esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Resources for the Hybrid System
### Photovoltaic System  
Six photovoltaic modules were considered, along with their respective equipment, and were connected to the grid. The electrical system, including the photovoltaic modules, was modeled using the multi-cell equivalent circuit model [47]. The model used allows for determining the maximum extractable power of the system as well as the photovoltaic efficiency, depending on the solar radiation levels at the site. The efficiency of a modern inverter was considered to be 95%. The small photovoltaic system was sized for 3 kWp and was mounted on the roof of the building at an inclination of 15\({}^{\circ}\) with respect to the horizontal and with the reference plane facing south. The solar potential in Ecuador is very high due to its geographical location on the equator, varying between 4.5 and 5.5 kWh/m\({}^{2}\)/day across most of the territory. The site experiences consistently high levels of solar irradiation throughout the day, with the peak intensity being around midday. The electrical output of the photovoltaic system is defined by Equation (4) below.  
\[P_{pv}=f_{pv}*Y_{pv}*\frac{I_{T}}{I_{S}}\ \ [\mathrm{W}] \tag{4}\]  
\(Y_{PV}\) is the nominal power of the PV solar panels, and \(f_{PV}\) is the reduction factor. \(I_{T}\) is known as the total incident radiation on the surface of the solar panel (Wh/m\({}^{2}\)). \(I_{S}\) is considered as a base value of 1000 W/m\({}^{2}\), knowing that it is an optimal value of desirable irradiation.  
The temperature to which the solar panels are exposed must be considered and can be determined using Equation (5):  
\[T_{C}=G_{S}\bigg{(}\frac{NOCT-25}{1000}\bigg{)}+T_{amb}\ \ \big{[}^{\circ}\text{C} \big{]} \tag{5}\]  
\(G_{S}\) is the solar irradiation in W/m\({}^{2}\), and \(T_{amb}\) is the ambient temperature in \({}^{\circ}\)C. NOCT is the normalized temperature of the cell that is considered to have a global radiation of 1000 W/m\({}^{2}\), with a reference ambient temperature of 25 \({}^{\circ}\)C.
## 4 Experiment Setup and Results
### Optimized Experiments for MLS Signal Inhibition of SBS
Figure 7: (**a**) Forward spectrum at output power of 2.844 W and (**b**) filtered PRBS modulated output spectrum after pre-amplification. The beam quality at an output power of 2.844 W was measured, as shown in Figure 8. The beam transmission factor in the X direction was \(M_{x}^{2}=1.05\), and the beam transmission factor in the Y direction was \(M_{y}^{2}=1.10\). The measurement results showed that the beam propagation factor M\({}^{2}\) of the laser was close to the diffraction limit of the fundamental mode Gaussian beam.
## 2 Materials and Methods
### Kriging Interpolation Method  
The Kriging interpolation, alternatively referred to as spatial local interpolation, is a spatial estimation methodology initially conceptualized and named in honor of the South African engineer, [PERSON], in 1951. Further refined and elaborated upon by the distinguished French geostatistician, [PERSON], this method stands as an unbiased optimization approach for interpolating variables within a delimited geographical domain [52; 53]. Initially, a specified distance interval, which profoundly influences the point value targeted for interpolation, is delineated. Thereafter, the attribute value of the point is ascertained by utilizing the sampling points situated within this designated range. This interpolation approach not only acknowledges the spatial relationship between the point under estimation and the known data points but also accounts for the inherent spatial correlation of the variables in question. In this study, daily concentration data for PM\({}_{2.5}\), PM\({}_{10}\), SO\({}_{2}\), NO\({}_{2}\), O\({}_{3}\), and CO were utilized to calculate their respective annual and seasonal averages. The Kriging interpolation method was subsequently applied to chart their spatial distribution maps, facilitating a comprehensive exploration of the prevailing spatial distribution characteristics of pivotal pollutants such as PM\({}_{2.5}\) and O\({}_{3}\). The exact computational formula is expounded in Equation (1).  
