimental data obtained from further and specific experimental tests, in which the obtained best light recipes have been employed. These data confirm the good predictive power of the models and the fact that the calculated light recipes are capable of promoting the basil properties. In fact, comparing Table 9 with Table 6, it appears clearly that in the validation dataset the highest parameters in terms of Highness, Number of Plants and LAI have been obtained for all the considered periods of growth. Nevertheless, it must be noted that, among the limits of the statistical approach, the results here obtained are valid for basil growth in the conditions established in this work, e.g., keeping the same soil and the same type of LEDs. Therefore, further work must be done to verify the validity of the results for other plants or in different soil conditions, for example.
# TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
## 5 Results and Analysis
### Discussion  
As can be seen from the results, the proposed scheme improves the security and reliability of the IoT network by providing a trust based task computation mechanism. While the proposed technique offers improved trust level and higher rate of task execution, it requires sharing of control messages to compute trust in the network. Thus, enhanced reliability of task offloading is achieved at the expense of additional message overhead.
###### Abstract  
In this paper, a novel concept of a three-dimensional full metal system including a Dual-Mode Converter (DMC) network integrated with a high-gain Conical Horn Antenna (CHA) is presented. This system is designed for 5G millimeter wave applications requiring monopulse operation at K-band (37.5-39 GHz). The DMC integrates two mode converters. They excite either the \(TE_{11}{}^{cir}\) or the \(TE_{01}{}^{cir}\) modes of the circular waveguide of the CHA. The input of the mode converters is the \(TE_{10}{}^{m}\) mode of two independent WR-28 standard rectangular waveguide ports. By integrating the DMC with the CHA, the whole system, called a Dual-Mode Conical Horn Antenna (DM-CHA), is formed, radiating the sum (\(\Sigma\)) and difference (\(\Delta\)) patterns associated to the monopulse operation. To adequately prevent the propagation of higher order modes and mode mutual coupling, this integration procedure is carefully designed and fabricated. To prove the performance of the design, the DMC network was fabricated using subtractive manufacturing by Computer Numerical Control (CNC) technology. The CHA was fabricated using additive manufacturing by Direct Metal Laser Sintering (DLMS) technology. Finally, the simulation and measurement results were exhaustively compared, including return loss, isolation, radiation pattern, and gain of the full DM-CHA structure. It is noteworthy that this system provided up to \(\pm 11^{\circ}\) per beam in the angular of arrival detection to support the high data rate operation for 5G satellite communications in the millimeter-wave band.  
monopulse; dual-mode converter; mode converters; conical horn antenna; K-band; 5G; millimeter-wave applications +
Footnote †: This paper is an extended version of our paper published in 2023 17 th European Conference on Antennas and Propagation (EuCAP), Florence, Italy, 26–31 March 2023.
## 3 ALS-KF Based Visual Object Tracking Algorithm
### Object Detection Using Background Subtraction  
The first step in the tracking process is to identify the object of interest. In our proposed method, we utilize the background subtraction method to detect the moving object. The background is initialized as the first frame of the video. For each subsequent frame, we subtract the background from the current frame. By applying a threshold to the difference image, we convert it into a binary image. The binary image is then processed using morphological operations to reduce noise and fill gaps in the detected objects.  
Object tracking in video sequences often requires adaptive thresholding techniques to robustly handle changes in object appearance over time. The process noise covariance  
Figure 1: Flowchart of the ALS-KF based VOT algorithm.  
estimated by the Kalman filter provides a principled metric to adapt threshold levels based on the dynamics of the tracked object. The process noise captures uncertainties in modeling the motion of the object, thus larger values of \(\hat{Q}\) imply more aggressive maneuvers by the target. This leads to greater shape deformation and scale variation as the object moves rapidly through the scene. By leveraging \(\hat{Q}\), the threshold \(T\) can be adapted during tracking according to:  
\[T_{t}=\alpha\hat{Q}_{t}+T_{\text{min}} \tag{15}\]  
where \(\alpha\) is a scaling parameter and \(T_{\text{min}}\) is a minimum threshold level. The key idea is that a larger process noise covariance \(\hat{Q}t\) at time \(t\) indicates faster motion, which requires relaxing the threshold to accommodate greater changes in appearance. This avoids losing the target due to overly tight thresholds based on stale measurements.  
