esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Limitations of the Proposed Method of Survey Time Estimation and Future Research Directions  
When surveying with multi-beam echosounder, line spacing is influenced by water depth, data density, and overlap \"\(p\)\". In real conditions, line spacing will vary across the survey area since the water depth and terrain roughness change. The proposed method assumes the same average depths within the designated survey area, but this will never happen unless a small body of water is involved. If we are dealing with a varied bottom relief, two approaches might be considered. One of the methods of optimal selection of the survey line spacing and overlap is to divide the survey area into two separate water bodies and perform time estimations separately. The second approach is to carefully monitor the recorded depths during the survey operation and respond online by updating the spacing and overlap parameters in the project. The latter method is quite commonly used by practitioners onboard the survey vessels. Some unmanned surface vehicles (USV) are equipped with solutions consisting of autonomous calculation and selection of the line spacing based on current, on-line depth measurements, and the required overlap value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Depth [m]** & **MBES Swath Angle [\({}^{\circ}\)]** & **MBES Swath Width [m]** \\ \hline
10 m & 140\({}^{\circ}\) & 54.9 m \\
30 m & 120\({}^{\circ}\) & 103.9 m \\
50 m & 100\({}^{\circ}\) & 119.2 m \\
75 m & 80\({}^{\circ}\) & 125.7 m \\
100 m & 70\({}^{\circ}\) & 140.0 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: MBES swath width for five different water depths.  
Figure 5: Hydrographic survey effectiveness for water bodies of different depth (water area 6 \(\times\) 3 Nm).  
The quality of the final product of the hydrographic survey is directly related to the accuracy of the depth measurements performed with the multi-beam echosounder. Depth measurement accuracy is not only dependent on the accuracy of the multi-beam echosounder itself; this parameter depends on many factors, mainly on the performance of additional sensors such as the motion sensor, the gyrocompass, the sound velocity profiler, the positioning sensor, the accuracy of sensors' offsets, and so on. The issue of how different error sources contribute to the final accuracy of data as a function of survey duration is complex. To propose a comprehensive survey planning approach, future work should be focused on the integration of bathymetric data quality alongside time considerations in the survey planning. However, to create such a dependency diagram, actual measurement ought to be performed with the use of a multi-beam echosounder. Such tests could include the acquisition of real depth values on the same sea area (lake) for various settings of MBES swath angles and the estimation of the final depth measurement uncertainty. The studies should be performed at different depths. In this way, a diagram could be created taking into account the requirements of the IHO S-44 standards, the available MBES swath angles, and the time required for performing the hydrographic survey.
## 2 Potential Applications of Aeroacoustic Metamaterials  
Sustainable development of the air transport system implies that the fundamental issues related to the rapid and continuous expansion of the urban areas surrounding airports, and, in parallel, the increase in air traffic increasing the community noise levels, both have to be tackled. This has been the driving force over the last decade that has led national and international funding bodies throughout the world to provide increasing financial support to research projects focused on the mitigation of the impact of aeronautical noise on the residential community (see, e.g., the call for Mobility for Growth within the context of the _Small_, _Green and Integrated Transport_ challenge of the Horizon 2020 Program). Airlines also have an economic interest in operating quieter aircraft, as airport authorities impose the payment of fees proportional to the noise generated by their airplanes and in the future, property taxes will be introduced for owners of noisy aircraft to encourage the innovation of operating fleets and the spreading of the most advanced noise-reduction technologies. This development scenario is worsened by the apparent stagnation in the development of new technologies typically adopted on commercial aircraft, thus imposing the need for a breakthrough to guarantee further reductions in noise emissions. Metamaterials may very well prove to be the breakthrough technology needed to advance beyond the current state-of-the-art in noise reduction technology.  
The introduction of any type of metamaterial-based device in aeronautical applications is restricted in several different ways. The first condition that has to be met is the weight: an excessive weight penalty is, in general, not acceptable as it would compromise the performance and fuel efficiency of the aircraft. For this reason, concepts involving a high mass density will be excluded from our analysis. As far as community noise is concerned, take-off and landing are the most critical phases of an aircraft operational cycle. Most of the noise emitted comes from the propulsion system and the high lift devices required during climb and descent. From these particular source locations, the application of metamaterials aimed at providing significant noise reductions could be the engine nacelle, the internal ducts of the engine itself, and the trailing and side edges of the wings and flaps.
