esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper presents a novel optimal design method for a chiller plant based on life-cycle total cost optimization. By quantitatively considering the uncertainties of design inputs and the reliabilities of equipment in operation for a chiller plant, the proposed method can ensure a high energy efficiency and sufficient cooling supply under various cooling load scenarios. Minimized life-cycle total cost can be achieved by optimizing the total cooling capacity and the configurations of a chiller plant (i.e., numbers/sizes of chillers). A case study has also been conducted to validate the proposed method. The main conclusions are drawn as follows:  
* The searching range of total cooling capacity of a chiller plant can be determined by the cooling capacities with predefined unmet hours under the cooling load distribution. In the case study, the minimum cooling capacity is 5100 kW and the maximum cooling capacity is 6600 kW.
* Although the chiller plant with one variable-speed chiller might operate at a lower efficiency than that with two variable-speed chillers, the design option with one variable-speed chiller is still more economical when the life-cycle total cost is considered.
* The design option of a chiller plant using the proposed method can operate with a relatively high efficiency, and has a relatively sufficient cooling capacity to supply the cooling under various cooling load scenarios. The lowest total cost can be achieved by considering both design uncertainties and reliabilities for a chiller plant.  
Figure 10: The costs of the optimum configurations using different design methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Total Cooling** & **Best Configuration (Size** & **Availability Risk** & **Operation Cost** & **Total Cost** \\  & **Capacity (\&W)** & **(\&W)** \(\&\)**Number)** & **Cost (10\({}^{3}\) HKD)** & **(10\({}^{3}\) HKD)** \\ \hline Robust optimal design & 6300 & 1300 \(\times\) 4-CCD \(\times\) 1100 \(\times\) 1 VSD & 131 & 3463 & 4222 \\ Uncertainty-based design & 5100 & 1400 \(\times\) 3-CCD \(\times\) 900 \(\times\) 1 VSD & 656 & 3495 & 4765 \\ Conventional design & 5100 & 1700 \(\times\) 3-CCD & 992 & 4318 & 5703 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal configurations using different design methods (penalty ratio: 10 HKD/kW).  
* Results show that the life-cycle total cost of optimal and robust design is reduced by 26% and 11.4%, respectively, when compared with those from uncertainty-based and conventional design methods.  
Conceptualization, C.Y.; investigation, C.Y.; methodology, Q.C.; supervision, C.Y.; validation, Q.C.; writing--original draft, Q.C.; writing--review and editing, H.C.  
The research presented in this paper was financially supported by a grant from the National Natural Science Foundation of China (51708287) and a grant from the Natural Science Foundation of Jiangsu Province (BK20171003).  
The authors declare no conflict of interest.
# Mechanical-Empirical Pavement Design Guide Applied to Portuguese Pavement Structures
## 2 The MEPDG Design Methodology
### Traffic  
While the AASHTO 1993 pavement design methodology requires the number of 18-kips Equivalent Single Axle Load (ESAL) as the only traffic input, the MEPDG requires four main traffic inputs for the design of pavement structures [3; 20; 21]: (1) base year truck traffic volume; (2) traffic volume adjustment factors; (3) axle load distribution factors; and (4) general traffic inputs. The traffic volume adjustment factors are used to adjust the base year traffic volume. These adjustment factors are the monthly adjustment factors (MAF), vehicle class distribution (VCD), hourly truck distribution (HTD), and traffic growth factors. The general traffic input data include the number of axles per truck, axle configuration, tire pressure, traffic wander, and wheel base. The MEPDG considers 10 different vehicle classes, ranging from class 4 to class 13 as presented in Figure 2.  
In Portugal, traffic classes are not equal to the traffic classes presented in Figure 2. There are 11 classes, ranging from A to K [22]: classes A and B correspond to bicycles; class C corresponds to motorcycles; classes D and E correspond to cars; and the remaining classes (F, G, H, I, J, and K) correspond to trucks. For to this reason, detailed traffic data are required to convert the 6 classes of trucks considered in Portugal into the 10 classes considered in the MEPDG. In Portugal, there is not enough information to achieve level 1; however, using all the available information, level 2 can be achieved. Level 1 requires, for example, axle load spectra (ALS), also called axle load distribution factors, which can only be determined from WIM data.