\[Z(x_{0})=\sum_{i=1}^{n}\lambda_{i}Z(x_{i}) \tag{1}\]  
In Equation (1), \(Z(x_{0})\) represents the estimated value at the interpolation point \(x_{0}\). \(Z(x_{i})\) represents the observed value at a known data point \(x_{i}\), where \(i\) ranges from \(i\) to \(n\). \(n\) represents the total number of data points. \(\lambda_{i}\) represents the weight coefficient, which reflects the influence of each data point \(Z(x_{i})\) on the estimated value \(Z(x_{0})\). The weight is usually determined based on distance or spatial correlation, and the closer the point, the greater the weight.
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 5 Discussion  
In this section, we provide an overall discussion of the obtained outcomes.  
* Regarding the larger-scale experiments (large number of nodes; large number of time slots), it is observed that the two modified Modified-near protocols decisively limit both the average age of the packets and the maximum age observed in the system (which naturally equals with the threshold value set in each simulation, after which the packets are dropped). On the other hand, the two FRESH-near protocols do not deal with the age of the packets, with the result that in almost all experiments, packets that are practically constantly circulating in the network (for example, for a number of time slots equal to 5000, the maximum age observed in the system is equal to 5000, that is, one of the first packets of the system has not yet been delivered at the end of the simulation. Correspondingly, for a number of time slots equal to 15,000, Max AoI 14,995 or even 15,000 are observed). It is therefore noticeable that in these cases, the packets remaining in the network offer absolutely nothing, as they are constantly circulating from node to node or are stored in the buffers of the DTN nodes, with the possibility of their successful delivery being significantly reduced. The situation is worse, since not only do these packets offer absolutely nothing, but they also waste the available network nodes. In other words, in a more realistic simulation where the nodes would have buffers of finite capacity, these packets would occupy space in the buffers unnecessarily, a fact which would probably have a significant impact on the system throughput itself.
* A reasonable observation that can be made is that in the three modified protocols, the number of remaining packets at the end of the simulations is relatively small, as the packets are limited to the part of their age, resulting in being dropped. On the other hand, in the three original FRESH protocols, the number of remaining packets is very large (especially in FRESH-near), which is justified by the fact that, as mentioned before, even packets produced in the first moments of the simulation may be circulating on the network.
* In the simulations, it appears that the increase in the size of the square Grid from \(100\times 100\) to \(1000\times 1000\) did not bring about any significant change in the metrics considered (throughput, average age of the packets). However, this is not true, as in the simulations the value of the neighborhood radius has been increased accordingly (for both \(100\times 100\) and \(1000\times 1000\) an appropriate neighborhood radius has been chosen, so that the expected number of neighbors of each node is approximately equal to 7% of the number of network nodes).
* Regarding the execution time, obviously the implementations of the algorithms can be improved further, in order to reduce their time complexity and possibly run simulations in even larger networks and with an even greater number of time slots.
# Progress of Organic/Inorganic Luminescent Materials for Optical Wireless Communication Systems
## 2 Luminescent Materials for VLC Systems
### Features of BODIPY Dyes  
The organic system known as BODIPY is a class of luminescent dyes that is extensively used in VLC scenarios. BODIPY dye is typically incorporated into a polymeric film, which is then used as a filter or coating on a light source, such as a LED. The dye absorbs light from the LED and re-emits it at a different wavelength [66], which can then be detected via a photodetector to create a VLC system. In particular, BODIPY dyes are attractive for VLCs due to their high photostability, high quantum yield, and narrow emission spectra [67; 66]. In this context, [PERSON] et al. [4] reported two different families of oligofluorene-BODIPY materials as alternative materials for color conversion in VLC, such as Y- and T-type, as shown in Figure 3a. Steady-state measurements of BODIPY show that the photophysical properties, including absorption and emission, can be tuned by controlling the molecular shape and lengths of the fluorene arms, and all the materials in solid state are highly emissive, with PLQY ranging from 55% to 75%, making them suitable for light-emitting devices and VLC.  
