esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Optimal Measurement Speed in the Evaluation of Transmission Precision
### Optimal Measurement Speed  
Transmission error is the main basis for evaluating the transmission precision of a precision reducer. The key to its measurement is to avoid or minimize the influence of external factors as much as possible. At present, the transmission error measurement is mainly realized under the conditions of zero-load and low speed. The purpose of zero-load setting is to avoid the influence of elastic deformation that is caused by load torque and the purpose of low speed is to reduce the influence of friction torque of a precision reducer. The concept of optimal measurement speed is put forward in this paper from the perspective of reducing the influence of friction torque of precision reducers.  
The friction torque of precision reducers decreases with the increase in the speed when the speed is low. The friction torque increases with the increase in the speed when the speed is greater than a certain value, showing a typical Stribeck effect (Figure 2) [10; 11; 12; 13]. It can be seen that the forward and reverse Stribeck curves of precision reducers have a wave trough point \(A^{+}\) and a wave peak point \(A^{-}\), respectively. Taking the forward curve as an example, when the speed \(\omega\leq\omega_{A}^{+}\), the friction torque of precision reducers decreases with the increase in speed. When the speed \(\omega\geq\omega_{A}^{+}\), the friction torque of precision reducers increases with the increase in the speed. When the speed \(\omega=\omega_{A}^{+}\), the precision reducer is subjected to the smallest friction torque. Therefore, the friction torque is the smallest when the speed \(\omega=\omega_{A}^{+}\), and this speed is the optimal speed for the forward transmission error measurement. Similarly, \(\omega=\omega_{A}^{-}\) is the optimal speed for the reverse transmission error measurement.  
Therefore, the influences of external factors can be effectively reduced under the conditions of zero-load and optimal measurement speed and the objective evaluation of transmission precision can be realized in the dynamic measurement of the transmission error of a precision reducer. The transmission error can be expressed as:  
\[TE=\left\{\begin{array}{ll}F(\omega_{A}^{+},T_{0})&\omega>0\\ F(\omega_{A}^{-},T_{0})&\omega<0\end{array}\right. \tag{2}\]  
where \(F(\omega_{A}^{+},T_{0})\) is the forward transmission error curve under the conditions of zero-load and optimal measurement speed \(\omega_{A}^{+}\) and \(F(\omega_{A}^{-},T_{0})\) is the reverse transmission error curve under the conditions of zero-load and optimal measurement speed \(\omega_{A}^{-}\).  
Figure 1: Transmission error curve.
## 3 Validation of the Numerical Model
### Comparative Analysis of Single-Phase and Mixture Models
For the sake of simplicity when developing new numerical models, it may be interesting to consider the mixture model with constant properties if satisfactory results may be obtained. Figure 6 displays the axial distributions of the local heat transfer coefficient for \(Re=1600\), \(d_{np}=42\) nm and \(Al_{2}O_{3}\)-water based nanofluids with four concentrations of nanoparticles. The results are obtained using Equations (7) and (10) either with constant (CP) or variable (VP) properties for the base fluid. In all cases, the local heat transfer coefficient \(h\) decreases exponentially with the axial distance \(z\). Using temperature-dependent properties (VP) leads to very satisfactory results as already shown in Figure 3a. On the contrary, using constant properties (CP) leads to a strong overestimation of the heat transfer coefficient, with more pronounced differences towards the thermally fully developed flow region. In fact, the thermal conductivity of the nanofluid increases drastically with decreasing values of both temperature and density. Using VP, the circumferential wall temperature appears to be non-uniformly distributed in the tangential direction, whereas the CP model exhibits a more uniform and axisymmetric behavior. The VP model takes then into account buoyancy effects, which result in a noticeable increase of the fluid temperature in the upper half of the pipe. This confirms previous results such as those suggested by [17] except from the previous work of [PERSON] et al. [16], who showed that CP lead to an underestimation of the heat transfer coefficient. This difference may be attributed to the different multiphase models used. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\boldsymbol{\varphi}\) & **Experiments [11]** & **Simulation 1** & **Simulation 2** \\ \hline \multirow{2}{*}{0.006} & \multirow{2}{*}{1313.54} & 1089.66 & 1086.37 \\ \cline{3-3}  & & (17.04\%) & (17.29\%) \\ \hline \multirow{2}{*}{0.016} & \multirow{2}{*}{1626.12} & 1085.69 & 1075.72 \\ \cline{3-3}  & & (33.23\%) & (33.85\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Influence of the different correlations on the average heat transfer coefficient \(h_{avg}\) (W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\)) for \(Re=1600\), \(d_{np}=42\) nm and \(Al_{2}O_{3}\)-water based nanofluids. The relative error is given in brackets. Simulation 1 combines Equations (7) and (10), whereas simulation 2 uses Equations (8) and (9). Results obtained using the single-phase model. Figure 6: Local heat transfer coefficient obtained for \(Re=1600\), \(d_{np}=42\) nm and \(Al_{2}O_{3}\)-water based nanofluids: Influence of temperature-dependent properties on the performances of the mixture model. It is important to note that taking into account temperature-dependent properties does not increase the computational cost. Using the same mesh grid, both calculations take about 4 h using 8 processors on Mammouth Parallel 2. Calculations using VP lead to a rather faster convergence as compared to the CP case.
