esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Energy Management Systems
### Learning-Based Energy Management Systems
#### 4.3.2 Deep Q-Network  
As depicted in Figure 24, the Deep Q-Network (DQN) builds upon the foundation of Q-learning with several notable modifications [122]. Firstly, DQN innovatively replaces the traditional Q-table with an approximate value function process employing a neural network. This transformation effectively addresses the challenges associated with the dimensionality and discreteness of the state space encountered in conventional Q-learning. Secondly, the DQN provides an experience pool mechanism, which serves to mitigate the correlation among individual samples, thereby enhancing the stability and efficiency of the learning process. Lastly, to reduce oscillations in label values, the DQN maintains label  
Figure 23: Q-learning schematic diagram.  
Figure 22: Reinforcement learning schematic diagram.  
value stability over a specific timeframe by replicating a Q_target network, which closely mirrors the architecture of the original Q_network.
###### Abstract  
The objectives of this paper will be to discuss the issues related to the determination of the limits of detection (_LOD_) in laser-induced breakdown spectroscopy (_LIBS_) analytical applications. The derivation of the commonly used '3-sigma over slope' rule and its evolution towards the new official definition recently adopted by the International Union of Pure and Applied Chemistry (IUPAC) will be illustrated. Methods for extending the calculation of the _LOD_ to _LIBS_ multivariate analysis will also be discussed, using as an example the detection of Cu traces in cast iron samples by _LIBS_.  
LIBS; quantitative analysis; detection limit; _LOD_; copper; calibration curves; multivariate analysis; IUPAC
## 2 Optimization Model  
### Well Track Length  
Common calculation methods of two-dimensional borehole trajectory mainly include the arc model, catenary model, parabola model, cycloid model, etc. [32; 33; 34]. The arc line model assumes that the rate of deviation change remains unchanged on the track length, which is convenient for trajectory control. Therefore, it is assumed that the well track is composed of a straight line segment and an arc line segment.  
The arc line model of the build-up section is shown in Figure 1, and the calculation model is [35]  
\[\alpha_{2}=\alpha_{1}+k\Delta L=\alpha_{1}+\frac{180}{\pi}\cdot\frac{\Delta L} {R} \tag{1}\]  
\[\Delta H=R(\sin\alpha_{2}-\sin\alpha_{1}) \tag{2}\]  
\[\Delta S=R(\cos\alpha_{1}-\cos\alpha_{2}) \tag{3}\]  
where \(\Delta L\), \(\Delta H\), and \(\Delta S\) are, respectively, the well track length, vertical depth increase, and horizontal length increase of point \(B\) relative to the initial point \(A\) of deflecting, m. \(R\) is the radius of the build-up section. In order to reduce annular pressure loss and operation time of drilling before the target, the well track length before the target should be minimized. Then, the minimum objective function of the well track length can be expressed as follows:  
\[\text{min}L=\text{min}\sum_{i=1}^{n}L_{i} \tag{4}\]
## 2 Literature Review
### Comparison with Other Recent Research  
The PSO method is used for energy management of the hybrid system of an electric vehicle charging station [40]. In this paper, the PSO method of energy management has been developed, taking into account active power loss, reactive power loss operation cost, power flow, and voltage deviation. Although the research is applicable to smart grid applications, the primary function of the goal is to reduce operating costs. In the study [41], PSO was used to optimally coordinate the charging schedule of electric vehicles, considering the habits of users. In this paper, the function of the goal is to minimize power loss. The results show that coordinated charging can optimize the load and reduce energy network losses. Article [42] uses an improved PSO aiming to increase the energy efficiency of the ship. The microgrid model minimizes system costs and improves system management with proper scheduling. Research [43] based on the Swarm Exchange Particle Swarm Optimization Algorithm reconfigures the power grid of a circular power system with the aim of extending the lifespan of a ship's power station. The simulation model reduces the time required for reconfiguration and improves the performance of the ship's power system. As the ports are important consumers of electricity, their energy efficiency is attracting more and more attention. For ports to comply with increasingly stringent regulations, it is crucial to apply new technologies of renewable energy sources and alternative fuels with the aim of optimizing operations or, for example, reducing peak shavings [44]. In the research [45], a mixed linear programming model was proposed with the aim of energy management considering the uncertainty of energy generation through renewable sources. The study shows that by using a smart grid, significant economic savings are possible compared to conventional settings. In most research, the goal is to reduce economic costs. However, in this study, the goal is to reduce the peak power required for charging groups of ferries. Given the research gap, this research represents a novelty.