## 4 Discussion
### How Spatial Information Contributes to Field-Scale \(V_{cmax,0}^{25}\) Estimation
In summary, textures are important but may be better used as Supplementary Information to estimate field-scale \(V^{25}_{cmax,0}\). The SHAP method was also employed to interpret the AlexNet-based \(V^{25}_{cmax,0}\). Figure 8 shows the spatial distribution of SHAP on 4 typical days, clearly revealing the spatial heterogeneity in the SHAP maps. The contributions from roadway and water bodies differed obviously from those from the paddy field. When the underlying surface was bare soil, the spatial variability in SHAPs across bands was relatively low. When the paddy field was covered by rice, the SHAP variability intensified, particularly in the nir and red-edge bands. Specifically, the regions with a high contribution in these bands were closely linked to areas with a mix of vegetation and bare soil or blends of well-growing and poor-growing vegetation. As the rice matured, the spatial variability in the SHAP in the nir band weakened, while its variability in the red band intensified. To sum up, the CNN model attached much importance to the subregion with high spatial heterogeneity in the nir and red-edge bands for \(V^{25}_{cmax,0}\) estimation. This emphasis was particularly evident in regions characterized by a mix of vegetation and bare soil, and a mix of well-growing and poor-growing vegetation. The SHAP method was not used to analyze the ResNet-based model due to the complexity of ResNet, which made SHAP computation too resource-intensive. Therefore, the feature contribution of the ResNet-based model was not included in this study. It should be noted that UAV images were resampled from 6.5 cm/pixel to 1 m/pixel before being fed into the CNN due to limitations to computation capacity. Resampling large images to a smaller, manageable resolution is the most common approach, and this was employed in this study. This approach retains the global spatial information but loses the fine-scale features. The loss of spatial detail will possibly affect the extraction of textural features. Alternative strategies, such as patch-based processing [57], and patch gradient descent [58], offer promising solutions to these challenges. Developing comprehensive  
Figure 7: SHAP-based feature importance analysis for the DNN \(V^{25}_{cmax,0}\) model. The feature importance is defined as the mean of the absolute value of the SHAP for each feature (mean(\(\,\mathrm{|SHAP\,|}\,\))). Only the top 5 most important features are displayed. methods to interpret ultra-high-resolution UAV images using deep learning is a key area for future exploration.
# PPP and PPP-AR Kinematic Post-Processed Performance of GPS-Only, Galileo-Only and Multi-GNSS
## 3 Results
### Global Network of Stations  
The previous graphs from the example stations give some indications:  
* The PPP-AR mode gives better repeatability for the timeseries than the PPP mode.
* Galileo only solution gives similar level of 1-\(\sigma\) values repeatability than the GPS only solution.
* The use of Galileo can improve the current precise positioning situation of GPS when used in a Multi-GNSS combination in a PPP-AR mode.  
Nevertheless, the graphs shown in detail referred to a very small specimen of stations. It was considered important to process a network of 50 the IGS stations used in MGEX. In this way it can be investigated whether these assumptions are valid globally and whether there are any potential geographical dependencies that affect the positioning accuracy.  
Figures 3 and 4 show the overall network performance of the 1-\(\sigma\) values (as computed for Figure 2) as well and the global RMS values (the RMS of all the values plotted) for GPS PPP-AR and GPS+Galileo PPP-AR scenarios for east, north and up components.  
The following Table 7 is giving the RMS of all AR percentages for the entire network and the RMS for the whole week of processing. It is observed that in general the Galileo percentages are a little higher than the GPS ones.  
The global positioning RMS values are gathered in Table 8 for the three directions from the global maps. As it is seen, adding Galileo to the constellation can improve the positioning globally both for PPP and PPP-AR modes. Even the PPP mode of Multi-GNSS gives better accuracy than the GPS PPP-AR (which is considered as the best positioning that can be achieved until now). Ambiguity resolution  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Network AR (\%)** & **042** & **043** & **044** & **045** & **046** & **047** & **048** & **RMS** \\ \hline Galileo & 90.71 & 95.38 & 95.39 & 95.56 & 96.16 & 95.67 & 89.39 & 94.07 \\ \hline GPS & 93.55 & 92.25 & 89.85 & 93.90 & 94.09 & 94.54 & 89.62 & 92.56 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ambiguity resolution percentages for Galileo and GPS system for BRUX station.  
