esa-sceva/satcom-chunk-collection huggingface dataset:
_model sciences_  
_Concept Paper_  
**Ancillary Services Offered by Distributed Renewable Energy Sources at the Distribution Grid Level:**  
**An Attempt at Proper Definition and Quantification**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1,**  
**[PERSON] 2, [PERSON] 1\({}^{\text{\textregistered}}\), [PERSON] 1\({}^{\text{\textregistered}}\),**  
**[PERSON] 1 and [PERSON] 2\({}^{\text{\textregistered}}\)**  
Received: 22 September 2020; Accepted: 10 October 2020; Published: 13 October 2020  
**Abstract:** The gradual displacement of synchronous generators driven by conventional power plants, due to the increasing penetration of distributed renewable energy sources (DRES) in distribution grids, is creating a shortage of crucial ancillary services (AS) which are vital for the frequency and voltage stability of the grid. These AS, and some new ones, could now be offered by the DRES, particularly those that are converter interfaced, in a coordinated way in order to preserve the grid stability and resilience. Although recent standards and grid codes specify that the DRES exhibit some system support functions, there are no specifications on how to measure and quantify (M & Q) them both at DRES level and in aggregated form. The M & Q of AS is crucial, since it would allow the AS to be treated as tradable AS in the current and future AS markets. This paper attempts to define a number of AS that can be offered by converter-interfaced DRES and suggests methods for their M & Q. The new AS addressed are: (1) inertial response; (2) primary frequency response; (3) active power smoothing (ramp-rate limitation); (4) exchange of reactive power for voltage regulation; (5) fault-ride-through (FRT) and contribution to fault clearing; (6) voltage harmonic mitigation. Additionally, a rough estimation of the additional investment and operational cost, as well as the financial benefits associated with each AS is provided in order to form the basis for the development of business models around each AS in the near future.  
**Keywords:** active harmonic filtering; active power smoothing; ancillary services; distributed renewable energy sources; distribution grids; inertial response; primary frequency response; voltage regulation
## 2 Material and Methods
### Procedures  
#### 2.2.1 Anthropometric Data  
The body weight of the schoolchildren was determined with an electronic scale, accurate to 0.1 kg (Tanita BC-545, Tokyo, Japan), and their height was measured with a portable measuring rod accurate to 0.1 cm (Leicester Tanita HR 001, Tokyo, Japan). The body mass index (BMI) of the schoolchildren was computed by the ratio of their body weight (kg) to their squared height (m\({}^{2}\)). Further, BMI z-score (zBMI) was calculated following the age- and sex-specific thresholds of the WHO [25] and, consequently, the proportion of excess weight (overweight and obesity) of schoolchildren was determined.
## Nomenclature  
\begin{tabular}{l l l} \(Q_{d}\) & [m\({}^{3}\)/h] & Rated flow \\ \(H_{d}\) & [m] & Rated head \\ \(n_{d}\) & [rev/min] & Rated rotating speed \\ NPSHr & [m] & Required net positive suction head \\ \(D_{1}\) & [m] & Impeller inlet diameter \\ \(D_{2}\) & [m] & Impeller outlet diameter \\ \(b_{2}\) & [m] & Impeller outlet width \\ \(N\) & & Number of blades \\ \(D_{3}\) & [m] & Volute base diameter \\ \(D_{4}\) & [m] & Pump inlet diameter \\ \(D_{5}\) & [m] & Pump outlet diameter \\ \(t\) & [s] & Time \\ \(u\) & [m/s] & Fluid velocity \\ \(p\) & [Pa] & Pressure \\ \(\rho\) & [kg/m\({}^{3}\)] & Density \\ \(\mu\) & [kg/(m-s)] & Effective viscosity \\ \(f\) & [N/m\({}^{3}\)] & Body force \\ \(g\) & [m/s\({}^{2}\)] & Gravity constant \\ \(F_{1}\) & & Function value of the SST \(k\)-\(\omega\) turbulence model \\ \(\varphi\) & & Coefficients of the SST \(k\)-\(\omega\) turbulence model \\ \(\varphi_{1}\) & & Coefficients of the SST \(k\)-\(\omega\) turbulence model \\ \(\varphi_{2}\) & & Coefficients of the SST \(k\)-\(\epsilon\) turbulence model \\ \(Z\) & [m] & Height difference between inlet and outlet center points \\ \(\eta\) & [\%] & Efficiency \\ \(H\) & [m] & Head \\ \(Q_{v}\) & [m\({}^{3}\)/h] & Flow rate \\ \(P_{s}\) & [kw] & Shaft power \\ \(\Psi\) & & Diffusion coefficient of flow scalar \\ \(F\) & [N] & Force \\ \(T\) & [s] & Impeller rotation period \\ \(\theta\) & [\({}^{\circ}\)] & Hydraulic radial force Angle \\ \(a\) & [N] & Amplitude \\ \(\omega\) & [rad/s] & Angular velocity \\ \(\Phi\) & [rad] & Initial- phase \\ \(b\) & [N] & Constant terms \\ \(R^{2}\) & & Goodness of fit \\ \(S_{1}\) & & Fit residuals \\ \end{tabular}  
Appendix AFigure A2. Residual curves of radial hydraulic force components in X direction on blade 1 under different flow conditions: (a) 0.9\(Q_{d}\); (b) 1.0\(Q_{d}\); (c) 1.1\(Q_{d}\); and (d) 1.2\(Q_{d}\).  
Figure A2. Residual curves of radial hydraulic force components in X direction on blade 1 under different flow conditions: (a) 0.9\(Q_{d}\); (b) 1.0\(Q_{d}\); (c) 1.1\(Q_{d}\); and (d) 1.2\(Q_{d}\).  
\begin{tabular}{c c c c c c c} \hline \hline
**Direction** & **Blade No.** & \begin{tabular}{c} \(a\) \\ **[N]** \\ \end{tabular} & \begin{tabular}{c} _\(\omega\)_ \\ **[rad/s]** \\ \end{tabular} & \begin{tabular}{c} \(\Phi\) \\ **[rad]** \\ \end{tabular} & \
\begin{tabular}{c} \(\mathbf{b}\) \\ **[N]** \\ \end{tabular} &
\begin{tabular}{c} \(\mathbf{R^{2}}\) \\ \end{tabular} \\ \hline \multirow{4}{*}{X} & 1 & 32.852 & 303.687 & 1.161 & 1.651 & 0.986 \\  & 2 & 32.852 & 303.687 & 0.114 & 1.651 & 0.986 \\  & 3 & 32.852 & 303.687 & \(-\)0.  
Figure A3: Fitted curves of radial hydraulic force components in Y direction on blade 1 under different flow conditions: (a) 0.9Q\({}_{d}\); (b) 1.0Q\({}_{d}\); (c) 1.1Q\({}_{d}\); and (d) 1.2Q\({}_{d}\).
## 1 Introduction
### Literature Review on Prediction of Maximum Wall Deflection  
Predicting the maximum deflection of a diaphragm wall before construction is a crucial task in building design. It is common to utilize empirical methods and numerical  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Depth (m)**} & **Soil** & **Use \(N\)** & \(C^{{}^{\prime}}\) & \(\Phi^{{}^{\prime}}\) & \(\gamma_{\textit{unsat}}\) & \(\gamma_{\textit{sat}}\) & _Es_ & \\  & **Classification** & **Value** & **(kN/m2)** & **(e)** & **(kN/m3)** & **(kN/m3)** & **(kN/m2)** & **(kN/m2)** & **\(v\)** \\ \hline
0.0–4.0 & SF, ML, SM & 5 & 0 & 30 & 19.3 & 19.5 & 15,000 N & 0.33 \\
4.0–15.7 & GW & 40 & 4.9 & 38 & 21.6 & 22.0 & 7840 N–9800 N & 0.28 \\
15.7–20.3 & GW, SW & 35 & 0 & 38 & 21.1 & 21.4 & 7840 N–9800 N & 0.28 \\
20.3–24.4 & ML & 16 & 0 & 32 & 19.4 & 19.7 & 100,000 N & 0.32 \\
24.4–33.0 & GW & 100 & 9.8 & 40 & 22.1 & 22.3 & 7840 N–9800 N & 0.26 \\ \hline \end{tabular}
\end{table}
Table 1: Input form of soil layer strength parameters in Case 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Depth (m)**} & **Soil** & **Use \(N\)** & \(C^{{}^{\prime}}\) & \(\Phi^{{}^{\prime}}\) & \(\gamma_{\textit{unsat}}\) & \(\gamma_{\textit{sat}}\) & _Es_ & \\  & **Classification** & **Value** & **(kN/m2)** & **(o)** & **(kN/m3)** & **(kN/m3)** & **(kN/m2)** & **\(v\)** \\ \hline
0.0–3.7 & SF, ML & 7 & 0 & 30 & 19.4 & 19.5 & 24,000 N & 0.33 \\
3.7–16.1 & GW & 40 & 4.9 & 38 & 21.9 & 22.0 & 7840 N–9800 N & 0.28 \\