## 5 Experimental Analysis
### Example for Evaluating Uncertainty of Flatness Measurement  
According to Formula (9), the uncertainty component caused by indication errors can be calculated as follows in flatness measurement:  
\[u_{\mathrm{E}}=\frac{\mathrm{MPE}_{P}}{\sqrt{3}}=\frac{3.5}{\sqrt{3}}=2.021\ \mathrm{\upmu m} \tag{33}\]  
Similarly, under the condition of repeatability, complete 10 times' continuous and rapid measurement of the plane to be measured, calculate the standard deviation of the repeatability experiment as \(S_{\mathrm{r}}\) = 0.618 \(\mathrm{\upmu m}\); if the average value of three times' measurements is taken as the best estimate of the flatness measurement, then the standard uncertainty caused by measurement repeatability is as follows:  
\[u_{\mathrm{r}}=\frac{S_{\mathrm{r}}}{\sqrt{3}}=0.357\ \mathrm{\upmu m} \tag{34}\]  
Similarly, complete the repeatability measurements by three surveyors and try to have the sampling strategy and other factors kept as different as possible, thus obtaining the standard uncertainty caused by the reproducibility of flatness measurement as \(u_{\mathrm{R}}=0.915\ \mathrm{\upmu m}\).  
Then see Table 5 for the uncertainty component for flatness measurement:  
The combined standard uncertainty is as follows for flatness measurement tasks:  
\[u_{\mathrm{c}}=\sqrt{u_{\mathrm{E}}^{2}+u_{\mathrm{r}}^{2}+u_{\mathrm{R}}^{2} }=2.3\ \mathrm{\upmu m} \tag{35}\]  
Similarly, the expanded uncertainty is as follows:  
\[U=k\times u_{\mathrm{c}}=4.6\ \mathrm{\upmu m} \tag{36}\]
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 8 LoRa/LoRaWAN/Wi-Fi Use Cases  
### USV: Ship Obstacle Avoidance  
On the channels of the Danube Delta, silting frequently appears as a consequence of the water level decrease. The installation of classical guiding buoys represents a solution to avoiding the affected regions. However, they do not cover the entire affected area or are not installed in time when the water level is modified. A solution consists of using autonomous aquatic vehicles with small dimensions, equipped with GPS modules and ultrasonic sensors for constant monitoring of the navigation channels and mapping the underwater land-forms under continuous change. The same vehicles are also able to  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(n_{UAV}\) & \(n_{USV}\) & \(n_{vn}\) & \(d\) (km) & \(\rho\) & \(\delta d\) (m) & \(t_{2}\) (s) & \(\sigma\) \\ \hline \multirow{4}{*}{7} & \multirow{4}{*}{2} & \multirow{4}{*}{9} & 0.05 & 20 & 32.4113 & 0.8807 \\  & & & & 0.1 & 20 & 32.1097 & 0.8839 \\  & & & & 0.12 & 20 & 31.9687 & 0.8783 \\  & & & & 0.13 & 20 & 32.0568 & 0.8792 \\  & & & & 0.14 & 20 & 32.1442 & 0.8801 \\  & & & & 0.15 * & 20 & 31.9802 & 0.8809 \\  & & & & 0.2 * & 20 & 31.8643 & 0.8844 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Algorithm results: execution time (after time execution improvement) and performance indicator \(\sigma\) (\(nt=3\), \(LB=115\) dB, \(d=9.3362\) km).  
identify obstacles, such as fallen trees etc. For improved autonomy, these vehicles can be connected by employing the LoRa communications technology.
## 3 Temperature Tide Identification  
### Calculation of Tidal Characteristics  
All the disturbances induced by dynamic parameters can propagate in the whole atmosphere. Diurnal signals (24 h) induced by tidal forcing can be detected everywhere, in addition to harmonics, with the former exhibiting a period of 12 h. In practice, strong interactions occur between planetary waves and tides up to the thermopshere [29].  