Figure 4: GPS + Galileo PPP-AR solutions for the network of IGS stations in East, North and Up components and their global RMS (mm).  
improves the solution about 1-3 mm in East and North directions (around 10-20% improvement) and about 0-2 mm in Up direction (around 2-8% improvement). Combining Galileo with GPS can improve the timeseries by around 3-4 mm in East and North directions and by 9 mm in Up direction. Comparing the current best positioning performance (i.e., GPS-only PPP-AR) with the one when adding Galileo (i.e., GPS+Galileo PPP-AR), we see that the results improve from 9.3 mm to 6.7 mm (28%) for the East component, 8.3 mm to 5.6 mm (33%) for the North component and 24 mm to 16.8 mm (30%) for the Up component.
Appl. Sci._**2015**, \(5\), 608-621; doi:10.3390/app5030608  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**A Study of Optical Design on 9\(\times\) Zoom Ratio by Using a Compensating Liquid Lens**  
**[PERSON] * and [PERSON]**  
Department of Electrical Engineering, National Formosa University, Yunlin 632, Taiwan;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +886-5-631-5625.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 13 July 2015 / Accepted: 11 September 2015 / Published: 21 September 2015_  
**Abstract:** This paper proposes using a liquid lens as a compensating lens and applying intermediate optics to achieve a 9\(\times\) zoom ratio. The droplet shape and location of the liquid lens can be adjusted reversibly, enabling the focal length and position of the lens to be tuned by varying the voltage applied to a set of electrodes. Therefore, liquid lenses can replace complex lenses to reduce the total length of optomechanical systems. The CODE V built-in optimization method was used to determine the optimal liquid lens parameters at various zoom ratios. Several general optical aberrations were considered. Compared with a standard intermediate optics system featuring 9\(\times\) zoom and no compensating lens, the proposed lens improved the Petzval surface curvature (PTZ) and modulation transfer function (MTF). Simulation results revealed that the PTZ decreased 10.92% and the MTF increased 3988% in 40 1p/mm.  
**Keywords:** 9\(\times\) zoom lens; intermediate optics; liquid lens; petzval surface curvature (PTZ); modulation transfer function (MTF)
## 4 Experiment Results and Analysis
### Tracking Performance of the Proposed Additive ASP Algorithm
Moreover, it is expected that the tracking performance can also be degraded if \(\hat{d}(n)\) has a large value that cannot be negligible compared with that of \(c_{2}(n)\). The degree of the tracking error depends on the amplitude and phase difference between the two signals.
## 4 Proposed Orchestrator
### Description of the Architecture
#### 4.1.3 Logical Multi-Provider Network Slices  
This block consists of two modules: the _Multi-Provider Network Slice Selector_ (please refer to Section 4.1.4) and Multi-Provider Network Slice Functions. The _Multi-Provider Network Slice Functions_ makes the interface between the _Multi-Provider Network Slice Selector_ and the multiple providers in terms of control plane (VNFs) and data plane (PNFs and VNFs).  
The infrastructure management performed by each SP uses several layers of virtualization. That is, the TIPs (composed of Core and Edge data centers) must make compute and network resources available from a multi-tier structure.  
Hence, several tools and platforms have been used to provide the _softwarization_ layers. In [99], a set of open source tools is presented for each of the layers that enable implementing 5G networks. In this work, in addition to the ONAP and OSM orchestration platforms, and the SDN controllers OpenDayLight ([[https://www.opendaylight.org/](https://www.opendaylight.org/)]([https://www.opendaylight.org/](https://www.opendaylight.org/)) accessed on 7 December 2021) and ONOS ([[https://opennetworking.org/onos/](https://opennetworking.org/onos/)]([https://opennetworking.org/onos/](https://opennetworking.org/onos/)) accessed on 7 December 2021), other tools and platforms are being evaluated. Additional modules implemented have the functionality of providing E2E horizontal orchestration, the specification of interface partners, in addition to providing traffic prediction strategies and SLA assurance using data analysis.
