esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Analysis  
In this paper, the effect of regular waves on the propulsive performance of a flexible foil is investigated, considering the encounter frequency \(\omega_{0}\), wavelength \(\lambda\), and wave amplitude \(a\). Other conditions, such as phase difference, foil motion, and flexible parameters, have been discussed in previous studies. One can find details in references [38; 39]. For convenience, we change \(\omega_{0}\), \(\lambda\), and \(a\) to \(\omega_{0}/2\pi\), \(\lambda/c\), \(a/c\) by non-dimensional treatment.  
In our analysis, a basic parameter case is given and a controlling variate method is adopted, that is, only one variable of the basic case is changed in every working condition and the others remain constant. Therefore, we give the basic case setting as the inflow constant velocity \(V_{0}=0.1\) m/s. The regular wave height is \(H=2a=0.04\) m and the wavelength \(\lambda=6.64\) m. The foil is profile NACA0012 with the chord length \(c=0.1\) m and the submergence depth \(h_{l}=0.12\) m, and the phase difference between the heaving and pitching motion of the foil is \(\varphi=90^{\circ}\). The foil motion period is \(T=2\) s \(=1/f=2\pi/\omega_{f}\), where \(\omega_{f}\) is the angular frequency of foil motion. The phase between wave and foil motions is \(\psi=-90^{\circ}\). For foil flexible deforming, \(\delta_{c}=0.2\)\(c\), \(\varphi_{0}=-90^{\circ}\), while the controlling coefficients \(s=100\), \(\varepsilon=2.0\). The heaving amplitude of the foil is \(y_{0}=0.5\)\(c\) and the pitching angle is \(\theta_{0}=15^{\circ}\). The wave encounter frequency \(\omega_{0}=\omega_{f}\). In addition, for comparison, the performance in infinite flow field and under calm water of flexible, as well as the rigid foil, have also been calculated, where \"infinite\" means that no free surface exists and the whole computational domain is filled with water.  
Before discussing the results, another important parameter should be mentioned--the submergence depth of foil \(h_{l}\)--as the flow under waves in deep water is strongly affected by it. In our preliminary studies based on the above basic case for the flexible foil, with \(h_{l}\) changing from 0.12 m to 0.20 m, the average thrust force coefficient \(C_{T}\) descends from 3.21 to 2.8 and its decreasing rate slows down. Thus, combined with the following analysis, we can infer that the favorable effect of wave on the foil's propulsive performance will be reduced with increasing \(h_{l}\) and this effect will disappear when \(h_{l}\) is deeper than half the wavelength.
## 2 Materials and Methods
### Double Threshold Case  
The latest active SoC restoration mechanism is a hybrid of the dead-band strategy and of the available energy strategy. The DoF that is exploited is the possibility of offsetting the \(\mathrm{P_{req}}\) while the system frequency is in its dead-band. Therefore, the flag variable is triggered when SoC gets outside the thresholds in Table 3 and stays on until SoC approaches \(\mathrm{SoC_{target}}\pm\varepsilon\). In any case, the restoration process can only occur when the frequency is inside the dead-band expressed in Table 1. This is called the double threshold strategy. Equation (7) is reworked as Equation (8) to depict the mechanism.  
\[\mathrm{P_{req}} =\mathrm{P_{FCR}}+\mathrm{R_{offset}}*\mathrm{P_{nom}}\] if \[\mathrm{SoC(t)}\ \geq\ \mathrm{SoC_{target}}\wedge\mathrm{flag}=1 \wedge\left|\mathrm{dF}\right|\leq\mathrm{dF_{DB}}\] \[\mathrm{P_{req}} =\mathrm{P_{FCR}}-\mathrm{R_{offset}}*\mathrm{P_{nom}}\] if \[\mathrm{SoC(t)}\ \leq\ \mathrm{SoC_{target}}\wedge\mathrm{flag}=1 \wedge\left|\mathrm{dF}\right|\leq\mathrm{dF_{DB}}\] \[\mathrm{P_{req}} =\mathrm{P_{FCR}}\] elsewhere. (8)  
The resulting droop control curve with the exploitable DoF is the same as shown in Figure 6 for the dead-band strategy. The double threshold concept, thus the domain in terms of SoC and dF where the DoF can be exploited is described in Figure 8.  