16.1–19.5 & GM, SM & 23 & 0 & 34 & 21.1 & 21.4 & 7840 N–9800 N & 0.31 \\
19.5–24.4 & ML & 13 & 0 & 31 & 19.5 & 19.7 & 100,000 N & 0.33 \\
24.4–35.5 & GP, GM & 100 & 9.8 & 40 & 22.1 & 22.3 & 7840 N–9800 N & 0.26 \\ \hline \end{tabular}
\end{table}
Table 2: Input form of soil layer strength parameters in Case 2.  
approaches with sophisticated constitutive models for this purpose [27; 28; 29]. Several empirical techniques, expressed through equations or design charts, are available for estimating the maximum wall deflection induced by excavation [30; 31; 32; 33].
## 2 Methods  
As in a previous work [1], the specialized version of Perdew-Burke-Ernzerhof functional for solid materials [42] was used to study the mechanical properties of the uranyl carbonate mineral, rutherfordine. This functional is implemented in the CASTEP (Cambridge Serial Total Energy Package) program [43], a module of the Materials Studio package [44] (Dassault Systemes BIOVIA, version 8.0, San Diego, 2017) which was employed in all the calculations. The pseudopotentials employed for the carbon and oxygen ions in the unit cell of rutherfordine were standard norm-conserving pseudopotentials [45] provided by the CASTEP program, and the norm-conserving relativistic pseudopotential employed for uranium ion was developed in our laboratory in previous works [6; 7].  
The atomic positions and unit cell parameters of rutherfordine were optimized by using the Broyden-Fletcher-Goldfarb-Shanno method [5; 46] with a convergence threshold on atomic forces of 0.01 eV/A. This method was also used in order to optimize the structure of the material considered under the effect of different applied pressures. The calculation parameters used in this work, that is, the kinetic energy cut-off and k-point mesh [47], 1050 eV and 9 \(\times\) 4 \(\times\) 10 (50 k points), respectively, were much larger than the ones used in a previous work [1], 1000 eV and 5 \(\times\) 3 \(\times\) 6 (18 k points), in order to confirm the previous results and to obtain a high quality description of the mechanical behavior of rutherfordine mineral. The elastic constants required to calculate the mechanical propertiesof the considered material and to study the mechanical stability of its crystal structure were obtained from stress-strain relationships using the finite deformation method [1; 48].  
From a theoretical point of view, the theoretical solid-state calculations reported in this paper have been carried out with the best available methods applicable to the study of crystalline materials and with very demanding high-quality calculation parameters. The present computations could be improved by using more advanced energy-density functionals as hybrid or meta-functionals, but their efficient implementation in the current solid-state codes is still under development. A restriction, which could be removed from the calculations, is the core-valence approximation. This could be made by including explicitly the inner uranium electrons in the calculations, thus avoiding the use of pseudopotentials. However, the realization of all-electron calculations is very complicated and expensive, especially for systems containing rare earth elements. The norm-conserving relativistic pseudopotential developed in our laboratory [6; 7] has permitted to perform very precise studies of the structural, spectroscopic, mechanical, and thermodynamic properties of uranium containing materials [6; 7; 8; 9; 10; 11; 12; 13; 14; 15], and the results were in very good agreement with the experimental data in all the cases in which they were available for comparison.
