esa-sceva/satcom-chunk-collection huggingface dataset:
# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
## 3 Methodology
### Computation of Shadows over Rooftops  
Knowing whether a portion of a rooftop is shaded for a given position of the sun in the sky allows the amount of total irradiation received by the photovoltaic panels to be corrected by excluding the contribution of the direct component of the irradiation. Using the shadow mapping algorithm presented in [32], we determined for all individual pixels of the rooftop surfaces whether they are illuminated or in shadow for each of the selected positions of the sun. The procedure is a variation of the well-known Bresenham algorithm [44] used in computer graphics to draw segments between two points on a grid. Please refer to [32] for a detailed description of our implementation.
## 2 Material and Methods
### Gamma Spectrometry Measurements  
The radionuclides 241 Am and 137 Cs were detected with a gamma-ray analyser CANBERRA Packard (USA) with a GX2018 coaxial Ge(Li) crystal-based semiconductor detector and Genie-2000 program. The resolving power of the gamma-ray spectrometer at 1.33 MeV (60) energy equals 1.75 keV and its comparative effective efficiency is 22.4%.  
Detector calibration by energies and the estimation of registration efficiency was performed using standard samples with a set of radionuclides 241 Am, 109 Cd, 88Y, 137 Cs and 152 Eu. A flat vessel of 0.1 L volume was selected as the size of the geometry of the test due to the small mass for peat samples. The sample was measured for a period not less than 18,000 s.  
The total activity of the 137 Cs nuclide was measured by using the 661.66 gamma energy line having a quantum efficiency of 89.90%, which belongs to its 137 mBa radiation nuclide. The radionuclide 241 Am activity was detected with the 59.54 keV line with a gamma-quantum emission of 35.9 [36].  
The minimum measured energies at exposition t = 18,000 s for the 0.1 litre flat vessel geometry for the GX2018 detector amounted to 0.3 Bq of 241 Am and 0.1 Bq of 137 Cs.
# Recent Advances in Intrinsically Fluorescent
## 3 Application
### Detection of Biomolecules
It is worth noting that CoOOH does not quench the fluorescence as mentioned in the previous article, which can be attributed to the different concentrations of CoOOH (90 \(\upmu\)g mL\({}^{-1}\) and 580 \(\upmu\)g mL\({}^{-1}\)), and a high concentration of CoOOH can also cause a fluorescence decrease. The effect of MnO\({}_{2}\) is also similar to that of CoOOH [24]. To further improve the sensitivity and selectivity, a fluorescence biosensor of colorimetry dual-mode assay based on manganese dioxide (MnO\({}_{2}\)) has recently been developed for the determination of alkaline phosphatase (ALP) activity [118]. Dopamine was oxidized by MnO\({}_{2}\) to form FPDA, which could quench the red fluorescent quantum dots (QDs). Another fluorescent indicator, 3,3\({}^{\prime}\),5,5\({}^{\prime}\)-tetramethylbenzidine (TMB), can also be oxidized by MnO\({}_{2}\) to yellow TMBox. When there were ALP and 2-phospho-L-ascorbic acid in the reaction system, MnO\({}_{2}\) would be reduced to Mn\({}^{2+}\) ions by ascorbic acid, which would prevent the synthesis of FPDA. The sensing system expanded the scope of detection and lowered the detection limit down to 0.015 mU/mL. It was tested in livestock sera and milk to investigate its practical application, consistent with that measured by a commercial ALP assay kit. Similar to turn-on fluorescence sensors, the pros of simple synthesis, good sensitivity, and selectivity, as well as low-cost turn-off fluorescence sensing, showed great potential for biomolecular  
Figure 7: **(A)** Schematic of detection of \(\upalpha\)-glucosidase activity. **(B)** FL with different concentration of CoOOH. **(C)** Change in FL with CoOOH [113]. Reprinted/adapted with permission from Ref. [113], 2019 Elsevier. detection. However, it must also be considered that many factors can quench or weaken fluorescence (pH, reaction time, temperature, etc. ), which will limit its wide application in biomolecule detection.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN
### Technical Benefits to IoT Devices
#### 6.1.3 Longer Transmission Range  
Dynamic spectrum access implies that CR-LPWAN technologies will be able to leverage the opportunity to increase their transmission power leading to longer transmission ranges. This is possible in the VHF bands wherein signals are known to propagate much farther in space. This advantage is presently explored by Sigfox, which enables their networks to cover extremely long transmission ranges as against other LPWAN technologies. Said longer transmission range of Sigfox has been verified in practical deployment environments and test campaigns [88].
