esa-sceva/satcom-chunk-collection huggingface dataset:
# The Usage of Virtual and Augmented Reality in Underwater Archeology
## 5 Use of Augmented and Virtual Reality in Underwater Archeology--Example Solutions
### Mercurio Shipwreck  
Activities related to the creation of a VR application based on the archaeological site--the Mercurio wreck, were carried out in 2016-2017 as part of the project entitled \"Restituzione 3D di Relitti Antici Sommersi finalizzata alla realizzazione di Musei virtual con realta immersiva e aumentata\". This initiative was implemented thanks to the European Social Fund, with financial resources from the Veneto region, Italy [100].  
The 19 th century brig Mercurio was sunk during the Battle of Grado in 1812. It currently lies at a depth of 17 m in the northern Adriatic Sea. Artifacts found during research on the wreck were recovered and are currently exhibited in the Maritime Museum of Caorle (Museo Nazionale di Archeologia del Mare of Caorle, near Venice), where a multimedia station has also been installed to make the exhibition there more attractive [100]. The application has been available to visitors since July 2018. The created virtual application uses HMD display technologies (the \"Oculus Rift\" model was selected) and is integrated with other multimedia applications and displays available in this museum [100].  
The application allows you to virtually visit the shipwreck in the condition it was in at the end of the excavations. Currently, the underwater site has been protected against corrosion processes in the sea using a geotextile, which makes access to it impossible in reality. Therefore, a virtual tour is de facto the only possibility to see the wreck [100]. The virtual application, in addition to a realistic reconstruction of the wreck, also contains elements of the seabed and artificially generated elements of flora and fauna, e.g., eels, shells, and seagrass [100]. Additional elements have also been added, such as a decrease in water transparency, a decrease in the amount of light with depth, and a dirty mask simulation, which are intended to achieve a realistic virtual underwater scenario [100]. Moreover, selected artifacts from the wreck, i.e., a cannon and a gun, contain additional information that can be reproduced during a virtual dive [100].
# Evaluating the Effectiveness of Text Pre-Processing in Sentiment Analysis
## 3 Materials and Methods
### Sentiment Classifiers
#### 3.4.3 Naive Bayes Classifier  
Naive Bayes classifiers are based on the Bayes' theorem with the \"naive\" assumption of conditional independence between every pair of features. Naive Bayes has proved useful in many real-world situations--for instance, spam filtering [76]--and requires a small amount of training data to estimate the necessary parameters.  
Generally, naive Bayes performs its tasks faster than other more sophisticated methods [77]. In our case, we used it to implement sentiment analysis as a two-category classification question: _positive or negative?_--neutral tweets were removed.
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Audio Material  
The recordings were of two groups of vehicles, ICEVs and HEVs. In total, 20 units were measured--in Japan, 8 HEVs and 2 ICEVs, whereas in Italy 2 HEVs and 8 ICEVs. In all cases, HVAC noise was measured at the front-left seat inside each vehicle, i.e., the driver position in Italy, the passenger position in Japan. The background noise inside the vehicles was less than 35 dB in the equivalent continuous A-weighted sound pressure level (\(L_{\text{Aeq}}\)). Measurements were carried out with a 4-channel measuring system. This consisted of a SQobold, a binaural headset (Head Acoustics/BHS II) positioned on a Mk2 Cortex manikin in Italy; and a 2-channel head and torso simulator (HATS Type 4100, Bruel & Kjaer), in Japan. The playback sound pressure level was calibrated using, respectively, a binaural Mk2 Cortex manikin, and a head and torso simulator HATS. The position of the right microphone of the HATS/BHS II was approximately 0.80 m from the closest central outlet and about 0.70 m above the seat. During the measurements, all the windows and doors were kept closed, and all the air outlet valves were fully opened and slightly oriented to the floor to prevent noise induced by airflow on the microphones. Recordings were made compatible using a comparative measurement carried out at the Kyushu University and by developing equalizer filters. Each recording lasted 30 s and considered different Function Modalities (FMs), Air Outlets (AOs), and Air Flow Rates (AFRs) [4]. Sections having a length of 5 s were analyzed by the software (Artemis Suite, Head Acoustics) to extract representative audio samples. To avoid a click noise, 60 ms fade-in and 80 ms fade-out were processed for each stimulus. The excerpts were then grouped into two Functional Modalities (Ventilation and Air-Conditioning) and Engine Types (ICEV and HEV) as shown below:  
* VE: Ventilation driven by Internal Combustion Engine;
* AC: Air Conditioning driven by Internal Combustion Engine;
* EVE: Ventilation driven by Electric Engine;
* EAC: Air Conditioning driven by Electric Engine.  
