esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Nomenclature of Parameters in Theoretical Modeling  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Definition** & **Unit** & **Parameter** & **Definition** & **Unit** \\ \hline \(R\) & Inner radius of pipe & m & \(l\) & Length of pipe & m \\ \(R_{0}\) & Outer radius of pipe & m & \(\theta\) & \multicolumn{2}{c}{Deflection angle of} & rad \\ \(D\) & Pipe diameter & m & \(\rho\) & \multicolumn{2}{c}{Density} & kg/m\({}^{3}\) \\ \(A\) & Cross-sectional area & m\({}^{2}\) & \(r_{p}\) & \multicolumn{2}{c}{Contrifugal radius of} & m \\ \(M\) & Moment & Nm & \(\beta\) & \multicolumn{2}{c}{Angle between pipe} & rad \\ \(G\) & Shear modulus & Pa & \(\dot{\theta}\) & \multicolumn{2}{c}{Angular velocity of} & rad/s \\ \(V\) & Fluid velocity & m/s & \(\dot{\omega}\) & \multicolumn{2}{c}{Pipe velocity} & m/s \\ \(P\) & Fluid pressure & MPa & \(\dot{\psi}\) & \multicolumn{2}{c}{Bending angle of pipe} & rad \\ \(K\) & Fluid bulk modulus & MPa & \(f\) & \multicolumn{2}{c}{Forces in cross-section} & N \\ \(K^{\prime}\) & Corrected fluid bulk & \multicolumn{2}{c}{Shear stress of pipe} & Pa \\ \(I\) & \multicolumn{2}{c}{Products of inertia} & m\({}^{4}\) & \(\sigma\) & \multicolumn{2}{c}{Stress} & N/m\({}^{2}\) \\ \(J\) & \multicolumn{2}{c}{Polar moment of inertia} & m\({}^{4}\) & \(k\) & \multicolumn{2}{c}{Shear coefficient} & - \\ \(T\) & \multicolumn{2}{c}{External moment of constraints} & m\({}^{4}\) & \(v\) & \multicolumn{2}{c}{Poisson’s ratio} & - \\ \(e\) & \multicolumn{2}{c}{Thickness of pipe} & m & \(\varepsilon\) & \multicolumn{2}{c}{Strain} & - \\ \(m\) & Mass & g & \(x\),\(y\),\(z\) & \multicolumn{2}{c}{Directional subscripts} & - \\ \(E\) & Modulus of elasticity & MPa & \(f\),\(p\) & \multicolumn{2}{c}{Structural subscripts} & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nomenclature of parameters in theoretical modeling.
## 1 Introduction
Few studies have been conducted so far on the aeroacoustic properties of interacting propellers and are mainly related to counter-rotating propellers [16; 17] and drones [18; 19; 20; 21; 22], where the distance between rotor hubs is a crucial aspect. In Reference [23], numerical aeroacoustic analyses of innovative propeller geometries have been carried out, accounting for a single propeller mounted on a wing. In Reference [24] a parametric study for a single open rotor aimed at reducing noise has been reported whereas in Reference [25], a new method has been presented to estimate the influence of the number of blades on noise contribution. A further analysis on the installation effect for two consecutive propellers has been presented in Reference [26]. Despite its lack in the literature, the aerodynamic and aeroacoustic characterisation of an unducted array of propellers is a primary aspect for designing an optimal DEP configuration so as to achieve the targets discussed in Reference [3]. Here, the aircraft layout has been optimised and analysed within the in-house Multidisciplinary Conceptual Robust Design Optimisation (MCRDO) framework FRIDA (Framework for Integrated Design of Aircraft) [8; 27; 28]--the optimised configuration has been tested over the design mission and the flight mechanics variables successfully derived. The initial propeller geometry has been designed using a lifting-line theory method [29; 30]. The main design variables that should be considered to maximise the single propeller efficiency are angular velocity of rotor, radius and chord of blades, and the number of blades per propeller. When it comes to DP sizing, the design of each fan is also constrained by the number of devices chosen for the configuration which, in turn, is a consequence of the optimal distance between propellers in the array. All these factors determine the propeller loading, that is an important aspect for its efficiency. Nowadays, it is of high interest for the optimisation of DP to start analysing how the performances of the entire array are influenced by the distance between propeller and the array installation. After the rotor has been designed, based on the geometrical constraints of encumbrance, the blade pitch angle is verified and corrected, when necessary, by means of an in-house BEM (_Boundary-Element-Method_) aerodynamic solver, to match the required thrust. Then, the aeroacoustic response of the selected case studies is evaluated through the Farassat 1A boundary integral formulation [31]. The paper is organised as follows. A description of the unconventional Hybrid-Electric-Propelled (HEP) aircraft design process is presented in Section 2. Section 3 presents a synthesis of the propeller noise generation. The aerodynamic solver is presented in Section 4, as well as the formulation employed for the aeroacoustic analyses. In Section 5, the numerical results for one isolated propeller and those for the mutual interaction of two or three adjacent rotors are reported. Finally, Section 6 gathers the conclusions.
