esa-sceva/satcom-chunk-collection huggingface dataset:
# Fluid Net Models: From Behavioral Properties to Structural Objects
## 2 Basic Concepts and Notations
### Fluid or Continuous Petri Net System
Define:_  
\[N_{\Sigma}=\langle P,T,\mathbf{Pre}_{\Sigma},\mathbf{Post}\rangle\]  
_such that \(\mathbf{Pre}_{\Sigma}[p,t]=0\) if \(\mathbf{Pre}[p,t]=0\), and \(\mathbf{Pre}_{\Sigma}[p,t]=\Sigma_{p^{\prime}\in\mathbf{\ast}}\mathbf{Post}[p^{\prime},t]\) otherwise._  
_A set \(\Sigma\subseteq P\) is a siphon of \(N\) iff \(\mathbf{y}\geq\mathbf{0}\) exists such that \(\|\mathbf{y}\|=\Sigma\) and \(\mathbf{y}^{\mathsf{T}}\mathbf{C}_{\Sigma}\leq\mathbf{0}\)._  
Algorithm 2 decides if the net is consistent and there are no empty siphons at \(\mathbf{m}_{0}\). For this second condition, it computes a generating family of siphons for \(N\). Then, it evaluates if there is an empty siphon at the initial marking. ```
0:\(\langle N,m_{0}\rangle\). 0: If \(N\) is consistent and there are no empty siphons
1: Compute the token-flow matrix \(\mathbf{C}=\mathbf{Post}-\mathbf{Pre}\)
2: Compute the existence of a positive T-semiflow \(\mathcal{P}_{2-1}:\{\text{Find }\mathbf{x}\geq\mathbf{1}\text{ s.t. }\mathbf{C}\mathbf{x}=\mathbf{0}\}\)
3:if\(\mathcal{P}_{2-1}\) has no solution then
4:\(N\) is not consistent
5:else
6:\(N\) is consistent
7: Construct \(N_{\Sigma}\) for \(N\) as in Theorem 1
8: Find an unmarked siphon: \(\mathcal{P}_{2-2}:\{\text{Find }\mathbf{y}\geq\mathbf{0}\text{ s.t. }\mathbf{y}^{T}\mathbf{C}_{\Sigma}\leq\mathbf{0}\wedge\mathbf{y}^{T}\mathbf{m}_{0}=\mathbf{0}\}\)
9:if\(\mathcal{P}_{2-2}\) has a solution then
10: There is at least one empty siphon at \(\mathbf{m}_{0}\)
11:else
12: There are no empty siphons at \(\mathbf{m}_{0}\)
13:endif
14:endif
```  
**Algorithm 2** Computing if the net is consistent and there are no empty siphons at \(\mathbf{m}_{0}\).
# Low-Latency Bit-Accurate Architecture for Configurable Precision Floating-Point Division
## 5 Results and Comparisons
### Related Work and Comparisons
It must be pointed out that the comparison is done in terms of the latency and total time without taking into account that different processors might run at different technologies and frequencies. Moreover, since the classic processors are usually based on pipeline architecture, their clock speeds are high to a considerable extent. However, the design of the proposed architecture in this paper does not use pipeline architecture as it is only an infrequent unit in computer arithmetic, leading to a bit low clock speed. If our architecture adopts pipeline architecture, clock speed of our architecture is predicted to double at least, which we may study in the future. \begin{table}
\begin{tabular}{c c c c} \hline  & [58] **(with 1-Stage** & [58] **(with 2-Stage** & **This Paper** \\  & **Multiplier)** & **Multiplier)** & **This Paper** \\ \hline Subnormal & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline Tech. & 90 nm & 90 nm & 90 nm \\ \hline Latency (cycle) & 11 (61.1\%) & 18 (100\%) & 4 (22.2\%) \\ \hline Period (ns) & 1.72 (175.5\%) & 0.98 (100\%) & 3.3 (336.7\%) \\ \hline Power (mW) & 30.9 (48.1\%) & 64.21 (100\%) & 58.53 (91.1\%) \\ \hline Total time (ns) & 18.92 (107.3\%) & 17.64 (100\%) & 13.2 (74.82\%) \\ \hline Total energy (fJ) & 584.65 (51.6\%) & 1132.74 (100\%) & 772.6 (68.2\%) \\ \hline Efficient average energy (fJ/bit) 1 & 11.24 (51.6\%) & 21.78 (100\%) & 12.07 (55.4\%) \\ \hline \end{tabular} 1  
\end{table} 