Concerning the Air Outlets, only the frontal one was considered because it represents the most common and frequently used. Furthermore, concerning the Air Flow Rates, four AFRs were considered, when the ventilation power was at 25%, 50%, 75% and 100%.  
Finally, the collected samples were compared, and those within one standard deviation on the A-weighted sound pressure level were chosen. In total, 48 HVAC samples were used in the experiment--2 FM \(\times\) 4 AFR for each test vehicle, i.e., 3 ICEVs + 3 HEVs.
## 4 Testbed and Experiments
### Testbed  
To evaluate the performance of AIM5 LA and the proposed message protocol, several scenarios were designed. The first scenario was used to train AIM5 LA to find an advanced control policy that takes into account the wireless communication latency. The second scenario aimed to show the performance of AIM5 LA in a scenario never seen before by the algorithm during training to demonstrate that AIM5 LA can find robust control despite confronting unknown traffic and latency situations. In addition, before carrying out the experiments in these scenarios, the latency predictor module had to be optimized in a scenario using the Simu5G 5G computer simulation tool [32].  
AIM5 LA derives its control policy from DRL [34] and makes use of other advanced techniques and algorithms such as TD3 [22], PER [35], and curriculum learning [21] to accelerate training and maximize learned knowledge.  
To simulate the wireless communication channel and protocol, the 5G simulator Simu5G 1.2.0 [32] was used, along with OMNeT++ 5.6.2 [36] and INET 4.2.2 [37]. Simu5G  
Figure 3: Message diagram proposed for AIM5 LA. In this example, there are 3 vehicles to control. The _probe_ message is a broadcast message.  
is an OMNeT++ library for the end-to-end performance evaluation of 5G networks. It was decided to investigate latency in the 5G mobile communication protocol since it is a widely developed and researched protocol with ultra-low expected latency (1 ms). It focuses on assessing Vehicle-to-Network (V2N) use cases over the 5G New Radio (NR) and follows the Rel-16 specification to model the 5G NR radio access network and the data plane. In this case, the IM was simulated as a 5G gNodeB node and the vehicles, as 5G devices (NrUe-NrCar), were configured to offer low latency and throughput similar to that offered to the URLLC use case.  
For the simulation of vehicles, their behavior, and urban roads, SUMO 1.8.0 [38] was used together with TraCI [39] for vehicle control using AIM5 LA.  
For the development of AIM5 LA, as well as all the deep learning algorithms, Python 3.8.10 was used together with the deep learning framework PyTorch 1.9.0 with CUDA [40]. As for the hardware, an Intel 8-core/16-thread processor (if-7-11700k) and an Nvidia RTX 3080 graphics card were used. Table 1 summarizes the parameters used in the different simulators.