# Granular Material Development Applied in an Experimental Section for Civil Engineering Purposes
## 3 Results  
### Chemical Composition: XRF and XRD  
The chemical composition results in terms of the most stable oxides determined by XRF for WBA, APC, crushed M and GM are presented in Table 2. The major compounds for WBA sample are SiO\({}_{2}\) and CaO, due to the packaging glass and ceramics content of MSW [38]. The Fe\({}_{2}\)O\({}_{3}\) and Al\({}_{2}\)O\({}_{3}\) contents are likewise noteworthy, wherein the former is related to the existence of unrecovered ferrous metals while the latter is the consequence of non-ferrous metals and ceramics such as clays. Nevertheless, as expected, the SiO\({}_{2}\) content is low in APC residues, which are mainly composed of lime (CaO), the main compound used as an alkaline neutralizer during flue gas scrubbing from combustion. The Cl amount is significant in APC because of the polyvinyl chloride (PVC) combustion, and its leaching causes concern in terms of the APC residues' landfill treatment [39]. Besides, the LOI value is significant due to the use of Ca(OH)\({}_{2}\) for gas neutralization in the APC system, whereby part of the excess of Ca(OH)\({}_{2}\) leads to a formation of CaCO\({}_{3}\). Meanwhile, the SO\({}_{3}\) amount is related to the presence of CaSO\({}_{4}\). After the stabilization of APC with OPC to obtain the crushed M, its chemical composition is close to that of APC residues, with the supplementary contribution of OPC affecting SiO\({}_{2}\) content. Similarly, the GM composition was comparable to that of WBA, with the additional amount of CaO from APC residues.  
Considering the mineral phases, the XRD patterns obtained for WBA and GM show as main components the amorphous SiO\({}_{2}\) and CaCO\({}_{3}\). For the crushed M sample, the predominant phases are halite (NaCl) and sylvite (KCl) from APC, due to the fact that the hydrated phases of OPC are less crystalline than the determined ones. If more information about the present mineral phases is required, the following references [1,40] should be reviewed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{4}{c}{**wt. \%**} \\ \cline{2-5}  & **WBA** & **APC** & **Crushed M** & **GM** \\ \hline SiO\({}_{2}\) & 43.30 & 6.64 & 21.70 & 39.15 \\ CaO & 16.90 & 48.35 & 45.30 & 26.59 \\ Cl & 0.14 & 8.86 & 9.08 & 0.89 \\ Fe\({}_{2}\)O\({}_{3}\) & 14.10 & 0.79 & 1.65 & 16.89 \\ Na\({}_{2}\)O & 7.58 & 4.28 & 4.44 & 3.71 \\ Al\({}_{2}\)O\({}_{3}\) & 5.80 & 4.02 & 3.82 & 4.87 \\ MgO & 2.22 & 1.73 & 1.87 & 2.02 \\ K\({}_{2}\)O & 1.11 & 4.33 & 2.10 & 1.67 \\ CuO & 0.23 & - & - & 0.13 \\ SO\({}_{3}\) & 0.65 & 6.24 & 4.53 & 1.53 \\ TiO\({}_{2}\) & 0.35 & 0.86 & 0.68 & 0.30 \\ P\({}_{2}\)O\({}_{5}\) & 2.97 & 1.36 & 0.51 & 1.10 \\ ZnO & 0.18 & 0.72 & 0.32 & 0.19 \\ PbO & - & 0.12 & 0.13 & 0.03 \\ LOI & 4.47 & 11.7 & 3.87 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition determined by means XRF for the WBA, APC, crushed M and the GM formulated.