## 2 Results  
### RSV Differently Affects Endothelial and Epithelial Cells Proliferation and Morphology  
As shown in Figure 1A-F, RSV applied for 24 h at low concentrations (1, 5, and 10 uM) did not affect either HECa10 cell proliferation, as measured by neutral red or sulforhodamine B assays, or cell morphology. Higher concentrations of RSV (50 and 100 uM) caused a significant decrease in HECa10 cell number to about 20% (50 uM, \(p<0.01\)) and to 40% (100 uM, \(p<0.001\)) when compared to the control cells. HECa10 cells treated with RSV at 50 uM did not induce any morphological changes (Figure 1G). RSV addition only at 100 uM concentration changed the cell shape and induced apoptotic phenotype (Figure 1H). The incubations of cells with RSV in doses mentioned above and extended to 48 h caused a much deeper decrease in cell proliferation of HECa10.  
Figure 1: Effect of different RSV concentrations on HECa10 cell proliferation as analysed by (**A**) NR assay and (**B**) SRB assay. Cell were incubated for 24 h with RSV (1, 5, 10, 50, and 100 uM). Control cells were cultured with 0.1% DMSO and without RSV. (**C–H**) Light microscopy images of control and RSV-treated cells (Delta Optical, magnification 100\(\times\)). The results are presented as mean \(\pm\) SD at least of three experiments with \({}^{**}p<0.01\), \({}^{**}p<0.001\) compared to control.  
The treatment of ARPE-19 cells with low concentrations (1, 5, and 10 \(\upmu\)M) of RSV did not reduce either proliferation or cell morphology (Figure 2A-F). Interestingly, the NR assay, but not the SRB assay, showed a significant increase in the cell number after treatment with 5, 10, and 50 \(\upmu\)M of RSV (\(p\) < 0.001). The disparity between the results obtained with these two assays, and the fact that the NR assay measures the red dye uptake by lysosomes, suggests that RSV at 5, 10, and 50 \(\upmu\)M concentrations did not induce an increase in the cell number but either increased the lysosome number or provoked lysosomal swelling, or increased the uptake of the dye by other means. Higher concentrations of RSV (50 and 100 \(\upmu\)M) had no effect on ARPE-19 cells morphology (Figure 2G,H).
## 3 Case Study: Apartent Building MedZEB Renovation
### Renoration Measures
#### 3.2.3. Ventilation and Infiltration Solutions  
Two types of ventilation systems were considered for this renovation project. The first one was a mechanical system that provided 0.5 ACH using an exhaust fan and self-regulated vents located in the facade. The second option, also mechanically driven, consisted of a heat recovery unit and inlet and exhaust fans. The equivalent air change rate of this system was 0.3 ACH. The term equivalent means that this 0.3 ACH was the air flow which entered the building at the outdoor temperature. Table 3 summarizes the renovation measures corresponding to the ventilation system, their level of air changes per hour and cost.  
In addition, a night ventilation system was analyzed and included in the evaluation of the renovation measures to be implemented in the building. It consisted of a mechanical exhaust fan used on summer nights that mixed colder outdoor air with indoor air to regulate the temperature and comfort of each apartment. The air flow allowed the air in each apartment to be renewed 10 times per hour, and the cost of this system was 150 \(\upepsilon\) per apartment.  