## 3 Security Aspects Regarding Wireless Body Area Framework  
WBANs are playing an important role in healthcare by enabling real-time monitoring and treatment delivery. However, the inherent resource constraints of WBAN devices pose significant challenges in securing communication against malicious attacks. Integrating security measures faces various challenges. In addition to the limited resource aspect, which makes it difficult to implement robust security protocols without draining device batteries, the dynamic nature of WBANs, with devices frequently joining or leaving, requires efficient key management and authentication mechanisms. Interference and changing channel conditions further complicate security, necessitating resilient protocols. WBANs often handle sensitive health data, highlighting the need for strong encryption, privacy safeguards, and regulatory compliance.  
IEEE 802.15.6 [6], a standardized protocol for WBAN communication, has been shown to have inherent insecurities, including impersonation attacks and lack of forward secrecy, forward secrecy meaning that even if a key used to establish a secure connection is compromised, past or future sessions will not be vulnerable [11]. These vulnerabilities come from the authentication procedure employed by IEEE 802.15.6. We focused on addressing these vulnerabilities, as a strong device authentication mechanism will limit unauthorized access and prevent malicious actors from having access to the WBAN system, and attack scenarios like DoS and data tampering become obsolete.
## 2 State-of-the-Art V2X Technology
Adapting communication protocols and signal processing techniques to mitigate the impact of these conditions is necessary for maintaining reliability [22]. * Quality of Service (QoS) Requirements: Different V2X applications may have varying QoS requirements in terms of reliability, latency and bandwidth. Ensuring that the communication system can meet these diverse requirements while optimizing resource utilization and network performance is a complex challenge [23].
## 2 Materials and Methods
### Microwave Free-Space Monostatic Method  
To ensure the high quality of the end product required especially by the aerospace sector, a new free-space monostatic instrumentation was developed and integrated in-line in the R2R pilot line of Adamant Composites(tm) to allow for the online quality monitoring and characterization of the material being under production. There are possible positions for integrating such a system in the R2R pilot line and they are stated as Control Points to allow the monitoring of the product at different stages of the manufacturing process. In the present work, the material is checked on its final condition only, just before being winded to form the final roll. The prepreg substrate may or may not have a protective film on top of it. The main issue to investigate is the homogenous deposition of the powder and its integration in the prepreg substrate. In this setup, a handled Keysight(tm) Fieldfox VNA is  
\begin{table}
\begin{tabular}{c c} \hline Roll width & up to 600 mm \\ \hline Roll outer diameter & up to 600 mm \\ \hline Roll weight & max. 100 kg \\ \hline Forward speed range & 0.2–9 m/min \\ \hline Production rate & up to 1000 sqm/day \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of the R2R pilot line for prepreg production.  
Figure 1: (**a**) Schematic and (**b**) Picture of the Adamant Composites(tm) roll-to-roll (R2R) pilot line for the production of FXply(tm) functionalized prepregs.  
connected to a single horn waveguide antenna (working as transmitter and receiver) to measure the free-space reflection coefficient \(S_{11}\) of a planar material lying on a metallic plate [10]. A schematic diagram of the free-space monostatic microwave measurement system is given in Figure 2.  