Figure 7: Schematic representation of available energy DoF case: (**a**) flow diagram for flag variable activation; (**b**) droop curve adopted. The red line is the reference droop curve without offset, to be kept while flag = 0. The pink area delimited by the black dashed lines represents the range of power setpoints that can be adopted in the case flag = 1. For a given dF, dP can vertically span within the pink zone between the black dashed lines. In the simulation, this is used to restore the SoC in the case of scarce or excessive energy content.
# Optimization of Pico-eNB Tx Power and the Effects of Picocell Range Expansion in Multiband HetNet
## 5 Effects of CRE  
### CRE in HetNet  
In this section, we investigate the effect of CRE technique using a positive CSO under conditions including pico-eNB Tx powers close to the optimal value. Figure 11 shows the CRE using a positive CSO in a HetNet.  
Figure 10: Analysis of use rate of modulation method used in downlink MCS as a function of pico-eNB Tx power.  
Figure 9: Average user throughput of specific UE connected with macro-eNBs as a function of pico-eNB Tx power.  
In a normal case without CRE, UE1 outside the picocell is connected with a macrocell, as shown in Figure 10(a). With CRE, UE1 with a positive CSO is connected from the macrocell to the extended picocell, as shown in Figure 10(b). In this case, the original picocell range appears to be wider, representing an \"extended picocell\", for the UE with a positive CSO in the CRE zone. Consequently, the connection point of the UE in the CRE zone should be changed from macro- to pico-eNBs when CRE is activated.  
Figure 12 shows the cell selection procedure when CRE is activated in the HetNet. The RRC connection is assumed to be first established between the UE and macro-eNB. Then, according to CRE, the UE creates a positive CSO to the RSRPico obtained from the RSRP of the picocell. If the combination of the RSRPico plus the CSO is larger than the RSRPmacro, the connection of the UE is changed to the pico-eNB through a handover procedure and an RRC connection is established between the UE and pico-eNB. In this way, the CRE increases the probability of the UE connecting with pico-eNBs. If CRE does not work, i.e., CSO = 0 dB, the picocell range remains unchanged.
## 4 Development of ViPER+ for Construction Vehicles and Equipment Pose Estimation
### Localization Subsystem  
#### 4.2.1 TDOA Localization  
To calculate our tags' location, the localization subsystem collects the beacon messages from the data collection subsystem. If more than four anchors have reported the reception of a beacon message, the subsystem uses the TDOA estimation method to estimate the tag location. The reference anchor is chosen from one of the anchors using a TDOA estimation approach. Note that the reference anchor is different from the time sync anchor that was used to synchronize the clocks. A non-convex optimizer was given the reference anchor and the other chosen anchors. The optimization solver calculates the optimum location using the location of anchors and the given timestamps. For this process, the TDOA inputs are estimated using the given timestamps. From Equation (1), the TDOA input can be estimated for anchor \(a\) denoted as \(I_{a}\), which is the speed of light \(c\), multiplied by difference of each timestamp and the reference anchor's timestamp. After all the inputs are estimated, the next step is to define an objective function as shown in Equation (2). The parameters used in Equation (2) defined in Table 1.  
\[I_{a}=c\times\left(t_{a}-t_{ref}\right) \tag{1}\]  
\[f(x,\ y)=\sum_{a}^{anchors}\left(\sqrt{\left(x-x_{a}\right)^{2}+\left(y-y_{a} \right)^{2}}-I_{a}\right)^{2} \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Description** \\ \hline \(\left(x_{a},y_{a}\right)\) & Location of anchor a \\ \(\left(x,y\right)\) & Location of the tag \\ \(I_{a}\) & TDOA input for anchor a \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the objective function.  
The optimizer then estimates the location where the value of the objective function was minimized. In Equation (3), the \((x^{*},y^{*})\) is the estimated location of the tag.  
\[(x^{*},y^{*})=\underset{x,y}{arg\,inf}(x,y) \tag{3}\]  
\[Residual\ value=f(x^{*},y^{*}) \tag{4}\]  
In addition to the estimation of the tag, the TDOA algorithm also gives us a value called the residual value. The value of this parameter is calculated according to Equation (4). In an optimum situation where there is no error in any input, this value should be close to 0. Although this value cannot precisely represent the location estimation error, researchers have used this value to enhance the location estimation by removing outputs with high residual values [46].