## 3 Evaluation
### Scenario 2  
The second scenario is the most restrictive one because the BS is outside the sensing area, far from the edge of the network and, consequently, far from all the nodes. Figure 7 shows that the performance metrics are lower than in the previous case for all the tested methods. Again, DCAGBS gets very good results, only matched by CHEF in FND and LND. DCAGBS is the best proposal for LND, which implies that the applications have a longer lifetime. Additionally, the value for HND obtained by DCAGBS clearly outperforms other distributed methods. This implies that, when using DCAGBS, we can get that at least half of the nodes are operative much longer.  
Figure 6: First Node Dead (FND), Half Node Dead (HND), and Last Node Dead (LND) for scenario 1.  
Figure 7: First Node Dead (FND), Half Node Dead (HND), and Last Node Dead (LND) for scenario 2.
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 2 Key Concepts in Modeling Over-the-Sea Channels
### Measurements and Modeling of Propagation Loss  
PL can be defined as the reduction in the power density of an electromagnetic wave, as the propagation takes place through space [6]. From a basic point of view, the channel measurements require a transmitter and a receiver. Figure 8 shows the fundamental setup for radio wave propagation measurements using continuous waves. Amplification of the output from the signal generator is performed before the transmitting antenna sends out a signal. As the signal passes through an over-the-sea channel, the signal is captured by the receiving antenna. After that, the signal is filtered and displayed on a spectrum analyzer. Depending on the application, directional or omni-directional antennas can be used. The PL can be estimated using the following simple equation:  
\[PL=P_{T}+G_{Amp}+G_{AntT}+G_{AntR}-L_{f}-P_{R} \tag{3}\]  
where \(P_{T}\) is the output power of the signal generator, \(G_{Amp}\) is the gain of the amplifier, \(G_{AntT}\) is the gain of the transmitting antenna, \(G_{AntR}\) is the gain of the receiving antenna, \(L_{f}\) is the loss of filter, and \(P_{R}\) is the power indicated in the spectrum analyzer.  
Figure 8: Basic setup for measurements of propagation loss.  
Figure 7: Transceiver platform in atmospheric ducts.  
For free space and clear air conditions, the PL can be modeled using the free space loss (FSL) model [6] using the following equation, where \(L_{FSL}\) is the free space loss in dB, \(d\) is the propagation distance, and \(f\) is the frequency:  
\[L_{FSL}=\ -27.56+\ log_{10}(f)+\ 20 log_{10}(d) \tag{4}\]  
The reflection of the sea surface is very important to design a channel for the ocean environment. When it exists alongside with the LoS (direct ray) component, the PL can be measured using the two-ray model [6] as follows:  
\[L_{2-ray}=-10 log_{10}\{\left(\frac{\lambda}{4\pi d}\right)^{2}[2\sin\left( \frac{2\pi h_{t}h_{r}}{\lambda d}\right)]^{2}\}, \tag{5}\]  
where \(L_{2-ray}\) is the two-ray path loss in dB, \(h_{t}\) is the height of the transmitter, \(h_{r}\) is the height of the receiver, and \(\lambda\) is the wavelength.  
A simplified three-ray model [7] with an assumption that a horizontally homogeneous evaporation duct layer exists can be expressed as a simplified three-dimensional model [7]:  
\[L_{3-ray}=\ -10 log_{10}\ \{\left(\frac{\lambda}{4\pi d}\right)^{2}[2(1+\Delta) ]^{2}\} \tag{6}\]  
with  
\[\Delta=2\sin\left(\frac{2\pi h_{t}h_{r}}{\lambda d}\right)\sin\left[\frac{2 \pi(h_{e}-h_{t})(h_{e}-h_{r})}{\lambda d}\right] \tag{7}\]  
where \(h_{t}\) and \(h_{r}\) are the heights of transmitter and receiver in meters, respectively, \(h_{e}\) is the effective duct height, \(\lambda\) is the wavelength in meters, and \(d\) is the propagation distance in meters.