Table 5: Comparison of Predict–Correct Division Architecture On DP. Most of the designs in Table 6 use a multiplicative division algorithm or a radix-16/8/4 digit-recurrence algorithm. The Intel Penryn processor [59] implements a radix-16 combined division unit by cascading two radix-4 iterations every cycle. Consequently, the latency is almost halved with respect to that of the radix-4 unit. The IBM z13 processor [61] has a divide unit supporting SP, DP, QP, and all the hexadecimal FP data types. The underlying algorithm is a radix-8 division generating 3 bits per cycle. The major challenge was to perform a radix-8 divide step on a wide QP mantissa, 113 bits plus some extra rounding bits, and fit it in a single cycle. As shown in Table 6, our proposal obtains much lower latencies. The multiplicative implementation is limited by the latency of the multiplier of multiply-and-accumulate units. On the other hand, the implementation in [60] uses a very low radix, which implies a high number of iterations, although its implementation is quite simple. As for total time of SP or DP, owing to low clock speed, the performance of our proposal is in the middle of the classical processors.
## 1 Introduction
### Japanese Code  
A steel arch bridge can be approximately regarded as a narrow and long-span structure system with a tendency to buckle laterally away from the arch plane, so the Japanese code establishes rules and regulations for verification of out-of-plane buckling. If the arch axis forms a symmetrical parabola in the vertical plane, and if lateral bracing and sway bracing are installed on the basis of provisions presented in Japanese code, the verification of out-of-plane buckling can be performed using the following equation:  
\[H/A_{g}\leq 0.85\sigma_{ca} \tag{5}\]  
The meaning of symbol \(H\) can be seen in Figure 3; it represents the horizontal component of the axial force of the arch rib under load. \(A_{g}\) is the mean value of the gross cross-sectional area of the members of a single arch, \(\sigma_{ca}\) is the allowable axial compressive stress at the position of 1/4 span of a single arch rib. \(\sigma_{ca}\) is only relevant to slenderness ratio \(l/r\), and can be calculated according to some equations in tables of specifications when the local bucking of the arch rib is not taken into consideration. In this regard, the calculation method of the radius of rotation and the effective buckling length is shown in Equation (6).  
\[r=\sqrt{\left(I_{y}+A_{g}\left(\frac{b}{2}\right)^{2}\right)/A_{g}},\ l=\phi \beta_{z}L \tag{6}\]  
In this equation, \(b\) is the spacing between the arch ribs, \(L\) represents the bridge span, the mean value of the moment of inertia around the vertical axis of the members of a single arch rib is represented by the symbol \(I_{y}\), and \(\beta_{z}\) takes the values as shown in Table 1. When the value of \(f/L\) falls between values given in Table 1, \(\beta_{z}\) may be interpolated in a linear manner. Here, \(f\) is the rise of the arch. Values of \(\varphi\) are specified as follows: for a midheight-deck stiffened arch \(\varphi=1\), for an upper-deck stiffened arch \(\varphi=1+0.45\ k\), and for a through stiffened arch \(\varphi=1-0.35\ k\). Here, \(k\) is the ratio of the load shared by the hangers or shoring to the total load in the loading state shown in Figure 3. While considering the upper-deck stiffened arch, the value of \(k\) should be set at 1 if the arch and stiffening girder are not rigidly lined at the arch crow. \(P_{1}\), \(P_{2}\), and \(w\) in Figure 3 are the vehicle load, lane load, and dead load acting on the main structure, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Section**} & \multicolumn{5}{c}{**Rise Ratio \(f/L\)**} \\ \cline{2-6}  & **0.05** & **0.10** & **0.20** & **0.30** & **0.40** \\ \hline \(I_{y}=\text{constant}\) & 0.50 & 0.54 & 0.65 & 0.82 & 1.07 \\ \(I_{y}=I_{y,c}/\text{cos}\varphi_{\text{m}}\) & 0.50 & 0.52 & 0.59 & 0.71 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Value of \(\beta_{z}\).  