To extract both components of the atmospheric tides from temperature series, data are fitted using a combination of 12- and 24-h oscillations. Tidal anomalies as a function of time (\(t\)) can be represented by the following formula as defined in the climatological model [20]:  
\[\Delta T(t)=\alpha_{12}\cos\left[2\pi.f_{12}.t+\phi_{12}\right]+\alpha_{24} \cos\left[2\pi.f_{24}.t+\phi_{24}\right] \tag{1}\]  
with \(\alpha_{i}\) being, respectively, semi-diurnal and diurnal amplitudes expressed in Kelvin. The time (\(t\)) is given in hours, with the frequency \(f_{i}\) being in 1/h. \(\phi_{i}\) is the phase given in radians, and the ratio between the time of maximum (in hours) and \(f_{i}\), the frequency of tidal oscillations, is also expressed in 1/h. The respective values for the semi-diurnal and diurnal components as well as their respective phases (time of maximum) vary at each grid point. Two weeks have been used as a good compromise to improve the signal-to-noise ratio and keep a relatively low uncertainty. In the example presented in Figure 1, the diurnal mode dominates, and amplitudes of the tides vary slightly with time while the mean temperature varies, complicating calculations of tide estimates.
## 3 Trade-Off Between Efficiency and RX-Band Noise  
In the ET PA, the operating point of the PA is an important factor in determining efficiency and RX-band noise. If the PSRR is not an important factor (TDD system, good duplexer isolation, good SM noise), case 1 in Figure 4, having the highest PAE (Power-Added Efficiency) and a poor PSRR\({}_{\text{SSB}}\), is a suitable operating curve. If the PSRR is an important factor (FDD system, poor duplexer isolation, poor SM noise), case 2, having a good PSRR\({}_{\text{SSB}}\) and reduced PAE, is a suitable operating curve. Figure 5 shows the characteristics of the PA according to the saturation level in ET operation. As shown in this figure, the efficiency of the PA and RX-band noise (or PSRR) are in a trade-off relationship with each other, and the operating curve should be carefully determined by the system requirements.  
Figure 4: Simulated PAE, gain, and calculated PSRRSSB curves of PA at each supply voltage (I V to 3.5 V with 0.5 V step).  
Figure 5: Average PAE, gain, output power, and PSRRSSB according to saturation level in ET operation.
# Mitigation of Gravity Segregation by Foam to Enhance Sweep Efficiency
## 1 Introduction
[18] later provided further insight into the subject and demonstrated that Equations (1) and (2) demonstrate strict accuracy when both steady-state conditions and the standard assumptions of fractional-flow theory are employed. The degree of reservoir heterogeneity exerts an influence on the precision of the segregation length as an indicator of sweep efficiency. [PERSON]. [19] conducted a study that showcases the satisfactory accuracy of Equations (1) and (2) when applied to mildly heterogeneous reservoirs characterized by layers or checkerboard patterns. In order to properly account for the heterogeneity, it is necessary to modify the vertical permeability accordingly. Nevertheless, in instances of heightened reservoir heterogeneity, the effectiveness of \(L_{g}\) as a measure of sweep efficiency may be compromised. Additionally, as foam can be used to regulate gravity segregation during coinjection, extending this model to foam flow may be a useful tool for optimizing water-gas coinjection and improving sweep efficiency. This model was expanded to encompass the phenomenon of foam flow, given the condition of uniform injection along the wellbore, notwithstanding the intricate nature of foam behavior [20]. The gravity-segregation model developed by [PERSON] has been extended to dipping reservoirs through numerical simulations [21; 22; 23] or an analytical model that makes some assumptions that may not be strictly accurate [24; 25]. Nevertheless, limited investigations were conducted concerning the impact of reservoir heterogeneity on the phenomenon of gravity segregation in foam-enhanced oil recovery (EOR) procedures. In this work, we employ an implicit-texture (IT) model, called the 'STARS' model, which assumes local steady-state conditions to simulate foam generation and destruction [26]. The model integrates the influence of foam bubbles implicitly through the inclusion of a mobility-reduction factor, which is dependent on various parameters, including water/oil saturation, capillary number (accounting for shear effects), surfactant concentration, and salinity. The oil phase is not considered here and we assume that surfactant exists within the aqueous phase across the entire porous medium. Appendix A provides a detailed description of the 'STARS' foam model used in this work. This paper aims to investigate key problems related to gravity segregation in foam-application processes. Firstly, we provide a brief overview of these problems of interest. Then, we verify the accuracy of our simulation framework by comparing the numerical results with analytical solutions for a homogeneous porous medium. We also examine the impact of numerical dispersion on gravity segregation. In addition, we investigate the nature of gravity segregation in layered porous media, including the ultimate segregation length and storage efficiency. Finally, we conclude the paper by discussing and summarizing the key findings.