Regarding the air infiltration, improving the window contours using expansive foam and indoor and outdoor sealing tape was considered to increase the airtightness. Using this renovation measure the airtightness could be quantified with the n50 value. Table 4 shows the proposed renovation measures and their corresponding infiltration rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Thermal Bridges**} & \multirow{2}{*}{**System \& Location**} & **Linear Thermal** \\  & & **Transmittance \%** & **W/mK** \\ \hline \multirow{2}{*}{1/} & \multirow{2}{*}{Existing building envelope composition (description on Section 3.1 of this paper)} & \multirow{2}{*}{**Cost \&/m**} \\  & & & this paper) \\ \hline \multirow{2}{*}{2/} & \multirow{2}{*}{Facade to slabs joints} & \multirow{2}{*}{0.840} & \multirow{2}{*}{6} \\  & & & \\ \cline{1-1} \cline{5-5} \multirow{2}{*}{3/} & \multirow{2}{*}{Facade to slabs joints} & \multirow{2}{*}{0.751} & \multirow{2}{*}{7} \\  & & + openings contour & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proposed renovation measures for thermal bridges.  
Figure 6: Improvement of thermal bridges in facade 1. (**Left**): construction detail. (**Right**): calculated heat flux.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Ventilation**} & \multicolumn{1}{c}{**System**} & **ACH** & **Cost (\(\upepsilon\)/dwelling)** \\ \hline \multirow{2}{*}{1/} & \multicolumn{3}{c}{Existing situation—Natural and thermal wind driven depending on window opening} \\ \cline{3-5}  & & Mechanical exhaust & \\ \cline{1-1} \cline{5-5} \multirow{2}{*}{2/} & fan + self-regulated & 0.5 & 564 \\  & vents & & \\ \hline \multirow{2}{*}{3/} & Exhaust and inlet fans & \multirow{2}{*}{0.3} & \multirow{2}{*}{1364} \\  & & + heat recovery unit & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proposed renovation measures for the ventilation system.
## 5 Experimental Results
### Performance of MILP and CP Models
#### 5.1.2 Experimental Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{3}{c}{**Instance Characteristics**} & \multicolumn{3}{c}{\(MILP_{RC}\)} & \multicolumn{2}{c}{\(CP_{RC}\)} \\ \hline
**Instance** & **N** & **O** & **U** & \(T_{MILP}\) & \(CPU_{MILP}\) & \(T_{CP}\) & \(CPU_{CP}\) \\ \hline RCWOS7 & 10 & 17 & 3 & **0** & 68 & **0** & 950 \\ RCWOS8 & 10 & 29 & 3 & **0** & 140 & **0** & 950 \\ RCWOS9 & 10 & 43 & 3 & **180** & 240 & **180** & 950 \\ RCWOS10 & 10 & 49 & 4 & **1330** & 320 & **1330** & 950 \\ RCWOS11 & 13 & 18 & 4 & **685** & 40 & **685** & 950 \\ RCWOS12 & 13 & 34 & 4 & **1258** & 380 & **1258** & 950 \\ RCWOS13 & 13 & 49 & 4 & **2780** & 450 & **2780** & 1100 \\ RCWOS14 & 15 & 20 & 4 & **4200** & 490 & **4200** & 1100 \\ RCWOS15 & 15 & 45 & 5 & **7200** & 820 & **7200** & 1100 \\ RCWOS16 & 15 & 53 & 5 & 8400 & 1080 & **8320** & 1100 \\ RCWOS17 & 15 & 59 & 5 & 9600 & 1202 & **8592** & 1300 \\ RCWOS18 & 20 & 29 & 5 & 10,800 & 1440 & **9987** & 1300 \\ RCWOS19 & 20 & 55 & 6 & – & \textgreater{}1800 & **12,600** & 1300 \\ RCWOS20 & 20 & 64 & 7 & – & \textgreater{}1800 & **18,600** & 1300 \\ Average & 12 & 33 & 4 & 2609 & 326 & 3783 & 888 \\ \hline \hline \end{tabular} Optimal values in bold.  