# Analytical and Measured Effects of Short and Heavy Rail Cars on Railway Bridges in the USA
## 3 Results of Case Studies
### Riveted Truss Spans
#### 3.1.2 Results from Test of Truss Bridge
Four strain gauges were also placed on the stringers of the 61-m (200-ft) span. Figure 14 shows the general location of these gauges. Figure 15 shows the comparison of stress time histories for 12.8-m (42-ft) short railcars and for 16.2-m (53-ft) common length railcars in Stringer S2. The typical peak stresses due to different railcar types on the stringers were calculated using the same methods as on the other bridge members. Figure 16 shows the comparisons for 12.8-m and 13.1-m (42- and 43-ft) short railcars and for 16.2-m (53-ft) common length railcars. The stringers behaved differently due to the short railcars than the other members of the bridge. For example, Stringer S2 experienced 15% less stress, and S3 experienced 10% less stress due to short railcars. This was related to the ratio of the railcar length to the stringer length that was previously discussed by [PERSON] et al. [19]. The stringers connected by floor beams developed partial continuity, which also affected the results. Figure 14: Gauge locations in stringers. Figure 13: Stress range calculated from measured strains, comparisons for 33.5-m (110-ft) and 61-m (200-foot) spans. Figure 15: Stress calculated from measured strains, comparison for stringer S2 southeast. The effect of railcar length on stringers was also visible when evaluating stress ranges. The stringers developed higher stress range cycles due to 16.2-m (53-ft) common length railcars than due to the 12.8-m and 13.1-m (42- and 43-ft) short railcars (Figure 17). The reported stress ranges for stringers were below the variable amplitude fatigue limit of 41 MPa (6 ksi). Therefore, fatigue was not a concern for stingers either due to the 16.2-m (53-ft) common length railcars or to the 12.8-m (42-ft) short railcars according to AREMA provision. However, the stress ranges due to longer cars were higher and were close to the limit of 41 MPa (6 ksi).
## 2 Literature Review
### Related Work
#### 2.2.1 Tabular Data Approaches  
A significant body of the literature is focused on statistical and machine learning approaches to study the factors associated with crash severity and predict crash severity levels. Both approaches significantly overlap since they deal with data analysis. A critical difference between statistical and machine learning approaches is that the former requires some assumptions about the predefined relations between the dependent and independent variables. In contrast, the latter does not require any previous assumptions [16]. Statistical models have been widely used since the 1980s and 1990s to model road accidents [17]. A common approach in these models is to use explanatory regressors such as driver information, weather, and road conditions, among others, as independent variables and crash severity as the dependent variable in binary (logit or probit) with two discrete outcomes [18; 19; 20] or multinomial models with multiple crash severity outcomes [21; 22]. Regression models are the most common statistical modeling techniques to measure the level of severity of road crashes [23; 24; 25]. Apart from statistical methods, machine learning algorithms are booming in the transportation area because of their ability to model complex nonlinear functions without the need to understand the underlying mechanism [16; 26]. Artificial neural networks are the most applied machine learning technique, followed by decision trees and support vector machines [27; 28; 29]. Also, Bayesian networks, random forests, boosting classifiers, and many other machine learning techniques have been used to model crash severity [30; 31; 32]. Deep learning approaches such as convolutional neural networks and recurrent neural networks have recently become widely used for prediction tasks in transportation research, demonstrating their outstanding performance [33; 34].  
Figure 1: Timeline for most common deep learning-based models for text embedding and classification models [11].