Figure 2: Simplified Calculation Model of Arch Rib: (**a**) Simplified Model of Arch Rib; (**b**) Equivalent Vierendeel Truss.
## 5 Discussion
### Analysis of the 3 BL Results  
The weights of the indicators and scenarios seem to be uniform due to the small number of expert opinions that were gathered and the limited number of criteria that were being compared in each matrix (for example, the matrices for the pairwise comparisons of indicators were two by two, while those for the scenarios were three by three). Averaging scores for pairwise comparisons from a larger pool of people and increasing the number of indicators can be easily accommodated by this framework and may increase the accuracy of the model for future studies.  
From an environmental lens, the country's reliance on fossil fuels for its energy supply further exacerbates environmental quality degradation, which agrees with the study of [61]. The new development of coal-fired power plants in Victoria is at a halt with existing facilities being retired; thus, there are no expected capital costs. On the other hand, the operating costs of these facilities appear to be less competitive than the RE and EE technologies that were introduced in this paper. It can also be observed that there may be external costs that are related to GHG emissions, which are likely to further reduce the BAU model's environmental and economic performance. While these were not included in the analysis under economic indicators, their impact spills over and was considered in the evaluation of the social indicators. The expert judgment on the generally high social acceptability of renewable and energy efficiency technologies compared to BAU was well aligned with previously published studies [62; 63; 64; 65].  
The 3 BL results confirmed that BAU is not a sustainable pathway for communities due to its poor performance in all 3 BL metrics. The FB goals would not be satisfied and Melbourne's sustainability targets would not be achieved if BAU were to be continuously employed. It can observed be that the results were in good agreement with Australia's ongoing shift towards renewable energy sources and it slowly moving away from the use of BAU technologies due to their unsustainability in the long run. BAU's bottom ranking in the 3 BL analysis could be explained by the increasing uptake of renewable technologies and decreasing development of coal-fired power plants [66; 67]. The current trend provides more competitiveness and accessibility for renewable and energy-efficient technologies.  
While the results of the 3 BL analysis in this case study might not have necessarily reflected the most competitive technology in reality, the approach that was introduced  
Figure 5: The mapping process.  
here enabled us to quantitatively compare technologies in the early stages of their design. While the number of both indicators and technologies were limited here, the approach can easily be scaled up to accommodate more in order to improve the accuracy. Similarly, the pairwise comparisons can be scaled up and/or passed on to decision makers to quantify those that would otherwise remain subjective opinions.
# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
## 2 Fault Detection and Isolation Scheme
### Offline Model Training
#### 2.1.3 Residual Evaluation and Threshold Calculation  
With the data under a healthy condition, a health reference model can be trained. Then, we can calculate the multivariate residuals E between the input variables X and the reconstructed outputs \(\ \widetilde{\text{X}}\) as follows:  
\[\text{E}=\ \widetilde{\text{X}}\ -\text{X} \tag{5}\]  
To detect the existence of a fault, an integrated monitoring indicator needs to be calculated based on the multivariate residuals. The Euclidean distance or the Mahalanobis distance (MD) are typically used. The MD is a unitless distance measurement, and takes into account the correlations among  
Figure 2: Sparse autoencoder structure.  
variables. The was proposed by Indian statistician [PERSON] to represent the covariance distance of the data. The MD provides a univariate distance value for a multivariate data and is therefore applied in the anomaly detection models. In the past, MD was successfully applied for the anomaly detection in wind turbine data [6; 30; 31].  
In this paper, a robust MD [6; 32] is employed, as shown in Equation (6).  
\[h=\sqrt{(\mathbb{E}-\hat{\mu})MCD^{-1}(\mathbb{E}-\hat{\mu})^{\mathsf{T}}} \tag{6}\]  
Next, the fault detection threshold \(d\) is determined using the statistical properties of \(h\). In other words, the fault detection threshold \(d\) can be obtained from the probability density function (PDF) of \(h\) for a given confidence level \(\alpha\) by solving Equation (7):  
\[P(h>d)=\int_{-\infty}^{d}p(h)dh=\alpha \tag{7}\]  
where \(p(h)\) is the PDF function of \(h\). In this paper, the kernel density estimation (KDE) method is adopted for distribution fitting. The KDE method is a well-established approach in statistical distribution fitting and has been successfully applied to the field of processing monitoring and fault detection [6]. Therefore, according to the KDE method, \(p(h)\) can be written as:  
\[p(h)=\frac{1}{N\alpha}\sum_{i=1}^{N}K(\frac{h-h_{i}}{\sigma}) \tag{8}\]  
where \(N\) is the total number of \(h\). \(K(\cdot)\) is the kernel function and \(\sigma\) is the bandwidth. The selection of optimal value for \(\sigma\) is described in [33]. Here, the Gaussian kernel is used.