The complex permittivity of the planar sample is defined as  
\[\varepsilon^{*}{}_{m}=\varepsilon_{m}t-je^{\prime\prime}_{m} \tag{1}\]  
where \(\varepsilon_{m}t\) is the dielectric constant and \(\varepsilon^{\prime\prime}_{m}\) is the loss factor. We consider that the microwave response of conductive composite materials is mainly attributed to the wave reflection at the air to material interface. The complex reflection coefficient \(S_{11}\) measured by the VNA can be derived from the phenomenological flow graph given in Figure 2b where a two port error adapter is considered to relate \(S_{11}\) to the free-space reflection coefficient of the material under test \(r_{01}\) located at a reference distance \(L_{r}\) from the antenna aperture. The error terms \(\Gamma_{1}\), \(\Gamma_{2}\) and \(T_{1}T_{2}\) correspond respectively to the directivity, source match and reflection tracking error terms. These three terms determined from a calibration procedure are used to derive the actual reflection coefficient \(r_{01}\). The term \(r_{01}\) defines the reflection coefficient at the interface air-material. By applying boundary conditions at the air-sample interface, it is found that this parameter is related to \(\varepsilon^{*}_{r}\) by  
\[r_{01}=\frac{1-\sqrt{\varepsilon^{*}_{r}}}{1+\sqrt{\varepsilon^{*}_{r}}} \tag{2}\]
## 2 Results and Discussion
### Higher Terpenoids and Sterols  
Analysis of the profiles of higher terpenoids and sterols in _llex_ leaves showed the presence of 11 compounds (presented in Table 2). For the European cultivars, the study showed similarities in the terpenoid profiles. The dominant compounds (whose chemical structures are shown in Figure 3) in _I. aquifolium_ and _I. meserveae_ were unsolic acid (10.7-13.3 mg g\({}^{-1}\)), oleanolic acid (3.1-5.1 mg g\({}^{-1}\)), \(\alpha\)-amyrin (0.7-2.6 mg g\({}^{-1}\)), lupel (0.2-0.8 mg g\({}^{-1}\)), and uvaol (0.2-0.7 mg g\({}^{-1}\)). Compared to the European varieties, the leaves of _I. parasquarietiss_, which were used as a reference sample, had significantly lower contents of the compounds mentioned above. In particular, an approximately 3\(\times\) lower content of oleanolic acid (1.54 mg g\({}^{-1}\)) and about 2\(\times\) lower content of \(\alpha\)-amyrin (0.4 mg g\({}^{-1}\)) were identified. Figure 1: Structural formulas of the main compounds in the aromatic profiles of leaves of _I. parasquarietiss_ and European cultivars of _I. aquifolium_ and _I. meserveae._  
Figure 2: Principal component analysis of _llex_ taxons (PC1 vs. PC2) based on the contents of its volatile organic constituents; AM—_I. aquifolium_ Aurea Magninata, BG—_I. meserveae_ Blue Girl, BP—_I. meserveae_ Blue Prince, BPR—_I. meserveae_ Blue Princess, HNS—_I. aquifolium_ Handsworth New Silver, PA—_I. paragurarietiss_, SQ—_I. aquifolium_ Silver Queen; aPN—\(\alpha\)-pinene, bPN—\(\beta\)-pinene, gTR—\(\gamma\)-terpinene, HA—hexaoic acid, HPT—(_E,_E_)-2,4-heptdalenical, HX—hexaal, LA—linaly acetate, LM—limonene, LNL—linalool, mHP—6-methyl-hept-5-en-2-one, MR—myrcene, PA+IA—pentanoic acid + isoamyl acetate, pCM—\(\rho\)-cymene, SB—sabinene, TRP—terpinen-4-ol. Figure 4 shows the results of PCA of triterpenoids found in Ilex. Both components explained -74% of the variation, with the first one achieving a score of 52.88%. The plot enables the distinguishment of _I. paraguariensis_, which has strong positive PC1 and PC2 scores, whereas the European varieties are in opposition and are located in the central part of the plot. Almost all compounds, except uvaol and stigmasterol, have negative loadings in PC1. Compounds such as lupeol, erythroidol, \(\alpha\)-amyrin, \(\beta\)-amyrin, lup-20(29)-ene, and others located on the upper left quarter of the graph are positively correlated with _I.__meserveae_ samples. In contrast, triterpene acids are associated with _I.__aquifolium_. The compounds listed above showed no correlation with uvaol. Stigmasterol showed a negative correlation with lupeol and \(\alpha\)-amyrin.
## 3 Influence Factors of \(R_{s}\)
### Influence of Environmental Factors on \(R_{s}\)
#### 3.1.3 pH  
The solution pH values influenced the existing form of target compounds, further affecting their hydrophobicity, solubility, and charge properties. These physicochemical natures determined the sorption of compounds onto the adsorptive phase, thus affecting the \(R_{\mathrm{s}}\) values [70; 71].  