## 5 Pilot-scale and Farm-scale Plants for AD of RS
Similarly, [PERSON] et al. [127] investigated the performance of a full-scale biogas plant treating the MS. The digester had a capacity of 400 m\({}^{3}\) and was operated at a temperature ranging from 55 to 60 \({}^{\circ}\)C. They obtained a CH\({}_{4}\) yield of 200 L/kg TS. The abovementioned studies indicate the importance of pilot-scale experimentations to define the operational parameters such as heat input, TS, straw to wastewater ratio, digestion temperature, digestion time, loading frequency, and wastewater volume for the farm-scale plants treating RS. Although the literature reporting data on the AD of RS in farm-scale plants is scared. The implementation of a farm-scale plant using RS co-digested with other suitable substrates (PWW, manures) is possible as evidenced by studies that co-digestion performs better than mono-digestion. The farm-scale biogas plants are good candidates for the utilization of RS to recover energy, to facilitate the development of the agriculture economy and to provide local energy. However, further investigation is needed for a better understanding of the process. Moreover, reseach about the economical viability of farm-scale AD plants for RS should be conducted and published, covering all financial cost of capital and operation, including the logistics of RS and other co-substrates.
## Materials and Methods
### Methods
The second-order partial correlation analysis method can exclude the effect of other variables and analyze the correlation between the target variables [36]. Thus, the second-order partial correlation analysis can be used to eliminate the interference of other variables to determine the correlation between the remaining two variables. In this study, LST and ET were taken as control variables to analyze the correlation between NPP and precipitation. The correlation between NPP and LST was analyzed by controlling for precipitation and ET. The second-order partial correlation coefficient is calculated from the first-order partial correlation coefficient [37]. The correlation coefficient is calculated by the following formula:  
\[r_{xy}=\frac{\sum_{i=1}^{n}[(x_{i}-\overline{x})(y_{i}-\overline{y})]}{\sqrt{ \sum_{i=1}^{n}(x_{i}-\overline{x})^{2}\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}} \tag{7}\]  
Then, the first-order partial correlation coefficient is calculated by the following formula:  
\[r_{xy\text{-}1}=\frac{r_{xy}-r_{x1}r_{y1}}{\sqrt{1-r_{x1}^{2}}\sqrt{1-r_{y1}^{ 2}}} \tag{8}\]  
Finally, the second-order partial correlation coefficient is determined by the following formula:  
\[r_{xy\text{-}12}=\frac{r_{xy\text{-}1}-r_{x2\text{-}1}r_{y2\text{-}1}}{\sqrt{ 1-r_{x2\text{-}1}^{2}}\sqrt{1-r_{y2\text{-}1}^{2}}} \tag{9}\]  
where 1 and 2 are the control variables; \(r_{xy\text{-}12}\) is the partial correlation coefficient of \(x\) and \(y\) excluding variables 1 and 2; \(r_{x2\text{-}1},r_{y2\text{-}1}\) is the first-order partial correlation coefficient of \(x\), \(y\) and \(x\), 2 after excluding 1. The T-test is used to test the significance of the partial correlation coefficient and is expressed by the following formula [19]:  
Figure 1:0  
where \(r\) is the partial correlation coefficient, \(n\) is the sample number, and \(q\) is the number of independent variables. (5) Hurst index  
The Hurst index is an effective method to quantify the long-term dependence of time series information and is widely used in hydrology, economics, climatology, geology, and geochemistry [38; 39]. This study applied this method to predict the persistence of NPP trends into the future. The calculation steps are as follows:  
Figure 1:1  
Figure 1:2  
Figure 1:3  
Figure 1:4Figure 1:5  
where \(c\) is constant, the H value can be obtained by linear fitting with the least squares method to fit the equation \(\log(\mathrm{R/S})_{n}=\mathrm{a}+\mathrm{H}*\log(\mathrm{n})\). \(0<\mathrm{H}<0.5\) indicates that future changes in the time series will lead to a trend that is opposite of that in the past; \(0.5<H<1\) indicates that the time series will remain consistent with the past trend; \(H=0.5\) indicates a random event.
# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
## 1 Introduction
In conclusion, this paper aims to construct a hybrid degradation modeling and prognostic method for multi-modal systems, which combines the long-term cumulative degradation assessment and latest-stage degradation assessment, and takes into account changes in system capabilities to withstand damage under different aging conditions. The main contributions of this paper are as follows:  
(1) The cumulative dynamic physical difference feature is constructed to deal with the superposition effect of different operation modes, and gains the physical characteristics with the existence of mode switching, which is hidden in the inter-frame data. The health indicators are constructed based on the physical properties to improve the interpretability and robustness of a prediction model. The combination of a cumulative dynamic differential health indicator and the gated recurrent unit will further enhance the accuracy of cumulative degradation prediction. (2) During the latest stage of system operation, the system's ability to resist damage is usually the worst in its life time. The time window is used to extract local features, including statistical and energy features, which could mine aging information more fully to predict latest-stage degradation with the light gradient boosting machine. (3) The experiments on theoretical examples and the accessible turbofan aero-engine degradation dataset from NASA, and comparisons with the related model validate the effectiveness and superiority of the proposed method. The rest of this paper is organized as follows. The proposed model is detailed in Section 2. Section 3 evaluates the proposed model on the NASA degradation dataset and presents an analysis of the results. The conclusion and prospects are given in Section 4.
## 2 System Description and the Soft-Failure Process Analysis of Self-Recovery Products  
### System Description  
The life cycle of a product comprises the storage period and working period. To ensure the universality of the model, the effects of the impact load and continuous degradation are considered together. Product performance degrades when the product is used continuously. Product failure occurs when degradation \(X_{s}(t)\) reaches the certain degree of soft-failure threshold \(D\) or the magnitude of external shock is greater than hard failure threshold \(H\). Degradation volume accumulates during the storage period. The basic concepts assumed in this study are as follows:  
1. In a whole life cycle, the product has a storage phase \((0,T_{1})\) and two work stages, \((T_{1},T_{2})\) and \((T_{2},\infty)\). On the basis of the type of mathematical models used to describe the degradation processes, the three phases are the first and second Wiener processes, and the gamma process.
2. Provided that the shock process follows a homogeneous Poisson process with intensity \(\lambda\), the number of arrived shocks by time \(t\) is \(N(t)=n\). It was assumed that there was no shock in the first Wiener process.
3. The degradation comprises continuous degradation and degradation increments caused by shocks. Self-recovery occurs when shock interval \(B\) is greater than self-recovery threshold \(\tau\). No shocks occur during the self-recovery process; therefore, there is no additional non-stationary interference to the degradation processes. In this situation, the gamma process is adequate to describe the third-phase degradation.
4. The value range \([c_{1},c_{2}]\) of cumulative degradation \(c\) during the storage period is determined by analyzing the actual object, where \(c_{2}\) denotes the upper limit of the cumulative degradation allowed in the storage stage, and \(c_{1}\) is the minimal cumulative degradation allowed in the storage stage.
5. The \(i\)\(th\) shock follows a homogeneous Poisson process: \(W_{i}\sim(\mu_{W},\sigma_{w}^{2})\). The corresponding degradation increment caused by \(W_{i}\) is denoted as \(Y_{0i}\sim N(\mu_{3},\sigma_{3}^{2})\). The dependent competing failure processes of a product are shown in Figure 1.
# Using Zigzag Persistent Homology to Detect Hopf Bifurcations in Dynamical Systems
## 2 Materials and Methods
### Homology and Persistent Homology  
Homology is a tool from the field of algebraic topology that encodes information about shape in various dimensions. Zero-dimensional homology studies connected components, 1-dimensional homology studies loops, and 2-dimensional homology studies voids. Persistent homology is a method from TDA which studies the changing homology of a parameterized space.  