## 2 Noise Characteristics and Source Identification
The test results of the noise characteristics are different from the conclusions of previous studies [2; 3; 4; 5; 6; 7; 8; 9]. The rotation speed of the centrifugal air compressor for vehicle fuel cells is higher and the number of blades is more. The wavelength at the blade passing frequency is longer, and the attenuation of sound energy in the propagation process is larger. As a result, the noise pressure level at the blade passing frequency is far lower than the noise pressure level at the blade rotation fundamental frequency. The change in rotation speed also has an impact on the noise peak and the overall noise sound pressure level. The noise peak at the rotating fundamental frequency and the low order harmonic frequency gradually increases with the increase in rotation speed, and the broadband noise sound pressure level is higher. \begin{table}
\begin{tabular}{c c c c c} \hline
**Points** & **Positions** & **Idle Condition** & **Common Condition** & **Rated Condition** \\ \hline
1 & Suction pipeline & 79.7 & 82.9 & 89.1 \\ \hline
2 & First stage volute & 80.3 & 84.8 & 91.1 \\ \hline
3 & Near motor & 78.2 & 82.5 & 88.3 \\ \hline
4 & Outlet of first stage volute & 80.0 & 81.4 & 87.3 \\ \hline
5 & Inter-stage pipeline & 82.8 & 87.0 & 92.2 \\ \hline
6 & Elbow at second stage inlet & 77.5 & 82.3 & 89.5 \\ \hline
7 & Second stage volute & 74.9 & 82.1 & 87.1 \\ \hline
8 & Discharge pipeline & 76.9 & 78.7 & 85.8 \\ \hline
9 & 1 m from the compressor in the horizontal direction & 60.6 & 65.1 & 78.8 \\ \hline \end{tabular}
\end{table} 
Table 4: Sound pressure level at measuring points [dBA]. Figure 5: Near-field noise spectrum characteristics under different working conditions.
## 3 Results and Discussion
### Models of Water Content Changes with Random Coefficient  
Using the simulation of the value of the \(n\) coefficient, various models of water content changes were obtained for the same drying parameters.  
Figures 6-8 show exemplary results of the modelling of the water content measurements of drying leek slices using the model (6) with a random \(n\) coefficient. First, two limiting curves were determined, with the coefficients \(n_{\text{min}}\) and \(n_{\text{max}}\) among all selected values of the \(n\) coefficients of the model (6) (marked with a thicker line on the graph red and blue). Next, the coefficient \(n\) was randomized using a pseudo-random number generator and probabilistic models were obtained (black lines on the graph).  
Figure 6: The graphs of models for moisture content changes with random coefficient for leek slices 8 mm thick, during drying at temperature 40 \({}^{\circ}\)C.  
Figure 7: The graphs of models for moisture content changes with random coefficient for leek slices 4 mm thick, during drying at temperature 50 \({}^{\circ}\)C.  
It is worth noting that one plot of the model with a random \(n\) coefficient is a plot of a single measurement or averaged value of multiple repetitions of measurements carried out under the same conditions. Random models cover the area determined by the measurements of changes in the water content, limited by model graphs with determined extreme values of the analysed coefficient.  
The model (6) was used to model the drying kinetics of many vegetables in the first period of drying [39; 59; 62; 63]. The coefficient \(n\), selected in the model, characterized the method for shrinking the dried particles. The value of this coefficient could be determined on the basis of many repetitions of the experiment and, finally, the average value with an acceptable standard deviation was adopted for modelling.  
However, in the case of drying leek slices, the only predictable sample parameters are mass and volume, while the particle surface area of samples prepared from a homogeneous material may be different from the beginning. With the same initial mass, the number and shape of the dried particles may be different (slices as compact cylinders or cylinders of different thickness) and the surface can change in different ways during drying. The determined range of \(n\) values (0.67-7) is so large that the calculation of its mean value, as representative for the tested material using model (6), has no statistical or logical justification.  
Therefore, attempts at mathematical modelling of the drying kinetics of such a material as leek should take into account the randomness of the number and the shape of the dried particles, which, for model (6), means the randomness of the \(n\) coefficient.  