# Quantum Approximation for Wireless Scheduling
## 5 Performance Evaluation
### Software Implementation  
The application of the quantum gates, the basic units of the quantum circuit, is expressed by unitary operators. Based on the definitions of Hamiltonians in Section 4, the objective operator \(U_{O}(\gamma_{\zeta})\), constraint operator \(U_{C}(\gamma_{\zeta})\), problem operator \(U_{P}(\gamma_{\zeta})\), and mixing operator \(U_{M}(\beta_{\zeta})\), which are unitary operators, can be defined as follows:  
\[U_{O}(\gamma_{\zeta}) = e^{-i\gamma_{\zeta}U_{O}}, \tag{31}\] \[U_{C}(\gamma_{\zeta}) = e^{-i\gamma_{\zeta}pH_{C}},\] (32) \[U_{P}(\gamma_{\zeta}) = U_{O}(\gamma_{\zeta})U_{C}(\gamma_{\zeta})=e^{-i\gamma_{\zeta}(H _{O}+\rho H_{C})},\] (33) \[U_{M}(\beta_{\zeta}) = e^{-i\beta_{\zeta}H_{M}}, \tag{34}\]  
where \(\gamma_{\zeta}\) and \(\beta_{\zeta}\) are in \(\gamma\equiv\gamma_{1}\cdots\gamma_{p}\) and \(\beta\equiv\beta_{1}\cdots\beta_{p}\), respectively: \(\zeta\in\mathbb{Z}^{+}\) and \(1\leq\zeta\leq p\). Note that the implementation of \(U_{P}(\gamma_{\zeta})\) and \(U_{M}(\beta_{\zeta})\) is the core of QAOS implementation.  
In Figure 2, cirq.rz() and cirq.CNOT() are used for the implementation of \(U_{P}(\gamma_{\zeta})\). Note that, cirq.rz() and cirq.CNOT() represent the rotation-\(Z\) gate and the controlled-NOT gate, respectively. In addition, \(U_{M}(\beta_{\zeta})\) is implemented using cirq.rx(), which represents the rotation-\(X\) gate.  
The part that finds the optimal parameters using Keras (one of the well-known open-source deep learning computation libraries) is shown in Figure 2, from line 29 to line 36. Here, the parametrized quantum circuit (PQC) layer provides the auto-management of variables in the parameterized circuit. In this model, Adam is used as a gradient-based optimizer [27; 28].
## 2 Fundamentals of Pricing Schemes: Uniform, Zonal, and Nodal Pricing
### Pricing Mechanisms
#### 2.2.3 System Marginal Price (SMP)  
Another approach to addressing quasi-fixed costs in electricity markets involves incorporating them directly into market prices, as exemplified by the Korean electricity market. The SMP adopted in the Korean electricity market is determined by the marginal cost of the most expensive unit dispatched to satisfy the demand, including no-load and start-up costs as a form of average cost [22]. This is calculated using Equation (8).  
\[SMP_{t}=\max_{g\in G_{PSI}}\left\{2C_{g}^{QD}p_{g,t}+C_{g}^{LN}+\frac{\sum_{t =s}^{z}\left(C_{g}^{NL}x_{g,t}-C_{g}^{QD}p_{g,t}^{2}+C_{g}^{SU}y_{g,t}\right)} {\sum_{t=s}^{z}p_{g,t}}\right\},\quad\forall t\in T \tag{8}\]  
where \(C_{g}^{QD},C_{g}^{LN}\), and \(C_{g}^{NL}\) denote the coefficients of the quadratic cost function, and \(C_{g}^{SU}\) refers to the start-up costs of generator \(g\). Furthermore, \(x_{g,t}\) and \(y_{g,t}\) are the binary status of the generator, where \(x_{g,t}\) indicates the operating status and \(y_{g,t}\) refers to the start-up status. In addition, \(s\) and \(z\in T\) denote the start and end times of a contiguous commitment period, respectively, and \(G_{PSI}\) is a subset of the generators eligible to set the market price.  
The determination of \(G_{PSI}\) is crucial for replicating the effects of marginal cost pricing based on Lagrangian multipliers. In marginal cost pricing based on Lagrangian multipliers, the binding constraints naturally prevent certain generators from setting the prices. To replicate this effect in the SMP mechanism, which does not inherently have this feature, the Korean market applies the following rules to exclude certain generators from the price settings:  
* Generators with binding ramp rate constraints.
* Generators operating at their minimum output level.
* Generators with binding system stability constraints.  
Additionally, when transmission lines between adjacent zones are congested, the SMP for each zone is calculated using only the \(G_{PSI}\) within that specific zone. This allows the SMP mechanism to emulate Lagrangian multiplier-based pricing by separating market prices under these congestion conditions. Although this mechanism offers a straightforward method for including start-up and no-load costs in market prices, it is plagued by certain challenges, particularly in terms of the price regressivity problem and potential distortion of price signals [22].