## 3 MMPP/D/1 Simulation for Rare Event
### Monte Carlo Simulation for Queue  
Examine the MMPP (Markov-Modulated Poisson Process) as the input traffic, which consists of two states, each representing a Poisson process. Analyze the MMPP/D/1 queue system. Define \(Q_{t}\) as the steady-state queue length and \(S_{t}\) as the state of a Markov-Modulated Poisson Process (MMPP) at the \(t\)-th time slot. A time slot refers to the specific duration of time allocated for the processing of a packet. The Monte Carlo estimation for events \(\{Q>q\}\) is performed by  
\[P(Q>q)=\dfrac{1}{N}\sum_{t=1}^{N}\delta_{q}(Q_{t}) \tag{1}\]  
where \(N\) is the number of time slots and \(\delta_{q}(Q_{t})\) is the indicator function given by  
\[\delta_{q}(Q_{t})=\begin{cases}1,&\text{if}\quad Q_{t}>q,\\ 0,&\text{if}\quad Q_{t}\leq q\end{cases} \tag{2}\]  
If the event \(\{Q>q\}\) in Equation (1) is considered to be a rare occurrence, it is necessary to have a significantly large number of samples \(N\). Therefore, to estimate the probability of a rare event, MC simulation takes much time to obtain the target value [8]. This is the limitation of MC simulation for estimating the probability of rare events.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronyms** & **Meaning** \\ \hline ATM & Asynchronous Transfer Mode \\ MC & Monte Carlo \\ IS & Importance Sampling \\ MMPP & Markov-Modulated Poisson Process \\ ISDC & Importance Sampling Queue Counter \\ RC & Regenerative Cycle \\ \hline \hline \end{tabular}
\end{table}
Table 1: The acronyms and meaning.
# Methodology for Efficient Bitumen Storage with Reduced Energy Consumption
## 2 Materials and Methods
### Bitumen Thermal Storage Analysis
#### 2.1.2 Thermal Loss Calculation  
The heat losses from the bitumen storage tank (Figure 2) to the ambient were evaluated based on heat transfer calculations. In this case, the heat transfer rate was strongly related to the isolation quality, tank configuration, ambient temperature, wind speed, and the temperature difference between the tank and air or ground [16]. Therefore, the tank surface area was divided in four parts as shown in Figure 3: the dry sidewall, the wet sidewall, the tank bottom and the tank roof.  
Figure 1: Scheme of the work methodology.  
For dry sidewall  
\[q_{d}=U_{d}A_{d}(T_{v}-T_{a}) \tag{1}\]  
For wet sidewall  
\[q_{w}=U_{w}A_{w}(T_{L}-T_{a}) \tag{2}\]  
For tank-bottom  
\[q_{b}=U_{b}A_{b}(T_{L}-T_{G}) \tag{3}\]  
For tank roof  
\[q_{r}=U_{r}A_{r}(T_{v}-T_{a}) \tag{4}\]  
Finally, the total heat loss is given by:  
\[\dot{Q}_{load}=q_{d}+q_{w}+q_{b}+q_{r} \tag{5}\]  
where U, A, and T refer to the overall heat transfer coefficient, the transfer area, and the temperature, respectively. The subscripts d, w, b, r, v, a, and L refer to dry, wet, bottom, roof, vapor, ambient, and liquid, respectively. The overall heat-transfer coefficient for the dry sidewall of the tank (U) was calculated as the sum of the resistances of vapor film,  
Figure 3: Typical vertical–cylindrical tank.  
Figure 2: Bitumen reservoir studied in the factory.  
fouling, metal wall, insulation (if any), and outside air (convection plus radiation). The overall heat transfer coefficient for each surface was calculated by the following equation (Equation (6)):  
\[\frac{1}{U}=\frac{1}{h_{i}}+\frac{e_{M}}{k_{M}}+\frac{e_{I}}{k_{I}}+\frac{1}{h_{ e}} \tag{6}\]  
where h, e, and k refer to individual film heat transfer coefficients, thickness, and conductivity, respectively. The subscript i, e, M, and I refer to internal, external, metal, and insulation, respectively.
# Research on Large Divergence Angle Laser Ranging System +
## 4 Experimental Results and Discussion  
### Laser Beam Divergence Angle Test  
The divergence angle of the collimated laser is tested using the Aperture Method. During the experiment, the test pinhole plate is placed at the focal length of the reflector. The divergence angle of the rangefinder can be calculated using the following formula:  
\[\theta=\lim_{S\rightarrow\infty}\frac{d_{s}}{S}\times 10^{3} \tag{5}\]  
In the formula, \(\theta\): divergence angle of the multimode beam;  
_ds_: The diameter of the multimode spot at a distance \(S\) from the beam waist, in mm. During actual measurement, it is the diameter of the pinhole aperture placed on the focal plane of the reflector. In this test system, it is 52 mm.  
_S_: The distance from the beam waist to the measured spot, in mm. In actual measurement, it is the focal length \(f\) of the reflector, in mm. For this test system, it is 2000 mm.  