## 2 Materials and Methods
### Color Mark Detection and Positioning Model
According to the detection model, color mark positioning can be achieved by counting the number of encoder pulses from the color mark pulse to the printing roller, and the calculation formula is expressed as  
\[P(S_{n})=\frac{Cnt\Big{(}S_{n},P_{ref}\Big{)}L_{c}^{*}}{C^{*}}\ n=1,2,3......, \tag{2}\]  
where \(S_{n}\) is the register color mark; \(P\) represents the color-mark-detection position; \(Cnt\Big{(}S_{n},P_{ref}\Big{)}\) is the code pulse-counting value from the color mark to the printing roller \(P_{ref}\); \(L_{c}^{*}\) provides the printing roller circumference; \(C^{*}\) is the rotary encoder resolution size. Positioning accuracy and detection reliability are crucial to the control system. The system's theoretical accuracy can be evaluated according to the repetition detection accuracy of the color mark. As shown in Table 1, the system detection accuracy corresponding to different encoder resolutions and printing roller circumferences is listed. According to the data analysis, the detection accuracy decreases when the printing roller circumference is larger and increases when the rotary encoder increases in resolution. In theory, system detection accuracy can be improved infinitely by increasing the number of subdivided encoder pulses, such as \(L_{c}^{*}=400\) mm and \(C^{*}=200,\!000\), in which case the system detection accuracy is then \(\tau=0.002\) mm per pulse. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Resolution**} & **1024** & **2048** & **3600** & **4096** & **5000** & **8192** & **10,000** & **20,000** & **40,000** & \
\begin{tabular}{c} **Circumference** \\ **(mm)** \\ \end{tabular} \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Accuracy \\ (mm) \\ \end{tabular} } & 0.39 & 0.195 & 0.111 & 0.097 & 0.08 & 0.049 & 0.04 & 0.02 & 0.01 & 400 \\ \cline{2-10}  & 0.488 & 0.244 & 0.139 & 0.122 & 0.1 & 0.061 & 0.05 & 0.025 & 0.012 & 500 \\ \cline{2-10}  & 0.585 & 0.292 & 0.166 & 0.146 & 0.12 & 0.073 & 0.06 & 0.03 & 0.015 & 600 \\ \cline{2-10}  & 0.683 & 0.342 & 0.194 & 0.171 & 0.14 & 0.085 & 0.07 & 0.035 & 0.017 & 700 \\ \cline{2-10}  & 0.781 & 0.39 & 0.222 & 0.868 & 0.16 & 0.097 & 0.08 & 0.04 & 0.02 & 800 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection accuracy corresponding to different printing roller circumferences and encoder resolutions. Figure 4: The structure diagram of register color mark detection and positioning model.
# Online Static Security Assessment of Power Systems Based on Lasso Algorithm
## 4 Online Static Security Assessment Method
### Solution Process of Lasso Module  
According to the observations and responses of the MSA-Lasso algorithm, the solution procedures of the Lasso module are listed as follows, and the flowchart is as shown in Figure 3.  
Step 1: Input the data of the network, particularly the load conditions (i.e., light, normal, heavy load conditions). Input the ranges of controlled variables in Equation 14. In addition, the steps of the discontinuous variables, \(T\) and \(Q_{C}\), also need to be provided.  
Step 2: Set the iteration number \(ite=1\), and its maximum value \(ite_{\max}\).  
Step 3: Calculate the load flow of the power system with randomly controlled variables in the condition that every line has an outage respectively; then, the corresponding response \(PI_{c}\) is computed. In this step, the information is recorded, which includes the random controlled variables, the outage line, and the corresponding index \(PI_{c}\).  
Step 4: Judge the terminate conditions. If \(ite\geq ite_{\max}\), the cyclical process comes to an end, and continues to the following steps. Otherwise, come back to Step 3, and increase the iteration number by 1.  
Step 5: The Lasso module is trained with conservations and corresponding responses, where each conservation is the set formed by the random controlled variables and the status of lines, and each response is the security index \(PI_{c}\). Moreover, it should be noted that the size of Lasso training set \(S\) is same as the maximum iteration number \(ite_{\max}\).  
Step 6: The security index \(PI_{c}\) can be predicted by adopting the Lasso module, with a known set of controlled variables and outage lines in the power system.