###### Abstract  
Flavonoid-rich leaves of the _Ficus carica_ L. plant are usually discarded as waste. In this work, ultrasonic enzyme-assisted aqueous two-phase extraction (UEAATPE) was proposed as an innovative method to estimate the total flavonoids present in _F. carica_ L. leaves. Total flavonoids were analyzed qualitatively and quantitatively by UPLC-QTOF-MS. At 38% (\(w/w\)) ethanol/18% (\(w/w\)) ammonium sulfate, we achieved the optimum conditions in which to establish an easy-to-form aqueous two-phase extraction (ATPE) as the final system. The optimal UEAATPE conditions were set at an enzymatic concentration of 0.4 U/g, 150 min enzymolysis time, an enzymolysis temperature of 50 \({}^{\circ}\)C, a liquid-solid ratio of 20:1 (mL/g), and 30 min ultrasonic time. The yields of the total flavonoids, i.e., 60.22 mg/g, obtained by UEAATPE were found to be 1.13-fold, 1.21-fold, 1.27-fold, and 2.43-fold higher than those obtained by enzyme-assisted ATPE (EAATPE), ultrasonic-assisted ATPE (UAAITE), ATPE, and sokhlet extraction (SE) methods, respectively. Furthermore, eleven flavonoids from the leaves of the _F. carica_ L. plant were completely identified and fully characterized. Among them, ten flavonoids have been identified for the first time from the leaves of the _F. carica_ L. plant. These flavonoids are quercetin 3-O-hexobioside-7-O-hexoside, 2-carboxy-1,4-naphthhydroquinone-4-O-hexoside, luteolin 6-C-hexoside, 8-C-pentoside, kaempfer 6-C-hexoside-8-C-hexoside, quercetin 6-C-hexobioside, kaempfer 6-C-hexoside-8-C-hexoside, apigenin 2\({}^{\prime\prime}\)-O-pentoside, apigenin 6-C-hexoside, quercetin 3-O-hexoside, and kaempfer 3-O-hexobioside. Therefore, _F. carica_ L. leaves contain new kinds of unidentified natural flavonoids and are a rich source of biological activity. Therefore, this research has potential applications and great value in waste handling and utilization.  
Ficus carica L.; flavonoids; ultrasonic enzyme co-assisted; aqueous two-phase extraction; UPLC-QTOF-MS/MS; identification +
Footnote †: journal: Applied Sciences
## 4 Example of Application: Performance Simulations using the EIPS framework
### End-to-End Simualtion Settings and Inputs  
We first start by setting up three hypothetical MW instrument configurations namely A, B and C. Configuration A and B are based on existing multi-spectral MW instrument technology and C is based on recently developed technology (see Section 4.2.2). Some of the parameters for all the three instrument configurations have been taken as common to avoid any complexity in comparing the simulated results in context of this paper. These common parameters were set up in the EIPS, and are shown in the Table 1. EIPS component models require different inputs to start the end-to-end simulation process such as: atmospheric profiles (as an input to the HT-FRTC model) and a background error covariance matrix (as an input to the information and error analysis model).  
For input to the HT-FRTC model, a set of eight atmospheric profiles have been taken for calculating Jacobians. These atmospheric profiles are on the 70 Met Office Unified Model (UM) levels and spanning range of latitudes and atmospheric conditions. Out of the eight profiles, profile 1 is the US standard atmosphere and seven of these profiles (Profile 2 to 8) have been extracted directly from the Met Office UM in March 2012. These profiles have been used in recent studies in [26; 29]. The background error-covariance matrix was constructed using a randomisation method. The matrix is a 1D representation of the temperature-based component of the 4D-Var error covariance which was in operational use at the Met Office in March 2012. The **B** matrix used here was described in detail and used in experimental simulations in [26] and in [30].
# Electronic Properties of Electron-Deficient Zn(II) Porphyrins for HBr Splitting
###### Abstract  
Two different high potential Zn(II) porphyrin designs carrying either 4 or 5 _meso_ pentafluorophenyl moieties as electron acceptor groups and a further electron withdrawing branch inserted in either the \(\beta\) (**1**) or _meso_ (**2**) position were tested in photoelectrosynthetic cells for HBr splitting. Photoaction spectra in the presence of HBr showed that red photons up to 700 nm could be harvested and converted and that **2** performed better than **1**, thanks to better electronic properties of the excited state, favored by the insertion of the benzothiadiazole electron withdrawing group. Photoanodic performances in the presence of HBr, however, remained low, due to inefficient regeneration of the oxidized sensitizer as a result of an insufficient driving force for Br\({}^{-}\) oxidation.  