During the experiment, the laser energy needs to be tested both in front of and behind the pinhole. Let the laser energy in front of the pinhole be \(E_{1}\), and the laser energy behind the pinhole be \(E_{2}\). If the ratio of the energy behind the pinhole to that in front of the pinhole, \(E_{2}/E_{1}\) is less than 86.5%, then it can be considered that the pinhole aperture meets the testing requirements, and the measured laser divergence angle is greater than 26 mrad. The laser beam divergence angle test diagram is shown in Figure 12. The pinhole plate in the test is shown in Figure 13. The laser energy before and after the pinhole is shown in Figure 14.  
Based on the test results, the energy in front of the pinhol-e \(E_{1}\) is 391 \(\mu\)J, and the energy behind the pinhole \(E_{2}\) is 331 \(\mu\)J. \(\frac{E_{2}}{E_{1}}=84.65\%\), which is close to the ISO11146-1:2021 standard [11] requirement of 86.5%. Therefore, the calculated laser divergence angle is greater than 26 mrad, which meets the design specifications.  
Figure 11: Spot diagram at +60 \({}^{\circ}\)C.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## Nomenclature  
\(A\) [m\({}^{2}\)] Cross-sectional area of the tank;  
\(Q_{i}\) [m\({}^{3}\)/s] Pump flow rate into the tank;  
\(Q_{o}\) [m\({}^{3}\)/s] The flow rate of liquid out of the tank;  
\(H\) [m] Height of liquid in the tank;  
\(a\) [m\({}^{2}\)] Cross-sectional area of the tank output pipe;  
\(g\) [m\({}^{2}\)/s\({}^{2}\)] Ground acceleration;  
\(Q_{11}\), \(Q_{22}\) [m\({}^{3}\)/s] The pump flow rates into tank 1 and tank 2;  
\(Q_{01}\), \(Q_{02}\) [m\({}^{3}\)/s] The flow rates of liquid out of tank 1 and tank 2;  
\(Q_{03}\) [m\({}^{3}\)/s] The flow rate between tanks;  
\(a_{1}\), \(a_{2}\) [m\({}^{2}\)] The cross-sectional areas of tank 1 and tank 2 output pipes;  
\(a_{3}\) [m\({}^{2}\)] The cross-sectional area of the pipe between tanks;  
\(A_{1}\), \(A_{2}\) [m\({}^{2}\)] The cross-sectional areas of tank 1 and tank 2;  
\(H_{1}\), \(H_{2}\) [m] Heights of liquid in tank 1 and tank 2;  
\(H_{1s}\), \(H_{2s}\) [m] Stationary values of the height of liquid in tank 1 and tank 2;  
\(Q_{1s}\), \(Q_{22s}\) [m\({}^{3}\)/s] Stationary values of the pump flow rates into tank 1 and tank 2;  
\(h_{1}\), \(h_{2}\) [m] Height deviations from the stationary values;  
\(q_{i1}\), \(q_{i2}\) [m\({}^{3}\)/s] Pump flow rates into tank deviations from the stationary values;  
\(1/\lambda\) [s] Desired time constant of the system with the Dahlin controller;  
\(T\) [s] Sampling time;  
\(T_{d}\) [s] Desired transport delay of the ST system with Dahlin controller;  
\(K\) Single tank system gain factor;  
\(T_{1}\) [s] Single tank system time constant;  
\(\tau\) [s] Transport delay of the single tank system;  
\(T_{1}\), \(T_{2}\) [s] Time constants of the coupled tank system;  
\(K_{pkv}\) The critical gain;  
\(T_{kr}\) [s] The critical oscillation period;  
\(M_{c}\) The maximum sensitivity;  
\(G_{p}\)(s), \(G_{c}\)(s) The plant and controller transfer functions.
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 1 Introduction
### Agent-Based Modelling  
ABM is a computational modelling approach that simulates the interactions and behaviours of autonomous agents within a given environment [13]. In ABM, an _\"agent\"_ represents an individual entity, such as a person, an animal, or an organisation, capable of making independent decisions, integrating new information and responding to its environment based on a set of predefined behaviours and rules. These agents operate within a simulated environment where their interactions and decision-making processes unfold dynamically over time. One of the strengths of ABM lies in its ability to capture complex, emergent phenomena that arise from the interactions of individual agents, providing a bottom-up perspective on system dynamics [14]. This modelling technique finds applications in various fields, including social sciences, economics, ecology, and epidemiology, allowing researchers to explore and understand the intricate behaviours and patterns that emerge from the interactions of diverse agents. Beyond this, and feeding into higher levels, there are various examples of ABM contributing to policy development while originating at the grassroot level. [PERSON] and [PERSON] [15] utilised ABM to model climate change impacts on agriculture and subsequently draw conclusions for climate-related policy options to spur climate adaptation and mitigation.