The emission lifetimes show that all BODIPYs' lifetimes, as shown in Figure 3b,b', are much shorter (3.36-5.22 ns) than those of conventional phosphor color converters (\(\upmu\)s scale) [68], while modulation studies show that BODIPYs have an order of magnitude higher modulation bandwidth (\(\sim\)39 MHz) and data rate (\(\sim\)100 Mbps) than the commercially available phosphor-based LEDs for VLC, as shown in Figure 3c. In this sense, other authors agree with the notion that the BODIPY system was proven suitable for VLCapplications during the past eight years [69; 70], giving a maximum frequency of up to 73 MHz and date rate of 370 Mbps. BODIPY species in polydimethylsiloxane matrix were used as color conversion layers over blue LED to produce white light with CRI of 95% and CCT of 4200 K [71]. On the other hand, new OLEDs based on BODIPY (with different aromatic substitutes) and diverse mixtures with PMMA showed promising results for use in OLEDs due to their high stability, brightness, and color tunability resulting from their characteristic electroluminescence (EL) spectra, as shown in Figure 3d [72]. For PLQY, \(\sim\)87-95% was obtained, with a frequency bandwidth of 55 MHz and a data rate of 218 Mpbs. It is deductible that a high PLQY is crucial to achieve an improvement in the VLC system. Thus, BODIPY elements are an attractive option for use in various optoelectronic applications [71; 73; 74; 75]. Overall, BODIPY dyes offer a promising solution for VLC systems, as they can provide secure data transmission without interfering with other wireless communication systems.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 6 Performance Analysis
### Performance of the ARM Cortex-M for Asymmetric Cryptography
#### 6.1.2 Power Consumption  
Power consumption was considered a very important factor, especially for the resource-constrained devices used in IoT or certain IIoTs. The power consumed by the ARM Cortex-M in the execution of the cryptographic algorithm was calculated using Ohms Law, as shown in Equations (1) and (2):  
\[I=\frac{v}{R} \tag{1}\]  
where \(I\) represents the microprocessor current, \(V\) represents the voltage supplied to the microprocessor, \(R\) represents the resistance (1.0 \(\Omega\)), and \(V\) represents the supply voltage (5.0 V):  
\[P=V\times I \tag{2}\]  
We calculated the average power consumption for the microprocessor by running ECDSA 10 times. The results presented in Figure 6 and Table 2 show that all of the processors (M0, M3, M4 and M7) had low values of power consumption; they were therefore energy-efficient in terms of implementing ECDSA and could satisfy the requirements for resource-constrained devices.
# Self-Learning Mechanism for Mobile Game Adjustment towards a Player
## 3 Graphical User Interface Adjustment According to the User's Screen
Unlike the case of background image scaling, where it is crucial not to obtain white space on the screen, in the case of graphical game elements, it is significant to make all elements visible on the screen without cutting off some elements in the game view. Thus, all graphical elements of the game have to be visible on the screen. In the case of the _Wonderful Animals_ app, as can be noted from Figure 1, all elements of the main menu include the logo of the game, button for music and the exit button, icons for all levels of the games (puzzles, pairs and find differences), titles \"Pairs\" and \"Differences\" and spacing between elements. When scaling graphical game elements, two conditions have to be fulfilled. As the first condition (given in Equation (10)), it is necessary to provide that the sum of all elements' width, which will be displayed in one row on the screen, is less than the screen width:  
\[w_{1}+w_{2}+w_{3}+\cdots+w_{n}\ \leq width_{s}, \tag{10}\]  
where \(w_{i}\)\(i=1,..,n\) is icon width, and \(n\) is number of icons. As the second condition (defined by Equation (11)), the task is to verify if the sum of all elements height, which will be displayed in one column on the screen, is less than screen height:  
\[h_{1}+h_{2}+h_{3}+\cdots+h_{n}\ \leq height_{s}, \tag{11}\]  
where \(h_{i}\)\(i=1,..,n\) is icon height, and \(n\) is number of icons. **Theorem 2**. : _For a given screen display wherein the aspect ratio is \(R_{s}\) and a given sum of all icons in which the aspect ratio is \(R_{si}\), there is an adequate image scaling, regarding at least one image dimension and retaining the original \(R_{si}\), which results in complete visibility of all icons._  
Proof of Theorem 2.: The proof can be carried out in a similar manner as the proof of Theorem 1, considering three possible cases and using the definition of the screen aspect ratio \(R_{s}\) and sum of all icons' aspect ratio \(R_{si}\), according to Equation (12), respectively:  
\[R_{s}=\frac{height_{s}}{width_{s}}\text{ and }R_{si}=\frac{height_{si}}{ width_{si}},\text{ where }R_{s}>1,\ R_{si}>1. \tag{12}\]  
\(\square\)  
According to Theorem 2, all graphical elements (icons) can be properly scaled according to either the screen width or screen height, so that two conditions for complete visibility of all elements, given in Equations (10) and (11), are satisfied.