## 3 Primary Controller Design
### Outer Controller Design and Constraints  
Most of the controllers shown in Figures 5 and 6 have been based on a pure integrator controller [11]. This type of controller is of special interest since some of the transfer functions presented in Table 1 are mainly of a constant type or first order leading to the first-order dynamic behavior of the regulated system. The gain of the integrator controller can be obtained looking at the closed-loop pole of a desired first-order transfer function as follows:  
\[K_{i}=\frac{1}{G(0)\tau_{d}} \tag{11}\]  
where \(G(0)\) is the static gain of the transfer function and \(\tau_{d}\) is the desired constant time.  
For both \(C_{ccj}(s)\) and \(C_{cvj}(s)\) controllers of the charging mode and for the \(C_{dcb}(s)\) controller cancelling the SESS current, a settling time of 5 s has been considered. In contrast, a settling time of 100 ms has been designed for the power injection controller \(C_{ds}(s)\). With respect to the dc bus voltage regulation, a value of \(C_{bus}(s)\) of 2.2 F is assumed in order to reduce the impact of the power demand of the ChPs when an EV arrives to the station. Thus, the controller for regulating the voltage bus \(C_{dss}(s)\) has been designed in the frequency domain with a phase margin of 74.2\({}^{\circ}\) obtaining a settling time of 10 ms and an overshoot of 10%. Table 2 summarizes the outer converter transfer functions with the same nomenclature used in Table 1.  
Figure 6: Power sharing control loop implemented for power injection and voltage regulation modes.
# Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles
## 3 Model
### Description of Quadcopter Missions  
The proposed energy model of Section 3.1 uses component specific parameters to infer the power consumption at each point in time.  
**Definition 4** (Mission).: _The mission is a total function \(M(t):\mathcal{T}\rightarrow\mathcal{P}\) which maps each time point \(t\) of the mission time frame \([0,T_{end}]\in\mathbb{R}^{+}\) to a parameter vector \(\vec{p}\) of the global parameter space. We assume this function to be deterministic, non-surjective and non-injective._  
The mission function \(M(t)\) can be developed with the help of a set of help functions. Each helper function \(f_{i}(t):\mathcal{T}\rightarrow\mathcal{F}_{i}\) computes at least one parameter from the parameter space \(\mathcal{F}_{i}\subset\mathcal{P}\). The resulting mission function is the correctly ordered concatenation of the helper functions' output.  
\[M(t)=(f_{1}(t),f_{2}(t),...f_{m}(t)) \tag{1}\]  
To enable the energy model to be used in different missions, each mission needs to be translated to a time-series of parameter vectors to enable an energy consumption estimation at each point of time of the mission. A specific quadcopter mission defines the parameter vector of a quadcopter at each point in time during the mission time frame. This modelling is especially helpful if independent behaviour of the components of the copter needs to be modelled, like (de)activation of components. Typical missionhelper function are step functions for components, which can be activated or deactivated or linear algebra equations in homogeneous 3D-coordinates to represent motion.
## 4 Mechanical Test Results and Discussion
### Tensile Behavior
#### 4.2.2 Tensile Strength and Strain Capacity
It was noted that the increase in the dosage of the mineral additives resulted in greater tensile strength for the 28 d cured specimens in contrast with the cases of the 7 d cured specimens. As introduced previously, the tensile strength of specimens B7, D7, F7, and H7 consistently decreased with an increase in the dosage of the mineral additives. The tensile strength of specimens C7, F2, G7, and I7 were 6%, 25%, 22%, and 9% lower than those of specimens B7, D7, F7, and H7, respectively. On the contrary, in the case of specimens B28, D28, F28, and H28, the tensile strength increased by 20%, 11%, 5%, and 0% for specimens C28, E28, G28, and I28, respectively. The strain capacities, meanwhile, consistently decreased as the dosage of substituted OPC increased for both 7 d and 28 d cured specimens. The degrees of decrease, according to the increases in contents of substituted OPC, however, were lower for the 28 d cured specimens. The strain capacities of specimens B7, D7, F7, and H7 changed by 105%, \(-\)10%, \(-\)39%, and \(-\)31% for specimens C7, E7, G7, and I7, respectively. These rates for specimens B28, D28, F28, and H28 were 11%, \(-\)5%, \(-\)17%, and 2%, respectively.
# ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
###### Abstract  
Since the beginning of this century, the share of renewables in Europe's total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more than 5%; nowadays, Germany, Italy, and Spain account together for almost 70% of total European PV generation. In this context, the so-called _day-ahead electricity market_ represents a key trading platform, where prices and exchanged hourly quantities of energy are defined 24 h in advance. Thus, PV power forecasting in an _open energy market_ can greatly benefit from machine learning techniques. In this study, the authors propose a general procedure to set up the main parameters of hybrid artificial neural networks (ANNs) in terms of the number of neurons, layout, and multiple trials. Numerical simulations on real PV plant data are performed, to assess the effectiveness of the proposed methodology on the basis of statistical indexes, and to optimize the forecasting network performance.  
artificial neural network; day-ahead forecast; ensemble methods +
Footnote †: journal: Applied sciences  
_Article_  
ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
# An Experimental Study on the Migration of Pb in the Groundwater Table Fluctuation Zone
## 4 Conclusions  
* In the adsorption and desorption kinetic experiments for fine, medium, and coarse sand, the maximum adsorption capacity of Pb\({}^{2+}\) was 2367 mg\(\cdot\)kg\({}^{-1}\), 1848 mg\(\cdot\)kg\({}^{-1}\), and 1544 mg\(\cdot\)kg\({}^{-1}\), respectively. The maximum desorption capacity of Pb\({}^{2+}\) was 29.18 mg\(\cdot\)kg\({}^{-1}\), 62.38 mg\(\cdot\)kg\({}^{-1}\), and 81.60 mg\(\cdot\)kg\({}^{-1}\), respectively. For adsorption and adsorption experiments at different pH levels, the adsorption capacity of Pb\({}^{2+}\) gradually increased and the desorption capacity gradually decreased with increasing pH in the sample solution at pH 4.0 and above. In these experiments, the desorption capacity held steady with an increase in pH from 8.0 to 9.0;
* In the water table fluctuation experiments, the pH detected in the sample solution varied with the water table fluctuation. The location was further away from the origin pollution we set, resulting in decreased pH in the sample solution. The minimum pH was consistent with the original pollution. At the same sample port, this universally followed the order of the medium's average pH: fine sand \(>\) medium sand \(>\) coarse sand. The Pb\({}^{2+}\) concentration in the sample solution varies with time and water table fluctuations. Pb concentration in the medium results in Pb\({}^{2+}\) forming desorption products in the solution that adsorb the experimental medium surfaces once again;  
Figure 8: The variation of Pb concentration in the medium at different heights after experiments.  
* The migration of Pb\({}^{2+}\) is affected by pH in the solution, water table fluctuations, and the adsorption capacity of the medium. Water table fluctuations affect the desorption of Pb in the polluted medium. Consequently, the Pb\({}^{2+}\) concentration in the solution changes. Then, the pH variation results in the adsorption and desorption capacities of Pb\({}^{2+}\) changing as well;
* The effects of Pb's migration on groundwater table fluctuation zones are due to various factors. In this article, we only considered the water table, pH, and adsorption and desorption capacities in our analysis, which are the deficiencies of this work. Other meaningful factors will be added for future studies, such as dissolved oxygen, redox potential, etc.  
Conceptualization, J.Q.; methodology, T.Y.; validation, Y.Z.; formal analysis, Y.L.; investigation, R.T.; resources, W.G.; data curation, J.J.; writing--original draft preparation, J.Q.; writing--review and editing, J.Q. and T.Y.; funding acquisition, J.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D and Promotion Projects in Henan Province (202102310012), the Doctoral Research Fund of North China University of Water Resources and Electric Power (40651), and the Key Project of Science and Technology Research of Henan Education Department (14A170006).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.