## 1 Introduction
### Novelty and Aim of This Work  
As previously explained, the role of a combustion process is vital to increasing the efficiency of domestic or industrial stoves and, subsequently, the sustainability of the process, reducing the amounts of pollutants released into the atmosphere, depending on factors such as biomass composition [21; 22], previous conditions [23], stove design (including its evolution to improve performance) [24; 25; 26], or burning conditions [27; 28; 29]. Thus, some studies have previously dealt with this subject, as can be observed in Table 1. These efforts focused on evolved pollutants applied to the use of domestic or industrial stoves, or simulations [30], in addition to focusing on variables such as the kind of biomass, biomass load, or pre-treatments, like wood washing or drying [31].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Details and Findings** & **Reference** \\ \hline [PERSON] et al. & Analysis of the emissions of three different biomass products, and their torrefied counterparts, in a domestic wood stove. After torrefaction, lower emissions (CO and CH\({}_{4}\)) were found. & [32] \\ [PERSON] et al. & Study of the influence of a chimney on the combustion characteristics of a stove, observing changes in CO production. & [33] \\ [PERSON] et al. & Optimization of the effects of burner pot design by changing supply airflow position in a pellet stove through machine learning. & [34] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recent works related to the subject of this research.  
In that sense, the novelty of this work lies in the optimization of a pellet stove, considering both the raw materials and parameters utilized, depending on the stove's power type. Thus, an adapted optimization of a specific stove for each kind of biomass is offered, as well as an assessment of the influence of raw material on combustion optimization. Considering the above, in more detail, the aim of this work was to carry out the optimization of the combustion process in a commercial stove. For that purpose, different kinds of fuels were used at different air ratios and power levels, followed by assessing efficiency according to the flue gas flow and composition.
## 5 Experiments  
DBSCAN [30], K-means clustering [31], and the proposed SAMD model were compared. The testing dataset was PJM 2018, which was 1 h for the time interval of the electricity price monitoring time. A full understanding of the characteristics of real-time electricity prices was essential for the selection of the forecasting model, and real-time electricity prices were found to have the following characteristics: periodicity, volatility, mean reversion, and heteroscedasticity. Taking real-time electricity prices in the PJM electricity market for the two adjacent weeks of 1-14 February 2018 as an example, it can be seen from Figure 7 that the electricity prices exhibited strong daily and weekly cycle characteristics. Mean reversion was reflected in the fact that when the load supply exceeded demand, the price of electricity was on an upward trend; when the load supply exceeded demand, the price of electricity was on a downward trend, and the average price of electricity tended to be close to the cost price. In order to better understand the characteristics of real-time tariffs, this paper statistically analyzed the real-time tariff data in terms of the six dimensions in Table 1. The JB was mainly used to test whether the electricity price obeyed a normal distribution, and \(Q^{2}(10)\) was mainly used to test whether the electricity price had heteroskedasticity. From the statistical data in Table 1, it can be seen that real-time tariffs had a skewness of not 0 and a kurtosis of more than 3, which was the phenomenon of \"sharp peaks and thick tails\", and it can be concluded that the real-time tariffs had heteroskedasticity.  
Figure 6: SAMD-SVM forecasting flowchart.
## 5 Development of the Models
### Determination of the Model Parameters  
Continuing with the analysis of Figure 8, it was also observed that within the periods of seasonality (among the lags' multiples of the frequency) the most significant autocorrelation occurred with lags 1 and 5 and with some additional values in lags 24, 31, and 32, while the partial autocorrelation yielded a considerable value for the delay 1, 2, 4, and 31. From the previous observations, the AR (31) and MA (32) models and their combination ARMA (31,32) are obtained. However, as explained in [28], the behavior observed in the graph could also refer to an AR signature (1), since the PACF decayed relatively slowly and the ACF showed an abrupt cut in the fourth delay. This indicates that the autocorrelation with the fourth lag spread to higher lags, so another candidate to consider is the MA (4).  
For the seasonal component of the losses, the analysis of the transformed series was performed by means of a differentiation of order (D) equivalent to 32. Due to this differentiation to include seasonality in the SARIMA model, the value 1 was taken for D. Both the ACF and the PACF presented significant values for seasonal lags 1 and 2. From the analysis described, candidate models with SAR (2) (Seasonal autoregressive) and SMA (2) (Seasonal moving average) parameters were obtained, which additionally (including the analysis done for the ARIMA models within the periods of seasonality) had components AR (1) and MA (4). With the above, we had the following values or ranges for the specified parameters.  