# MuMIA: Multimodal Interactions to Better Understand Art Contexts
## 2 Related Work and Motivation
### Multimodal Technologies and Interfaces in Cultural Heritage
#### 2.2.2 Interactions with Visual/Gaze Interfaces  
Regarding visual/gaze interfaces, the data from specialized equipment that monitor and analyze eye movements (i.e., eye-tracking data) have been used for monitoring users' behavior and interactions in many domains such as drowsiness detection [37], diagnosis of clinical conditions [38], neuromarketing [39], and security [40]. In the cultural heritage domain, eye-tracking has been used to understand visitor experience [41] and learning experience [42]; to understand visitors' emotional states, appraisals, and visual exploration patterns [43]; to provide personalized and immersed visitor experiences [44]; to build visual attention models of visitors [45].; and to detect objects and areas of interest [46; 47].  
[PERSON] et al. [41] showed that, through eye-tracking analysis, different visitors' cognitive styles can be recognized as they lead to imbalances regarding understanding of the cultural content. Based on these differences, eye tracking can be used to model visitors' profiles and to deliver personalized and immersed experiences [44]. [PERSON] et al. [45] showed that, through eye tracking, it is feasible to build visual attention models of visitors to extract patterns and to provide art recommendations that would be interesting to them. These patterns and points of interest provide useful insights for optimizing existing augmented-reality applications for cultural heritage institutions. [PERSON] et al. [48] demonstrated the value of mobile eye tracking for identifying areas of attention and for identifying eye-movement patterns within exhibition spaces. [PERSON] et al. [46] used computer vision techniques for image-based positioning and for detecting objects and areas of interest in real-time. In the same vein, [PERSON] et al. [47] used mobile eye tracking to recognize cultural heritage objects and [PERSON] et al. [49] to interact with artwork.  
The aforementioned works present eye-tracking applications that are divided into two categories: _active_ and _passive_. Active eye-tracking applications enable the users to use their eye movements as an input modality to control a device, a virtual world, a game, etc. Passive eye-tracking applications are used to observe and evaluate human attention objectively and non-intrusively, enabling the researchers and designers to better understand the users and to increase the impact of the visual designs and communication. For the scope of the paper, active eye-tracking plays an important role as it provides a natural and engaging interaction experience, considering that people naturally gaze at the world in conjunction with other activities [50]. When active eye tracking is combined with other input modalities (e.g., voice interaction and mouse/touch), the user experience can be enhanced. Table 1 presents a comparison between our proposed tool and other tools in cultural heritage that are primarily based on active eye tracking.
## 6 Barriers and Challenges in Digitalization of MGs  
The digitalization of MGs refers to the integration of advanced communication, monitoring, and control technologies into the microgrid infrastructure to optimize energy management and improve reliability. However, despite the benefits of digitalization, there are still several barriers and challenges that hinder its implementation. Various barriers and challenges to digitalization of MGs and possible solutions to overcome these obstacles are summarized below:  
Technical complexity: The digitalization of MGs involves the integration of various technologies such as sensors, communication systems, and advanced control algorithms. The complexity of integrating these technologies can pose a significant challenge, especially for small-scale MGs that have limited resources and technical expertise [96; 256; 257]. The technical complexity of microgrid digitalization requires specialized knowledge and expertise, making it difficult for small-scale MGs to invest in such systems.  