The pH dependence of the \(R_{\mathrm{s}}\) values was strongly related to the type of compounds. [PERSON] et al. [51] reported that the \(R_{\mathrm{s}}\) values for acidic pharmaceuticals decreased as pH increased from 3 to 9, whereas those for basic compounds, such as \(\beta\)-blockers and anti-depressants, increased as the pH increased from 3 to 9. As for the neutral drugs, e.g., carbamazepine and phenolic compounds with high ionization constant, there were no significant impacts on the \(R_{\mathrm{s}}\) values by the ambient pH values. Similar results were also obtained for organophosphate flame retardants (OPFRs) by [PERSON] et al., who reported that the calculated \(R_{\mathrm{s}}\) values for the neutral molecules exhibited a slight variation in the pH range from 3 to 9 [72]. Therefore, due to differences in charge and physicochemical properties of the various species of a given compound, their uptake into passive sampler might differ significantly, resulting in the diverse pH dependence of the \(R_{\mathrm{s}}\) for various compounds [31].
## 3 Results
### Skin Contact Capacitive Images
#### 3.1.2 Skin Image Classification  
Table 2 shows the skin Contact Capacitive image classification results by using different Deep Learning Neural Networks, such as AlexNet, GoogLeNet, VGG16, ResNet-50, InceptionV3, MobileNetV2, DenseNet 201, SqueezeNet, InceptionResNetV2 and Xception, through Transfer Learning. The results show that DenseNet 201 gives the best accuracy (83.8%), but it also takes a long time to train (110 min). GoogLeNet gives the best performance if you consider both the accuracy (73.5%) and training time (21 min), VGG16 gives the worse performance with both accuracy (59.1%) and training time (114 min). SqueezeNet is the quickest to train (8 min) but the accuracy is very low (61.1%). The training was carried out by using MATLAB software on a standard desktop computer with Intel(r) Core(tm) i7-3770 CPU @3.4 GHz, 8 cores, 16 GB RAM and Windows 8.1 operating system.
# Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
## 4 Cloud Pattern API-Process Mining Method
### Event Pattern Substitution Step  
In this step, complex event patterns are substituted with a new event to reduce the complexity of the resulting process maps. If several event patterns are repeated and are known to occur together, then they can be substituted by a single, more abstract event. Defining, selecting, and substituting patterns in this step is done by domain knowledge experts. Several common patterns are defined as rewrite rules at this stage and can be used on the cloud-based application event log dataset. These and other patterns can be defined by the analyst depending on the analysis context.  
For example, consider a common event in a cloud application case: a page refresh. A pattern could consist of a non-empty sequence of _refresh_ events (_refresh_+). As whether the page is refreshed once or many times without any other events in between, that sequence _refresh_+ could be replaced by only a _refresh_ event. This would simplify many traces, and make them equivalent along the way, which would help simplify the resulting process model generated through process mining, without negatively affecting its usefulness. More complex patterns involving sequences of interleaving _click_ and _refresh_ events (_click-click | refresh_+_refresh_) could similarly be simplified by substituting them to a single _click-refresh_ pair. In all these situations, the analyst would still need to decide, depending on the context, how to aggregate the timestamp of these events, for example by selecting the first timestamp for the _click_ and the last one for _refresh_, the first one for both, the last one for both, or some average. The API presented in the next section provides aggregation functions that support this flexibility, not just for timestamps but also for all other attributes of the events.
## 3 Results and Discussion
### Flow Velocity Optimization  
Optimization of flow velocity was based on the comparison of trials performed under process conditions 2 and 3. An increase in global biomass productivity and specific growth rate was associated with an increase in the flow velocity. Accordingly, Moazami et al. reported that an increase in the paddlewheel rotation speed from 1.4 to 2.1 rad s\({}^{-1}\) resulted in a biomass productivity increase [58]. However, Weissman et al. reported that different mixing velocities had no apparent effect on biomass productivity [59].  
Despite the EPA content and, \(\omega\)-6/\(\omega\)-3 and PUFA/SFA ratios being similar, the same was not seen for the lipid content. This parameter was about 33% higher for a culture velocity of 0.30 m s\({}^{-1}\). According to Ma et al. (2016), lipid accumulation is favored under stressed conditions. Thus, 0.30 m s\({}^{-1}\) may induce mechanical stress on the cells. Nevertheless, as previously mentioned, this is the most commonly used flow velocity for biomass production [20].  
Specific power consumption was two-fold higher with a 0.30 m s\({}^{-1}\) circulation velocity. This was due to the two-fold higher paddlewheel rotation speed needed to double the flow velocity. Mendoza et al. [56] and Weissman et al. [59] also reported an increase in the specific power consumption with liquid velocity. The former authors refer to an exponential relationship between these two parameters due to the exponential relationship between the liquid velocity and its kinetic energy [59].  