From the possible combinations obtained by varying the parameters described in Table 2, 1190 ARIMA and 144 SARIMA models were obtained, many of which yielded similar results to others, taking into account that only some coefficients varied. Therefore, the range of the variables p and q was limited to take values 1 to 4 and 31 to 32 (important values in the autocorrelation function), with which 25 possible ARIMA models to be analyzed were obtained.
## 4 Extension to Constrained Designs
### Influence of Prototype Filter  
This section discusses how different prototype filters can efficiently support the joint signal. According to the Balian-Low theorem [39], there are some fundamental limitations  
Figure 6: (**a**) Pulse compression results in different frequency modulation rates and (**b**) its zoom in results.  
Figure 7: The effect of bandwidth change, time width = 50 us and time width change, bandwidth = 15 MHz on pulse compression output peak value (**a**) and effective time width (**b**) of FM rate mismatch signal.  
in multicarrier systems that make it mathematically impossible to simultaneously satisfy the following desired properties:  
1. Maximum symbol density \(TF=1\);
2. Time-localization \(\sigma_{t}<\infty\);
3. Frequency-localization \(\sigma_{f}<\infty\);  
One of the prototype filters for FBMC-QAM is based on the Hermite polynomial [40], where the Hermite pulses have the same shape in time and frequency, allowing the symmetry to be exploited. Since the pulses are Gaussian-based and have a good joint time-frequency localization \(\sigma_{t}\sigma_{f}=1.02/4\pi\), almost as good as those obtained by Gaussian pulses (\(\sigma_{t}\sigma_{f}\geq~{}1/4\pi~{}\approx~{}0.08\)), they are relatively robust in response to double-selected channels.  
Another prominent filter is the PHYDYAS prototype filter [41]. Figure 8 shows the ambiguity functions of the above multicarrier prototype filters [42]; both PHYDYAS and Hermite filters are well localized in both time and frequency domains. Note that the Hermite filter is better localized in time than PHYDYAS but worse in frequency.
# Dynamic Damage Mechanism and Seismic Fragility Analysis of an Aqueduct Structure
## 3 Seismic Fragility Analysis of the Aqueduct Structure
### Comparison of Fragility Analysis Results of Aqueduct Structure  
The comparison result of the two fragility curves based on the IDA and MSA methodologies is shown in Figure 13. Probability under various earthquake intensities is shown in Table 9. It can be seen that for LS1, the exceedance probability of the structure with IM = 0.11 g, the maximum difference of two fragility curves is about 18.50%, and their tendency is roughly in line. For LS2, the exceedance probability with IM = 0.31 g obtained by the two fragility methods is 81.36% and 69.92%, respectively. The IDA-based fragility curve has a steeper upward trend, with a difference of 11.44% between the two methods, and the two curves are in good agreement. The maximum difference for LS3 is about 12.50% for IM = 0.41 g. The maximum difference between LS4 and LS5 is 17.63%. The LS1 of the two methods shows a steep trend, and the fragility curves of the LS2 (slight damage) are also steep until the fragility curves of the LS3 and LS4 gradually slow down.  
In conclusion, the seismic fragility curves generated using the IDA and MSA methodologies mutually verify the aqueduct structure's seismic fragility results. The main reason for the large difference in the probability of individual damage is that MSA-based calculations are limited by the number of seismic waves exceeding the limit. In contrast, IDA-based calculations are based primarily on the mean and logarithmic standard deviation of the structural engineering demand parameter. However, their conclusions are largely in agreement, indicating that in the seismic analysis, the aqueduct structure will be difficult to maintain no damage, and it is very easy to exceed the damage index of the first limit state level. The IDA approach is very straightforward in theory in terms of calculation time, but it involves extensive calculations and complex data processing in practice. On the other hand, the MSA technique can provide the exceedance probability at any IM without complex work. In seismic calculations, IDA-based fragility analysis may fail to appropriately calculate the IM value corresponding to each limit state when the probability of the structure surpassing the damage index does not strictly increase with the IM positively. On the other hand, the MSA approach has no such flaws and can obtain multiple horizontal bands of IM-DM ground motion. The fragility curve is more precisely calculated using statistical analysis.  
Figure 12: Fragility curves (MSA).