## 3 Results
### Gas Hydrate Inhibition Study
For CO\({}_{2}\) hydrates, the THI impact of DES depends on the concentration due to the increased activity of DES in the aqueous phase [15]. However, in this study, the difference caused by the change in concentration was not very significant, albeit shifting the equilibrium to harsher conditions. Table 5 tabulates the HLVE data for 10 wt % of TEAB-based DES. Figure 8 displays the HLVE curve of 5 and 10 wt % of TEAB-based DESs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Regression Statistics** & **TEAAC:Glycerol 5 wt \%** & **TEAAC:MEG 5 wt \%** & **TEAB:MEG 5 wt \%** & **TEAB:Glycerol 5 wt \%** \\ \hline Multiple R & 0.978026 & 0.972272 & 0.972556 & 0.991241 \\ R square & 0.956535 & 0.945313 & 0.945866 & 0.982558 \\ Adjusted R square & 0.942047 & 0.927083 & 0.927822 & 0.976744 \\ Standard error & 0.510676 & 0.483046 & 0.480595 & 0.316228 \\ _Significance F_ & 0.003897 & 0.005520 & 0.005435 & 0.000983 \\ Observations & 5 & 5 & 5 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical analysis of experimental HLVE data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Water** & **TEAB:Glycerol 10 wt \%** & **TEAB:MEG 10 wt \%** \\ \hline
**T/K** & **P/MPa** & **T/K** & **P/MPa** & **T/K** & **P/MPa** \\ \hline
277 & 2.0 & 274 & 1.9 & 274.9 & 2.0 \\
279 & 2.5 & 276 & 2.5 & 277.5 & 2.8 \\
280 & 3.0 & 277 & 3.2 & 278.0 & 3.0 \\
281 & 3.5 & 278 & 3.5 & 279.6 & 3.4 \\
283 & 4.0 & 279 & 4.0 & 280.0 & 3.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: HLVE data with and without ten wt % TEAB-based DES of 1:7 molar ratio for CO\({}_{2}\) gas. As the 10 wt % concentration of TEAB:Glycerol DES was showing good hydrate inhibition, and the authors aimed to suggest a green alternative to currently available THIs, further comparisons were made. Figure 9 displays the HLVE curve of THIs from the current work and literature. The phase boundary measurement of 10 wt % TEAB:Glycerol was compared with pure Ethylene Glycol, Ionic Liquid of 1-Butyl-3-Methylimidazolium Tetrafluoroborate (BMI-BF4), N-Ethyl Methylmorpholinium Bromide (EMMor-Br), and Ethyl-Methylmorpholinium Tetrafluoroborate (EMMor-BF4) [49, 50], as shown in Figure 9. The advantages of Ethylene Glycol are that it is recoverable,  
Figure 8: HLVE data of TEAB-based DES systems at five wt % and ten wt % concentrations. Figure 9: Comparison with conventional Thermodynamic Hydrate Inhibitors (THIs). reusable, and soluble in water. However, based on the graphical comparison, it could be easily observed that TEAB:Glycerol was a better option for thermodynamic gas hydrate inhibition.
# The Significance of Hydrophobicity for the Water Retention Properties of Sand and Coal
## 2 Materials and Methods
### Model for the Water Retention Curves  
The WRCs for the sand mixtures were predicted using a model adapted from the literature [22], with the prediction based upon the particle size distribution and void ratio of the material. The model equations utilized in this study are shown in Equations (1) and (2), and the reader is directed to [22] for the derivations and an explanation of their usage. Broadly speaking, however, the model estimates an associated pore size for each size fraction of a PSD. The draining suction and volumetric water content associated with each pore size is then determined. Thus, at any suction, the amount of undrained water in the assemblage of pores can be determined, which allows a WRC to be constructed.  
Equation (1) is an expression for the suction head \(h_{i}\) corresponding to the particle radius \(R_{i}\) of the \(i\)th particle's size fraction in a soil with \(n\) fractions and a void ratio \(e\):  
\[h_{i}=\frac{0.543\times 10^{-4}}{R_{i}}\times\frac{1.9099}{1+e} \tag{1}\]  
Equation (2) is an expression that can determine the degree of saturation \(S_{i}\) or the volumetric water content \(\theta_{i}\) at the \(i\)th particle size fraction:  
\[S_{i}\theta_{s}=\ \theta_{i}\ =\theta_{s}\bigg{[}\frac{1}{1+(\alpha h_{i})^{n}} \bigg{]}^{m} \tag{2}\]  
where \(\theta_{s}\) is the saturated volumetric water content of the material and \(\alpha\), \(n\) and \(m\) are the fitting parameters from an equation based on the van Genuchten equation [23] fitted to the particle size distribution of the materials. The fitting equation using \(\alpha\), \(n\) and \(m\) can be found in [22].  