\end{table} 
Table 7: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{3}{c}{**Instance Characteristics**} & \multicolumn{3}{c}{\(MILP_{RC}\)} & \multicolumn{2}{c}{\(CP_{RC}\)} \\ \hline
**Instance** & **N** & **O** & **U** & \(T_{MILP}\) & \(CPU_{MILP}\) & \(T_{CP}\) & \(CPU_{CP}\) \\ \hline LRCWOS1 & 30 & 66 & 1 & – & \textgreater{}1800 & **0** & 1300 \\ LRCWOS2 & 30 & 80 & 1 & – & \textgreater{}1800 & **6760** & 1300 \\ LRCWOS3 & 40 & 88 & 2 & – & \textgreater{}1800 & **9900** & 1300 \\ LRCWOS4 & 40 & 96 & 2 & – & \textgreater{}1800 & **9998** & 1300 \\ LRCWOS5 & 50 & 110 & 2 & – & \textgreater{}1800 & 13,100 & 1300 \\ LRCWOS6 & 50 & 127 & 3 & – & \textgreater{}1800 & 19,760 & 1300 \\ LRCWOS7 & 65 & 143 & 3 & – & \textgreater{}1800 & 20,100 & 1487 \\ LRCWOS8 & 65 & 150 & 3 & – & \textgreater{}1800 & 21,900 & 1487 \\ LRCWOS9 & 65 & 165 & 3 & – & \textgreater{}1800 & 23,254 & 1487 \\ LRCWOS10 & 65 & 185 & 4 & – & \textgreater{}1800 & 23,978 & 1487 \\ LRCWOS11 & 70 & 164 & 4 & – & \textgreater{}1800 & 23,900 & 1487 \\ LRCWOS12 & 70 & 172 & 4 & – & \textgreater{}1800 & 26,020 & 1487 \\ LRCWOS13 & 70 & 190 & 4 & – & \textgreater{}1800 & 26,990 & 1580 \\ LRCWOS14 & 75 & 182 & 4 & – & \textgreater{}1800 & 26,840 & 1580 \\ LRCWOS15 & 75 & 198 & 5 & – & \textgreater{}1800 & 28,520 & 1580 \\ LRCWOS16 & 75 & 212 & 5 & – & \textgreater{}1800 & 29,760 & 1580 \\ LRCWOS17 & 80 & 210 & 5 & – & \textgreater{}1800 & – & \textgreater{}1800 \\ LRCWOS18 & 80 & 225 & 5 & – & \textgreater{}1800 & – & \textgreater{}1800 \\ LRCWOS19 & 100 & 320 & 6 & – & \textgreater{}1800 & – & \textgreater{}1800 \\ LRCWOSThe experimental results show that the \(MILP_{RC}\) model is not able to solve any problem of size up to 30 jobs, while the \(CP_{RC}\) model solved a large number of instances up to 75 jobs.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
###### Abstract  
The capability of efficiently processing the data streams emitted by nowadays ubiquitous sensing devices enables the development of new intelligent services. Data Stream Processing (DSP) applications allow for processing huge volumes of data in near real-time. To keep up with the high volume and velocity of data, these applications can elastically scale their execution on multiple computing resources to process the incoming data flow in parallel. Being that data sources and consumers are usually located at the network edges, nowadays the presence of geo-distributed computing resources represents an attractive environment for DSP. However, controlling the applications and the processing infrastructure in such wide-area environments represents a significant challenge. In this paper, we present a hierarchical solution for the autonomous control of elastic DSP applications and infrastructures. It consists of a two-layered hierarchical solution, where centralized components coordinate subordinated distributed managers, which, in turn, locally control the elastic adaptation of the application components and deployment regions. Exploiting this framework, we design several self-adaptation policies, including reinforcement learning based solutions. We show the benefits of the presented self-adaptation policies with respect to static provisioning solutions, and discuss the strengths of reinforcement learning based approaches, which learn from experience how to optimize the application performance and resource allocation.  
Data Stream Processing; self-adaptive; hierarchical control; MAPE; reinforcement learning 1
# Punching Shear Strength of Reinforced Concrete Flat Plates with GFRP Vertical Grids
## 2 Shear Strength Equations  
The FRP design guidelines provided in the design standards are modified based on steel reinforced concrete. Current design codes have an identical general form for calculating shear strength as the sum of the shear strength of concrete and shear reinforcement by Equation (1). However, in each code, the amount of shear reinforcement is considered as a different ratio. In this study, the GFRP vertical grid, whose performance has been verified in previous research ([PERSON] et al. [15]), was determined as the shear reinforcement for the concrete flat plate as shown in Figure 1.  
The GFRP reinforcement consisted of vertical and horizontal strips. Previous research ([PERSON] et al. [15]) noted that the resistance to shear of the horizontal strip was insignificant. Therefore, when the same method as that for the steel stirrup was applied for computing the shear strength of the GFRP plate shear reinforcement, ACI 318, BS 8110 and Eurocode 2 considered only the shear strength attributed to the vertical strip.  
\[V_{n}=V_{c}+V_{f} \tag{1}\]  
where \(V_{n}\) is the nominal punching shear strength (kN), \(V_{c}\) is the nominal shear strength provided by the concrete (kN) and \(V_{f}\) is the nominal shear strength provided by the GFRP vertical grid (kN).