The models used in the literature usually reproduce only one specific course of changes in water content, while the model presented in this study may have a wider application. A model with a random coefficient can indicate different courses of water content changes with the same drying parameters.
## 5 Conclusions  
In this paper, a virtual-force-based guidance law was proposed for path following and obstacle avoidance in UAVs. The guidance law was inspired by a spring mass system. In order to follow a planned path and avoid the newly detected obstacles in the planned path, four virtual forces were designed to guide the UAV. The virtual repulsive force pushed the UAV away from the planned path to move around the obstacles. The virtual centripetal force counteracted the influence of the curvature of the planned curve. Then, the virtual spring force and the virtual drag force guided the UAV to accurately track a curve in the same way as tracking a straight-line.  
The use of artificial physics means the guidance law is founded in solid physical theory and is computationally simple. The proposed guidance law was evaluated by both numerical and HIL simulations. The numerical simulation results show that the VFGL has a large parameter adaptation. It performed over the NLGL, PLOS, and PID in path following and performed over GVF and VFF in obstacle avoidance during path following.  
In the HIL simulation, the VFGL was applied to the problem of path following and obstacle avoidance for a fixed-wing UAV. The results show that the VFGL was easy to tune in application. The UAV accurately followed the predefined \"8\" shape path and successfully avoid the obstacle in the path. Although the VFGL was evaluated using a fixed-wing UAV in the HIL simulation, we considered that this is also applicable for other unmanned vehicles that can be modeled as unicycles.  
Figure 14: The HIL simulation result of path following with obstacle avoidance: (**a**) the screenshot of the X-Plane; (**b**) the trajectory of the UAV.  
**Author Contributions:** Methodology: X.W. and L.C.; software: L.K. and B.W.; resources: L.C.; validation: X.W., S.H. and C.L.; writing--original draft preparation: X.W.; writing--review and editing: X.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Simulation data are available from the corresponding author, [PERSON], upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# RF Exposure Assessment by Drone-Based Technology
## 4 Conclusions  
From the results obtained, we can conclude that the electric-field levels recorded in the four mobile-phone bands evaluated are considerably lower than the reference limits established by the ICNIRP 2020 standard. Although the values detected at a height of 20 m are between 2.9 and 4.8 times higher than those recorded at ground level, they remain within safe margins for the general population. The high dispersion of the data, with positive asymmetry, suggests that the exposure levels are not uniform, but are influenced by the proximity to the antennas, the direct line of sight, and the configuration of the environment. The use of geo-statistical techniques such as kriging, together with the logarithmic transformation of the data, has made it possible to generate precise heat maps that illustrate the spatial variations of the electromagnetic field, which facilitates the identification of areas of greater exposure. Finally, the ability to perform sampling at different heights will allow for the experimental validation of propagation models, the safe monitoring of antenna emissions in their proximity, and, taking into account research on the attenuation of electromagnetic waves by buildings walls, the planning of indoor radio  
Figure 8: Heat maps of electric-field intensities at 20 m height in the mobile-phone bands: (**a**) LTE 800 DL, (**b**) GSM + UMTS 900 DL, (**c**) GSM 1800 DL and (**d**) UMTS 2100 DL. The figures were obtained by merging the heat maps with aerial images of the area using Google Earth. The location of the site with sectorial mobile-phone antennas is shown in red along with the orientation of its sectors.  
coverage and the evaluation of the exposure of the resident population to electromagnetic fields emitted by radio base stations.  
J.M.P.-S.: Conceptualization, Writing--Review and Editing, Supervision, Project Administration, Funding Acquisition. C.M.-C.: Methodology, Software, Investigation, Data Curation, Writing--Original Draft. F.J.G.-C.: Software, Formal Analysis, Investigation, Visualization. M.R.-P.: Resources, Writing--Review and Editing, Funding Acquisition. A.G.-G.: Validation, Investigation, Resources, Supervision. A.J.-B.: Resources, Writing--Review and Editing, Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Extremadura Board and the European Regional Development Fund through the 5 th Regional Technology Development and Innovation Research Plan (2014-2017), grant number GR15070.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We thank the Extremadura Board and the European Regional Development Fund for the financial support provided through the 5 th Regional Technology Development and Innovation Research Plan (2014-2017).  
The authors declare no conflicts of interest.