# Using Zigzag Persistent Homology to Detect Hopf Bifurcations in Dynamical Systems
## 2 Materials and Methods
### Time-Delay Embedding  
In order to analyze time-series data using persistent homology, we will use a tool from nonlinear time-series analysis called the time-delay embedding. Here, we will give an intuitive overview of the method; however, we refer the interested reader to [31] for more details.  
Time-delay embedding is a method used to reconstruct an attractor of a dynamical system given only one observation function. This is done by taking the observation function, \([x_{1},\ldots,x_{n}]\), and two parameters, an embedding dimension \(d\) and a delay (or lag) \(\tau\). The reconstruction is then the point cloud \(\mathbf{X}=\{\mathbf{x}_{i}:=(x_{i},x_{i+\tau},\ldots,x_{i+(d-1)\tau})\}\subset \mathbb{R}^{d}\). Due to theoretical results from [PERSON] [32] and [PERSON] [33], given that the right choices of parameters and some underlying assumptions are satisfied, this reconstruction is in fact an embedding. Thus, the reconstruction has the same topological structure as the attractor of the dynamical system. In practice, not all parameter choices are optimal, and while there are no theoretical guarantees on how to pick the best parameters, many heuristics for making reasonable parameter choices have been developed [34; 35; 36; 37; 38; 39].  
Significant work has been done in using persistent homology on time-delay embeddings. Specifically, in [1], the authors provide a full theoretical analysis of persistent homology for time-series analysis. The authors show that maximum persistence of the persistence diagram computed from the time-delay embedding can be used to quantify periodicity. Additionally, they provide a description of how persistence diagrams change depending on the embedding dimension and the delay parameter. This work provides sound theoretical backing for the use of persistent homology on time-delay embeddings.  
In existing methods combining TDA with time-series analysis, most works analyze a collection of time series by embedding each one into a point cloud using time-delay embedding. However, this can be computationally expensive and requires analysis of the collection of computed persistence diagrams. Instead, we will employ a generalized version of persistent homology to avoid these additional steps.
## 3 System Model
### UAV-Assisted Edge Computing  
The data from the task can be transmitted via a wireless uplink when the task is carried out by a UAV. The spectral bandwidth of each user is \(B_{0}\). The computation bits offloaded from the user are expressed as Formula (6).  
\[R_{i}^{c}(n)=B_{0}\tau\log_{2}\left(1+\frac{p_{i}(n)h_{i}(n)}{N_{0}+w_{i}(n)P_{ NLOS}}\right) \tag{6}\]  
Among them, \(N_{0}\) represents the noise power spectral density, \(p_{i}(n)\) represents the transmit power of user \(i\) in time slot \(n\), and \(w_{i}(n)\) represents whether there is an obstacle between the UAV and user \(i\) at time slot \(n\). \(P_{NLOS}\) represents transmission loss. Formula (7) is used to express the corresponding amount of energy used to transmit computation bits.  
\[E_{i}^{c}(n)=p_{i}(n)\tau \tag{7}\]  
Local computation bits and offloading bits, which are determined using Formula (8), make up the total computation bits.  
\[R(\mathsf{s}(n),\mathsf{q}(n))=\sum_{n=1}^{N}\sum_{i=1}^{M}\Bigl{\{}(1-s_{i}(n ))R_{i}^{l}(n)+s_{i}(n)R_{i}^{c}(n)\Bigr{\}} \tag{8}\]  
In addition to the energy consumption of transmission and calculation and flight of the UAV, the actual system also involves baseband processing of other static energy consumption. The static power \(P_{c}\) is assumed to be constant, irrespective of other energy consumption [10]. Then, the total energy consumption can be expressed as Formula (9).  
\[E(\mathsf{s}(n),\mathsf{q}(n))=\sum_{n=1}^{N}\sum_{i=1}^{M}\Bigl{\{}\kappa f_{ i}^{3}(n)+s_{i}(n)p_{i}(n)+P_{c}\Bigr{\}}\tau \tag{9}\]  
According to [10], the computational efficiency is defined as the ratio of the total computation bits to the total energy consumption.  
\[\eta=\frac{R(\mathsf{s}(n),\mathsf{q}(n))}{E(\mathsf{s}(n),\mathsf{q}(n))} \tag{10}\]