This model uses [PERSON]'s law to estimate the suction associated with a pore size, and the contact angle (CA) during derivation was assumed to be zero [22]. [PERSON]'s law, shown in Equation (3), describes the height \(h\) to which a liquid with a surface tension \(\gamma\) rises in a capillary tube of radius \(r\):  
\[h= \frac{2\gamma\cos(\text{CA})}{\rho gr} \tag{3}\]  
In Equation (3), CA is the contact angle of the liquid to the tube wall, \(\rho\) is the density of the liquid and \(g\) is the gravitational acceleration. Note that assuming a value of zero for CA in Equation (3) causes the cosine of CA to equal 1. Thus, to accommodate materials with different CAs, the equation to calculate the suction head (Equation (1)) was slightly modified to what is shown in Equation (4):  
\[h_{i}=\frac{0.543\times 10^{-4}}{R_{i}}\times\frac{1.9099}{1+e}\times\cos(CA) \tag{4}\]Thus, the modified model (Equations (2) and (4)) can consider materials with a non-zero CA with the receding angle used in this study as it is representative of the WRCs that were measured along the drying path.
# Analysis of Road Sign-Related Factors Affecting Driving Safety with Respect to City Size
## 1 Introduction  
The introduction of navigation systems in the 2000s and the popularization of smartphones since 2010 have provided the public with accessible sources of information on destinations and road networks, thereby diminishing the role of road signs in ensuring driving safety. However, certain portions of the population, e.g., the elderly, are not well versed with such technology. Further, communication networks may fail without warning, limiting access to digital information. As a result, road signs continue to be essential to driving safety. In 2002, the Ministry of Land, Infrastructure and Transport designated the Korea Institute of Construction Technology as the Comprehensive Road Sign Management Center (henceforth referred to as \"Road Sign Center\") and entrusted it with the operation and management of road signs. The Road Sign Center performs comprehensive road sign-related tasks, e.g., operating the road sign information management system based on the road sign system, reviewing existing and new road signs, amending road sign rules, and providing other technology, policy, and research support. Completed tasks to ensure driving safety include developing illuminated signs, improving reflectivity, limiting the number of signs installed above lanes spanning the road, increasing font size and brightness, and unifying English notation. However, these tasks were conducted on the basis of individual requirements in the past--systematic studies have not been conducted to evaluate factors affecting driving safety comprehensively. Additionally, driving environments on roads and road information density depend on the city size; however, such factors have not been adequately researched. In this study, road sign-related factors affecting driving safety were classified and analyzed based on city size using a road sign recognition survey, and certain implications were derived.
## 3 Damage Identification Paradigm
### Hierarchical Neural Network Model  
The synergetic action of AANNs and LMNNs constructs a hierarchical neural network model for intelligent damage assessment, as shown in Figure 4. In the process of damage identification, AANNs are responsible for damage feature extraction and LMNNs account for damage pattern recognition. Distinctive features of the proposed model in damage assessment are:  
1. WPNEs are much more sensitive to damage than WPT coefficients, natural frequencies, and mode shapes.
2. AANNs acting as a smart NLPCA tool can extract damage features from WPNEs. Such extracted damage features have lower dimensionality than WPNEs while preserving enough damage information.
3. LMNNs can capture the underlying relations between damage features and damage states, on which they can recognize structural damage patterns.
4. The special structure of the hierarchical neural network model requires a small set of training samples of damaged cases to produce accurate prediction results of damage identification with great noise robustness.
5. The hierarchical neural network model is easily implemented in a computational language, e.g., Matlab, to create an automatic program of intelligent damage assessment.  
Figure 3: Levenberg-Marquardt neural networks (LMNNs) for damage pattern recognition. (**A**) input layer; (**B**) hidden layer; (**C**) output layer.