# Examining Pipe-Borehole Wall Contact and Pullback Loads for Horizontal Directional Drilling
## 5 Conclusions  
A method for examining pipe-borehole wall contact was presented in this paper. The contact pressure distribution and external load-displacement relationship, considering the effect of drilling fluid pressure, were derived. Compared to the [PERSON] and Persson models, the [PERSON] model has the highest accuracy in calculating the maximum contact pressure when the drilling fluid pressure is not considered in HDD projects. The maximum contact pressure considering the effect of drilling fluid pressure was obtained using FEM and nonlinear regression methods. The calculation results of maximum contact pressure considering the drilling fluid pressure were very close to the FEM results, with average absolute and relative errors of 0.001 MPa and 0.6%, respectively, under the 51 test conditions.  
Figure 12: Effect of rheological characteristics of drilling fluid on fluid drag force.  
For HDD projects, the value range of the pressure distribution exponent \(n\) is generally between 0.6 and 0.75.  
Additionally, based on the analysis of pipe-borehole wall contact, a novel pullback loads model was proposed. The model includes the main mechanisms that contribute to the pullback loads. The adhesion theory was used to calculate the pipe-borehole wall friction force, avoiding the influence of empirical factors on the results. The effects of the annulus size, flow rate, drilling fluid parameters, and eccentricity were considered when calculating the fluid drag force. Through case studies, the applicability and potential of the model in HDD projects were verified, and the effect of drilling fluid rheological characteristics on drilling fluid drag force was analyzed.  
Notably, the variability of the construction, material, and borepath parameters will always make precise theoretical predictions of the field results challenging. A reasonable model can show the expected range of magnitudes of pipe pullback loads, variation trends of pullback loads, and influences of specific parameters on pullback loads. Thus, the proposed model exhibits good application results and prospects for HDD projects. Analysis from the contact perspective can also provide new ideas for studying pipe pullback loads.  
Conceptualization, Z.W. and C.H.; funding acquisition, C.H.; investigation, Z.W.; methodology, Z.W.; resources, C.H.; software, Z.W.; supervision, C.H.; validation, C.H.; writing--original draft, Z.W.; writing--review and editing, Z.W. and C.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Shaanxi Province [grant number 2021 SF-523], and the Natural Science Basic Research Program of Shaanxi [grant number 2022]Q-375].  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Gas Emission Analysis  
Gas emission analysis has been used before assessing oxidation processes related to self-heating and self-ignition of biomasses [42; 43]. A fast and direct technique to detect the beginning of the self-combustion process consists of analyzing the air surrounding the stored or transported material in order to detect the presence of certain gases, such as CO and CO\({}_{2}\), which are characteristic products of combustion reactions. Using a new methodology, it is possible to determine early oxidation processes [44].  
Gas emission analysis has been carried out according to the method suggested by [PERSON] et al. [44]. The sample, inside a cubic cell of 100 mm side, was set inside a sealed container with a K-type thermocouple that allowed the reading of the sample temperature. The container was set inside an isothermal oven preheated at 20 \({}^{\circ}\)C. In addition, the container had a plastic tube connected to a suction pump allowing the filling of tedlar bags with the emitted gas from the sample. The temperature was increased up to 200 \({}^{\circ}\)C, and gases were extracted every 20 \({}^{\circ}\)C. After full heating was carried out, the bags containing the gas samples were analyzed by means of a Rosemount Analytical NGA-2000 analyzer that determines CO and CO\({}_{2}\) concentrations. Measurements were done at regular temperature intervals in steps of 20 K.  
The evaluation of the initial temperatures for both carbon monoxide and dioxide emissions, was carried out, calculating the relative differences for each temperature interval as (2):  
\[RD=\frac{e_{Ti+1}-e_{Ti}}{e_{Ti}} \tag{2}\]  
where \(e_{Ti}\) is the gas emission at the lowest temperature of the interval, and \(e_{Ti+1}\) is the gas emission at the highest temperature of the interval. Using the relative differences, relative percentages were calculated as (3):  
\[RP=\frac{RD}{\sum RD_{i}}\cdot 100 \tag{3}\]  
The temperature interval that corresponded to the first relative percentage greater than 10%, was the initial temperature interval of gas emission.