In order to determine which was the most suitable flow velocity to cultivate \(N\). _oceanica_ microalgae, the ratio of lipid production/energy consumption was calculated. The lower velocity (0.15 m s\({}^{-1}\)) displayed a higher ratio, 17.1 kg kW\({}^{-1}\), when compared to 0.30 m s\({}^{-1}\) flow velocity, 16.9 kg kW\({}^{-1}\). Therefore, 0.15 m s\({}^{-1}\) was considered to be the best option tested to induce lipid production.
# RTL-DEVS: HDL Design and Simulation Methodology for DEVS Formalism-Based Simulation Tool
## 1 Introduction
Figure 1 shows that RTL specifications based on the DEVS Formula can be written and sent via HDEVS XML (Extensible Markup Language) to other DEVS-based simulation tools, such as DEVSim++ abstraction simulator implemented in a C++ environment EventSim Event-driven Simulation Engine based on Discrete Event System Formalism with Python language, or DHIMF or DEVS2 VHDL (automatic transformation of XML-specified DEVS Model of Computation into synthesizable VHDL code). As shown in Figure 2, when we receive the Verilog description as input data, we simultaneously assign it to a DEVS format-based simulator and an open-source simulator. The final goal is to develop the RTL-DEVS co-simulator, which simulates high-cost functions that require additional development instead of using commercial RTL design tools and  
Figure 1: High scalability of DEVS-based specifications. simulates other functions using an open-source RTL tool. Verilog descriptions have already been confirmed to be transferable to other tools via XML several times, and SCXML (State Chart XML) and DEVS2 VHDL can be converted from DEVS to HDL code [7]. In this paper, we describe a methodology for designing hardware and performing gate-level synthesis and simulation in a development environment based on the DEVS format. The DEVS formalism, which includes computation and time, enables efficient and optimized design processes. By simplifying abstract hardware specifications, the DEVS simulator validates them and when the desired results are obtained, a system architect can proceed with the design process using the HDL tool. Section 2 describes the prerequisites for performing RTL simulations based on DEVS formalism. Section 3 deals with environmental modeling for RTL-DEVS simulations, and Section 4 deals with simulation results for basic component verification.
## 3 Discussion of Results from Sensitivity Analyses
### Tanks-Fuselage Integration Studies Based on Full-Section Layouts
#### 3.2.3 Multiple Groups of Tanks  
To evaluate pros and cons of using multiple groups of tanks, comparisons with the single tank cases given in Table 8 were performed. For each \((L/D_{str},t_{ins})\) pair, 2 groups of single tanks were derived, under the constraint of keeping the total LH\({}_{2}\) embarked mass unvaried (Equation (16)).  
With 2 tanks, the number of endcaps is doubled, hence the fuselage portion that needs to be transformed from the passenger cabin into the tank bay is longer. For the cases at hand, such difference leads to a reduction in 2 or 3 seat rows (16-24 passengers), depending on \(t_{ins}\) value, which leads to take-off weight reduction (2%, on average).  
Figure 21a shows that the double tank solution exhibits lower \(\mu_{tank}\) values as \(t_{ins}\) decreases, and in most cases, it also reduces the vented hydrogen amount. The weight reduction, as well as the advantages introduced in structural and thermodynamic behaviour, determine an increase in flight range, which as Figure 21b shows, is more significant as \(t_{ins}\) is reduced.  
Figure 21: Comparison between 2 tanks and 1 tank configurations with the same embarked LH\({}_{2}\): (**a**) relative \(\mu_{tank}\) and vented LH\({}_{2}\) variations, (**b**) relative range variation (reference values: 1 tank).  
Figure 20: Sensitivity to \(L/D_{str}\): (**a**) range and seat capacity, (**b**) burnt LH\({}_{2}\) per pax-km and vented mass fraction.  
By setting \(L/D_{str}\) = 2.5 and \(t_{ins}\) = 50 mm, it was observed that, compared to the case of a single group, the range increases of 6% and 10% for 2 and 3 groups of tanks were observed, respectively, whereas there is a penalisation in terms of passenger capacity (\(-10\%\) and \(-20\%\) approximatively). It is worth mentioning that a multiple tank solution allows to deal with other aspects not faced in the present work, such as aircraft balancing, stability, and control, with more flexibility.