Connected components in the resulting binary image are then identified, and a bounding box is generated for each component. The centroid of each component, which is computed as the geometric center of the bounding box, is then stored as the object's location. The process is repeated for each frame in the video, resulting in a series of centroids representing the object's trajectory.
## 2 Results and Discussion
### Profiling of Montespertoli EVOO
#### 2.2.2 Commercial Category of the Montespertoli Oils and Their Profile  
The first important finding emerging from the reported data is that all the oils produced were classified as extra virgin, fulfilling the primary requirement for offering a product that, even before being clearly recognizable to consumers for its origin-related characteristics, can be appreciated as belonging to the highest commercial category.  
With rare exceptions, all oils shared a consistent profile characteristic of the Montespertoli oils produced with the processing protocol employed:  
* Free acidity \(\leq 0.30\%\)
* Peroxide value \(\leq 10.0\) meq\({}_{\text{O2}}\)/kg
* K\({}_{232}\leq 1.90\)
* K\({}_{270}\leq 0.19\)
* Total phenolic content \(\geq 350.0\) mg/kg
* Hydrolysis ratio \(\leq 2.0\%\) at production
* Oleacein + oleocanthal content \(\geq 50\%\) of the total phenolic content
* Totaltocopherol content \(\geq 250.0\) mg/kg
* Total LOX VOC content \(\geq 30.0\) mg/kg
* (\(E\))-2-hexenal \(\geq 80\%\) of total LOX VOCs
* LOX esters \(\leq 1\%\) of total LOX VOCs
* 1-penten-3-one, (\(Z\))-3-hexenol, and (\(E\))-2-hexenol: 1-3% of LOX VOCs
* Median of fruitiness \(\geq 3\)
* Median of bitterness \(\geq 3\)
* Median of pungency \(\geq 4\)
* Fruity profile characterized by a prevalence of almond and artichoke.
* NVS \(\geq 80.0\)
## 2 Circuit Analysis and Design  
Figure 1 shows a schematic of the full Ka-band flat-gain CMOS amplifier using the inductive neutralization and frequency staggering techniques. Three common-source (CS)gain stages (M\({}_{1}\), M\({}_{2}\), and M\({}_{3}\)) were cascaded to secure a sufficient gain. To accomplish a wideband flat-gain response over the full Ka-band, the inductive neutralization was applied to the first stage, while the frequency staggering was employed in the following interstage and output matching networks.
## 2 Materials and Methods
### Scoring and Analysis of CAs  
An epi-fluorescence microscope (Axioplan2 imaging MOT, Carl Zeiss AG, Oberkochen, Germany) connected to a computerized platform (Metafer 4, MetaSystems) was used for automated scanning of slides. This allowed digital acquisition of each fluorophore color for subsequent classification of CA rearrangements, either manually by direct observation in the case of WCP or through dedicated software for karyotype reconstruction in the case of mFISH-painted PCC spreads. Specifically, CA analysis of WCP-treated samples was carried out by the same scorer on three-color stored images from blind-coded slides. Classification of CAs detected by m-FISH was carried out in accordance with the 24 XCytle labeling scheme as provided by the manufacturer after karyotyping of the PCC spreads by the ISIS imaging software, v. 5.2.23 (MetaSystems, Germany), which attributes a false color pattern depending on overlap signals intensity. All types of structural aberrations as originally defined by [PERSON] [44] were scored separately and categorized in simple exchanges (i.e., translocations and dicentrics), either visibly structurally complete or incomplete, acentric excess fragments and complex exchanges, these being assessed as the result of an exchange involving not less than three breaks in at least two chromosomes (that is the Chromosome, Arm, Break criterion--CAB) [33; 36]. When more than one option to achieve completeness was possible, the rearrangement that produced the minimum CAB was used. For this study, the frequency \"\(f\)\" of all classified CAs was calculated as the ratio between all exchange-type aberrations \"\(n\)\" (simple plus complex, both reciprocal and nonreciprocal) and the number of cells scored \"\(N\)\", which was not less than 500 cells for WCP (1500 for unirradiated controls) and at least 100 cells for m-FISH:  
\[f=\frac{n}{N} \tag{1}\]  
The standard error \(\sigma\) was calculated assuming the frequency \(f\) as binomially distributed:  
\[\sigma_{f}=\sqrt{\frac{f*|f-1|}{N}} \tag{2}\]  
The same formulas were used for the frequency of complex aberrations alone as well as for the number of chromosomes or of breaks involved in complex exchanges per total number of scored cells as a measure of the degree of complexity in the observed chromosomal rearrangements [5; 45]. If necessary, statistical significance was determined at the 95% confidence level by a Two-Sample \(t\)-test using SYSTAT (version 13.1, US).