Keywords:porphyrins; SnO\({}_{2}\); HBr splitting; sensitization +
Footnote †: journal: Applied Physics Letters
## 5 Analysis and Discussions
Plots of the differences defined above are presented in Figure 15. As can be seen, the maximum values of \(\Delta\)T are \(2.5\cdot 10^{-3}\)\({}^{\circ}\)C for the difference between the NDS and ADS (Figure 14a) samples and \(18\cdot 10^{-3}\)\({}^{\circ}\)C for the difference between the CDS and CADS samples (Figure 14b), respectively. This difference between samples without a central defect and those with such a defect is significant but not observable in real conditions. Figure 16 illustrates a comparison between the characteristics acquired from the regions of interest for the matching numerical and experimental results. One graph displays the characteristics obtained for the CADS sample (Figure 13d) and the real sample (Figure 13e). The indicated outcome demonstrates a high level of consistency. The highest disparity between the characteristics is around 0.7 \({}^{\circ}\)C. The agreement observed confirms the accuracy of the assumptions made in the developed numerical model. Figure 14: The comparison between the computed mean values for the chosen ROI: (**a**) NDS (blue line) vs. CDS (orange line); (**b**) ADS (blue line) vs. CADS (orange line). (**c**) Experimental results: orange line—ROI covering the defect; blue line—ROI covering the non-defected area.
## Nomenclature  
\begin{tabular}{l l} Notation & \\ \(A\) & flow annulus area m\({}^{2}\) \\ \(Aav\) & Average area m\({}^{2}\) \\ \(C_{p}\) & specific heat capacity, J/kgK \\ CW & Compressor work, W \\ \(d\) & diameter, m \\ \(\hat{\epsilon}_{T}\) & Total exergy, kW \\ FAR & Fuel-air ratio \\ \(F\) & Thrust, N \\ \(F_{N}\) & Net thrust, N \\ \(F_{b}\) & Cold thrust, N \\ \(F_{c}\) & Core thrust, N \\ \(FW\) & Fan work, kW \\ \(h\) & Enthalpy, kJ/kg \\ \(LH_{2}\) & Liquefied hydrogen \\ \(LHV\) & Low heating value J/kg \\ \(LPTW\) & Low-pressure turbine work, kW \\ P & Pressure, Pa \\ \(P_{r}\) & Power required, kW \\ SFC & Specific fuel consumption \\ \(t_{f}\) & Rupture time to creep failure, s \\ T & Temperature, \({}^{\circ}\)C \\ TET & Turbine entry temperature, \({}^{\circ}\)C \\ T\({}_{\rm m}\) & Blade metal temperature, \({}^{\circ}\)C \\ TW & Turbine work, W \\ U\({}_{\rm m}\) & Mean speed m/s \\ \(V_{0}\) & Free stream velocity, m/s \\ \(\dot{m}\) & Mass flow kg/s \\ \(m_{f}\) & Fuel flow, kg/s \\ \(\rho\) & Density, \(kg/m^{3}\) \\ \(\eta_{ex}\) & Exergy efficiency \\ \(\Delta h\) & Enthalpy change \\ \(\Delta P\) & Pressure change \\ \(\Delta T\) & Temperature change \\ \(\omega\) & Angular speed, m/s \\ Abbreviations & \\ C & Compressor \\ CFD & Computational fluid dynamics \\ DP & design point \\ HPC & High-pressure compressor \\ HPT & High-pressure turbine \\ IPC & Intermediate-pressure compressor \\ IPT & Intermediate-pressure turbine \\ LPT & Low-pressure turbine \\ ORC & Organic Rankine cycle \\ PF & Pressure force \\ PR & Pressure ratio \\ T & turbine \\ TRL & Technology readiness level \\ SLS & Sea level static \\ Subscript & \\ \(c\) & compressor \\ \(t\) & turbine \\ \(a\) & ambient \\ \(0\)-\(8\) & station number \\ \end{tabular}
## 7 SensorNC Applications
### Optimal Sink Placement
#### 7.1.2 Large-Scale WSNs: Comparison between MCP and GASP  
The question we address in this experiment is whether the GASP constitutes a more intelligent search strategy than a pure random search like the MCP. In fact, there would even be the possibility that the MCP could outperform the GASP. This would be the case if the GA operators were poorly designed and would mislead the GASP in areas of the search space that are fruitless. Therefore, in a certain sense, the following experiments also validate the design of the GASP heuristic.  