## 2 Materials and Methods  
### Study Area  
The study area covers a coastal stretch of about 14 km length in northern Portugal, between the Douro river mouth in the north and the city of Espinho in the south, corresponding to the coastline of the Vila Nova de Gaia municipality (Figure 1). This municipality is the third most densely populated in Portugal, with a population density in the coastal zone (up to 5 km from the coastline) of about 2500 inhabitants per square kilometre (data from the National Institute of Statistics, referring to 2011; www.ine.pt, accessed on 15 March 2022). Consequently, its coastline is densely occupied and urbanised. The coast is characterised by rocky beaches in the north (Figure 1a) and mostly sandy beaches with rocky outcrops in the centre and south. Being exposed to the high-energy ocean climate of the North Atlantic and suffering from sediment depletion, the Portuguese coast has several sectors that are prone to erosion [18; 32]. Therefore, about 14% of the Portuguese coast is currently defended by artificial structures [33], although these structures, which mitigate sediment loss locally, often increase downdrift erosion [18]. The studied coastal stretch comprises a groyne (Figure 1b), which was built to retain sediments and to fix a submarine outfall, as well as a breakwater (Figure 1c) that was meant to be detached but developed a tombolo during construction, connecting it to the coast [15]. The beaches near Espinho have been managed with repeated artificial beach nourishment [34].  
The north-western Portuguese Atlantic coast is highly energetic, presenting mean significant wave heights of 2-3 m offshore, and mean wave periods of 8-12 s [35; 36]. Waves usually come from the NW, which induces a longshore drift current from north to south. This current is in some areas inverted due to the presence of obstacles (such as breakwaters, jetties, groynes, ebb tidal deltas and bars) that promote wave diffraction. The continental shelf is 30-40 km wide [37] and the local tides are dominated by a semidiurnal regime [38], with amplitudes ranging between 2.5 m and 3.8 m.  
Figure 1: The Vila Nova de Gaia coast (left panel) and its location in Northern Portugal (red rectangle), and closeups of the rocky sector in the North (**a**), the groyne at Praia de Canide (**b**) and the breakwater at Aguda beach (**c**) (image: GoogleEarth, Data SIO, NOAA, U.S. Navy, NGA, GEBCO).  
The regional atmospheric climate varies seasonally. Mediated by the Azores Anticyclone [39], predominant winds from northerly and north-westerly directions occur during the whole year, but with the highest amplitude during the summer months [40]. During autumn and winter, southerly, south-westerly and westerly winds become dominant, though northerly winter winds continue to occur.
###### Abstract  
The distribution-level phasor measurement unit(D-PMU), as a new type of measurement equipment, can support the fast and high-precision observations of active distribution networks. This paper presents a new D-PMU optimal placement method that can be used to reconcile investments with the reliability of high-precision observation systems. The multi-objective optimization model primarily considers the influence of topology changes and \(N\)-1 contingencies on observation reliability. Its objectives include minimizing the number of D-PMUs, maximizing network measurement redundancy (NMR) and the average number of observable buses under \(N\)-1 contingencies (ANOBC). The model is extended to a form of multi-topology weighting and is combined with zero-injection buses and other measurements. We take the observability of high-weight topology set as the constraint. The model can be solved using the non-dominated sorting genetic algorithm-III (NSGA-III), which gives the set of Pareto optimal solutions. Then, the D-PMU placement order determination method based on the spatial electrical distance is proposed to improve the effect of state estimation faster. IEEE standard systems are taken to verify the effectiveness of the proposed method. With the same number of D-PMUs, this method derives more ANOBC and NMR than other methods. When considering topology changes, the proposed method can use fewer additional D-PMUs to substantially increase the ANOBC and NMR.  
D-PMU; active distribution network; observability; \(N\)-1 contingencies; non-dominated sorting genetic algorithm-III; topology change +
Footnote †: journal: Applied Sciences
## 2 Experimental Setup and Data Acquisition
The following output parameters were recorded during the experiments:  
* Vibration accelerations of the cylinder head in three orthogonal directions (X, Y, Z);
* Combustion chamber pressure (to assess combustion processes and engine operational correctness);
* Hourly fuel consumption (measured using AVL 733 m). After completing each measurement series, the data were transferred from the measurement cassette to a computer for subsequent vibration signal analysis.