High implementation costs: Digitalizing a microgrid requires significant upfront investment, which can be a barrier to its implementation, particularly for small and medium-sized enterprises (SMEs) [258; 259]. The cost of digitalization includes the installation of communication infrastructure, software, hardware, and sensors. The high cost of digitalization is a significant challenge, especially for MGs with limited resources and financing options.  
Figure 14: Crucial steps to perform a risk analysis in a microgrid.
# PV-Powered Electric Vehicle Charging Stations: Preliminary Requirements and Feasibility Conditions
## 5 PV-Powered EV Charging Station Simulation Results and Analyses
### Case 2--Publicly Accessible Charging Station: PV Parking Shade with Nine Spots and Nine Chargers
#### 5.2.2 Scenario 2b  
The hypotheses for the scenario 2b are shown in Table 4.  
Then, the total EVs demand energy is 21 kWh. Figure 16 shows the system power flows and the stationary storage SOC evolution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**EV\#** & **Arrival Time** & **SOC at Arrival** & **Desired SOC** & **Departure Time** & **Charging Mode/Power** \\ \hline EV1 & 09:30 & 65\% & 75\% & 15:00 & Slow/0.909 kW \\ EV2 & 10:30 & 62\% & 70\% & 16:00 & Slow/0.727 kW \\ EV3 & 12:00 & 61\% & 66\% & 14:00 & Slow/1.25 kW \\ EV4 & 13:00 & 58\% & 66\% & 14:30 & Slow/2.66 kW \\ EV5 & 14:30 & 57\% & 68\% & 17:00 & Slow/2.2 kW \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hypotheses for Scenario 2b.  
Figure 15: Scenario 2a, system power flows and stationary storage SOC evolution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**EV\#** & **Arrival Time** & **SOC at Arrival** & **Desired SOC** & **Charging Mode/Power** \\ \hline EV1 & 09:30 & 65\% & 75\% & Slow/1.8 kW \\ EV2 & 10:30 & 62\% & 70\% & Slow/1.8 kW \\ EV3 & 12:00 & 61\% & 66\% & Slow/1.8 kW \\ EV4 & 13:00 & 58\% & 66\% & Slow/1.8 kW \\ EV5 & 14:30 & 57\% & 68\% & Slow/1.8 kW \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hypotheses for Scenario 2a.  
PV and stationary storage share power to charge the EVs, without the need for the public grid supply. When the PV production is higher than the EV's demand power, PV charges the stationary storage so it can supply further power afterward. The stationary storage becomes full, reaching its maximum capacity around 12:50, therefore, PV injects power into the public grid. The EVs charging power and EVs SOC evolution for the two scenarios 2a and 2b are shown in Figure 17.  
A comparison between the two scenarios is shown in Figure 18. All EVs are charged mainly with PV energy, except for the last EV, i.e., EV5, where it comes in the late afternoon and the PV production is low. As the charging mode is slow for all EVs, the public grid is not required.  
In conclusion, scenario 2b may be superior to scenario 2a and may improve the PV benefits for the EVs when the park time is known and longer than the time actually needed for charging in scenario 2a. It should be noted that when the park time is longer, some EVs charge simultaneously so the PV production is shared between them thus reducing the portion of PV energy.  
These two scenarios are focused on slow charging mode only. The next scenarios will consider slow and fast charging mode and more than 10% of energy charge to analyze the impact of fast charging on the EVs and their PV benefits.  
Figure 16: Scenario 2b, system power flows and stationary storage SOC evolution.  
Figure 17: (**a**) EVs charging power and EVs SOC evolution for Scenario 2a; (**b**) EVs charging power and EVs SOC evolution for Scenario 2b.