We investigate a 500-node network with up to seven sinks for 10 different scenarios, i.e., 10 different node distributions. For each of the scenarios, this resulted in approximately 13,000 candidate sink locations, so that at maximum, the search space becomes as big as  
Figure 11: The worst case delay comparison of OSP vs. GASP.  
Figure 12: The worst case delay comparison of MCP vs. genetic algorithm-based heuristic for sink placement (GASP).  
\(1.24\times 10^{25}\). On the other hand, for the GASP, we used a population size of 100 with 100 generations until termination, resulting in 10,000 sink placements being evaluated for their worst-case delay. We allow the same amount of evaluations to the MCP. In any case, it is clear that this amount of sink placement evaluations constitutes only a tiny fraction of the overall search space.  
The results (averaged over the 10 scenarios) of these experiments are shown in Figure 12. This analysis shows that the GASP performs better than the MCP at the same amount of computational effort. For the GASP strategy, the worst-case delay improves from 12 s to 5.7 s to 4.1 s to 3.2 s to 2.7 s to 2.3 s to 2.0 s for the one- to seven-sink scenarios, respectively. For the MCP, the worst-case delay improves from 14.1 s to 6.6 s to 5.0 s to 4.2 s to 3.3 s to 3.2 s to 2.5 s for the one- to seven-sink scenarios, respectively. The delay difference between the two strategies is roughly between 0.5 s and 2 s. This shows that the GA operators do something sensible as the GA search improves on the pure random search of the MCP. Besides, these numbers also give a feeling what the delay vs. number of sinks tradeoff looks like. Moreover, the confidence interval of MCP varies from 0.1 s to 1.2 s, whereas GASP varies from 0.05 s to 0.8 s. Obviously, providing a second sink improves the delay performance very much, whereas further sinks have a lesser effect on the maximum worst-case delay observed.
## 4 Distance-Based Energy Aware Routing (DEAR) Algorithm
### DEAR Algorithm  
In the DEAR algorithm, each sensor node has two tables. One is the routing table which contains information like source node, previous and next hop node, _etc_. The other table is the neighbor table which contains neighbors' information like distance between them, distance to the sink node, residual energy, node degree, _etc_. Thus, each node can make intelligent decisions about the next hop based on the DEAR algorithm and the algorithm is easy to implement for practical engineering applications.  