# Applications of Digital Twin across Industries: A Review
## 1 Introduction
[PERSON] et al., in their review of 115 papers on DT, found that the most explored DT application contexts are: healthcare, maritime and shipping, manufacturing, city management, and aerospace [39]. How the DT technology is being used in these five aforementioned sectors is discussed in this paper. The paper also extends the discussion of DT applications in the real world to sectors such as education, construction, and retail, areas not considered by [PERSON] and [PERSON] [8] (Figure 2). Figure 1: Block diagram of a Digital Twin. The aim of the paper is to identify and understand the potential of DT in any sector and the purpose of implementing it for the right application, which can prove to beneficial to any researcher, business, or sector before investing in the technology to unleash the true potential of the technology. The number of studies in each sector discussed in the paper is varied, but all of them are increasing year by year. Table 1 shows the number of publications related to DT in a particular sector in Scopus, Science Direct, and Google Scholar (last checked May 2022). The increase in DT-related publications is quite recent, i.e., 2016 onwards, but since then the growth has been exponential [2]. The manufacturing industry is leading the chart with the most number of studies, followed by Energy, Construction, and Education, even though the applications of DT in the field of education has been relatively newer. The Aerospace sector, which is the pioneering industry in terms of DT technology, has not that many publications as compared to other sectors. The implementation of DT in sectors such as retail and agricultural is fairly new as compared to others, thus resulting in a low number of publications. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Industry**} & \multicolumn{3}{c}{**No. of Publications**} \\ \cline{2-4}  & **Scopus** & **Science Direct** & **Google Scholar** \\ \hline
**Aerospace and Aeronautics** & 168 & 980 & 2360 \\
**Manufacturing** & 1823 & 3295 & 25,300 \\
**Healthcare and Medicine** & 188 & 819 & 4810 \\
**Power Generation/Energy** & 780 & 2863 & 17,300 \\
**Automotive** & 177 & 939 & 7910 \\
**Oil and Gas** & 434 & 1463 & 9780 \\
**Smart City** & 236 & 462 & 4090 \\
**Mining** & 191 & 1001 & 9700 \\
**Maritime and Shipping** & 59 & 360 & 2330 \\
**Agricultural** & 63 & 495 & 3900 \\
**Education** & 223 & 939 & 14,900 \\
**Construction** & 748 & 1825 & 17,700 \\
**Retail** & 21 & 286 & 2830 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of Digital Twin-related publications in each industry based on different platforms. Figure 2: Different industrial sectors where DT has found its applications.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 3 Materials and Methods
### Multilayer Data Model  
An effective algorithm framework based on two graphs, one representing the room topology and the other the transport network, and maintaining links between them isdiscussed in [57]. In the LIFT project, the spatial data model consists of three connected layers: the transport network, the room topology, and the building topography. The meaning of the layers in the algorithm is summarised in Table 1.  
This model defines the responsibilities of each layer and simplifies the process of generating complex navigation directions. The process starts with finding the optimal route with the Dijkstra algorithm. At this stage, the transport network is used. The path network is represented as a graph with associated geometries of path segments. Such a graph is often referred to in the literature as a navigation graph. Some authors, like [50], define the navigation graph as a graph whose edges represent paths and whose nodes represent decision points, landmarks, or locations where information must be transmitted or presented by the system. This definition would cover all data used in the navigation process. To avoid confusion, the authors avoid using the term 'navigation graph' and refer to the specific layers of the data model.  
Once the route is calculated and its geometry is created with path segment polylines, the system generates navigation guidance. In this process, the topology graph is used. This makes it easy to determine at which point a change in room, an exit to a corridor, the ascent of a staircase, or other events occur that are easily interpreted by a user. The geometry of the route trajectory is useful for creating turn-by-turn directions, which in general are not optimal but in some cases, when the position of the user is known, can be helpful.  
To create comprehensive navigation guidance, additional information should be added. Typical information covers names or numbers of rooms, some distinctive features of the interior, as well as landmark descriptions. These data are retrieved from the topography layer.