The key strength of DEAR algorithm is that given the source to sink node distance d and hardware parameters in Table 1, we can provide energy efficient route with the optimal multi-hop number and corresponding individual distance under the practical sensor network. The energy will be consumed at  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} \hline \(\mathbf{n}\) & \(\mathbf{d_{1}}\) & \(\mathbf{d_{2}}\) & \(\mathbf{d_{3}}\) & \(\mathbf{d_{4}}\) & \(\mathbf{d_{5}}\) & \(\mathbf{d_{6}}\) & \(\mathbf{d_{7}}\) & \(\mathbf{d_{8}}\) & \(\mathbf{d}_{i}\) \\  & & & & & & & & & & 9 \\ \hline
2 & 100.0 & 0 & & & & & & & 100.0 \\ \hline
3 & 118.9 & 84.1 & 0 & & & & & & 203.0 \\ \hline
4 & 131.6 & 100.0 & 76.0 & 0 & & & & & 307.6 \\ \hline
5 & 141.4 & 110.7 & 90.4 & 70.7 & 0 & & & & 413.2 \\ \hline
6 & 149.5 & 119.0 & 100 & 84.1 & 66.9 & 0 & & & 519.5 \\ \hline
7 & 156.5 & 125.7 & 107.5 & 93.1 & 80.0 & 63.9 & 0 & & 626.7 \\ \hline
8 & 162.7 & 131.6 & 113.6 & 100.0 & 88.0 & 76.0 & 61.5 & 0 & 733.4 \\ \hline
9 & 168.2 & 136.8 & 118.9 & 105.7 & 94.6 & 84.1 & 73.1 & 59.5 & 0 & 840.9 \\ \hline \end{tabular}
\end{table}
Table 2: Corresponding \(d_{i}\) when hop number \(n=[2.9]\).  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline d & 800 & 900 & 1000 \\ \hline \multirow{3}{*}{\(d_{1}(n)\)} & \(\mathrm{d_{1}(8)=164.8}\) & \(\mathrm{d_{1}(9)=169.7}\) & \(\mathrm{d_{1}(10)=174.3}\) \\ \cline{2-5}  & \(\mathrm{d_{1}(7)=170.5}\) & \(\mathrm{d_{1}(8)=174.2}\) & \(\mathrm{d_{1}(9)=177.8}\) \\ \cline{1-1} \cline{2-5}  & \(\mathrm{d_{1}(6)=183.7}\) & \(\mathrm{d_{1}(7)=184.7}\) & \(\mathrm{d_{1}(8)=186.3}\) \\ \hline \end{tabular}
\end{table}
Table 3: \(d_{1}(n)\) under different \(d\).  
similar rate for all sensor nodes so that energy efficiency and energy balancing can be achieved and network lifetime can be prolonged.
The basalt fiber reinforcement had little effect on the effective internal friction angle of the clay, though the effective cohesion was significantly increased, suggesting an optimal fiber content of 0.3%. Through triaxial tests, [PERSON] et al. [10] found that the strength of glass fiber-reinforced soil increased with increases in the fiber length, fiber content, confining pressure, and soil compactness, but an increase in water content had the opposite effect. [PERSON] et al. [11] tested the unconfined compressive strength of waste carpet fiber-reinforced soil and found that the fibers increased the unconfined compressive strength, and the resilience of the soil was effectively improved, with an optimal fiber content of 5%. Through the unconfined compressive testing of fiber-reinforced soil, [PERSON] et al. [12] found that for polypropylene fiber-reinforced soil, the optimal fiber content was 0.2%, and the optimal fiber length was 15 mm; however, for rubber fiber-reinforced soil, the optimal fiber content was 2%, and the optimal fiber length was 10 mm. [PERSON] et al. [13] studied the effects of fiber length and content on the shear strength of fiber-reinforced soil and found that with increasing fiber length and content, the contact area between the fibers and soil particles increased, and the shear strength increased significantly. Thus, existing research has shown that the strength of fiber-reinforced soil is influenced by factors such as fiber characteristics, soil properties, and external factors, with fiber content having the most significant effect [14; 15]. Research on fiber-reinforced soil has mainly focused on the reinforcement effect and soil mechanical properties, while research on strength prediction and building constitutive models for fiber-reinforced soils is relatively rare. This study conducted a series of triaxial compression tests on polypropylene fiber-reinforced subgrade fill to analyze the effects of three parameters, namely, fiber content, fiber length, and confining pressure, on the stress-strain relationship and failure strength. Furthermore, this study established an empirical model for predicting the failure strength of fiber-reinforced soil and determined the best mechanical model and parameter calculation method. The results of this paper can provide technical guidance for the engineering of reinforced railway subgrade.