For the purposes of this paper, we will focus on persistent homology applied to point cloud data. Here, we need only assume that a point cloud is a collection of points with a notion of distance; however, in practice, this distance often arises from a point cloud in Euclidean space inheriting the ambient metric. Given a collection of points, we will build connections between points based on their distance. Specifically, we will build simplicial complexes, which are spaces built from different dimensional simplices. A 0-simplex is a vertex, a 1-simplex is an edge, a 2-simplex is a triangle, and in general, a \(p\)-simplex is the convex hull of \(p+1\) affinely independent vertices. Abstractly, a \(p\)-simplex can be represented by the set of \(p+1\) vertices from which it is built. Therefore, a simplicial complex, \(\mathcal{K}\), is a family of sets that is closed under taking subsets. That is, given a \(p\)-simplex, \(\sigma\), in \(\mathcal{K}\), any simplex consisting of a subset of the vertices of size \(0<k\leq p\), called a \(k\)-dimensional face of \(\sigma\), is also in \(\mathcal{K}\).  
To create a simplicial complex from a point cloud, we use the Vietoris-Rips complex (sometimes just called the Rips complex). Given a point cloud, \(X\), and a distance value, \(r\), the Vietoris-Rips complex, \(\mathcal{K}_{r}\), consists of all simplices for which vertices have a maximum pairwise distance at most \(r\). Taking a range of distance values, \(r_{0}\leq r_{1}\leq r_{2}\leq\cdots\leq r_{n}\) gives a set of simplicial complexes, \(\{\mathcal{K}_{r_{i}}\}\). Since the distance values strictly increase, we have a nested sequence of simplicial complexes  
\[\mathcal{K}_{r_{0}}\subseteq\mathcal{K}_{r_{1}}\subseteq\cdots\subseteq \mathcal{K}_{r_{n}} \tag{1}\]  
called a filtration. Computing \(p\)-dimensional homology, \(H_{p}(\mathcal{K})\), for each complex in the filtration gives a sequence of vector spaces and linear maps,  
\[H_{p}(\mathcal{K}_{r_{0}})\to H_{p}(\mathcal{K}_{r_{1}})\to\cdots\to H_{p}( \mathcal{K}_{r_{n}}). \tag{2}\]  
Persistent homology tracks how homological features such as connected components and loops change through the filtration. Specifically, it records at what distance value a feature first appears and when a feature disappears or connects with another feature. These distance values are called the \"birth\" and \"death\" times, respectively. These birth and death times are represented as a persistence diagram, which is a multiset of the birth death pairs \(\{(b,d)\}\).
## 3 Data Collection  
The dataset, sourced from the commercial provider Intelematics [62], covered a turbulent period from June 2020 to May 2021, which included various pandemic-related mobility restrictions and lockdowns. The data were collected from all sections of Hoddle Street, mainly at traffic signals at intersections but also leveraging multiple other fixed and non-fixed sources such as inductive loops, sensors, cameras, and in-vehicle trackers for collection and validation. However, the main locations were at signalised intersections in Melbourne, Australia. Intelematics provided pre-configured, high-quality data through its INSIGHT platform. This thorough data validation approach contributed to ensuring the data's quality and reliability for the forecasting models.
# Enhancing Backtracking Search Algorithm using Reflection Mutation Strategy Based on Sine Cosine
## 3 The Proposed Algorithm
### The Framework of The Proposed Algorithm  
Based on above description, the framework of the proposed RSCBSA is similar to that of BSA, but the difference is that mutation and crossover stages are replaced with reflection mutation based on sine cosine and crossover operator. The framework of RSCBSA is presented in Algorithm 1.  
```
1 Initiate population size \(N\) and maximum number of iterations \(MaxGen\);
2 Initialize the current population \(P\) and historical population \(oldP\) using Equations (1) and (2), respectively;
3 Compute the fitness value of all individuals in current population \(P\);
4\(gen\gets 1\);
5 whilegen \(<MaxGen\)do
6 Perform selection-I using Equations (3) and (4) to form the historical population \(oldP\);
7 Find the best individual from the current population;
8 fori=1 to N do do
9 Randomly select four different individuals form historical population \(oldP\) or current population \(P\) to construct a unit simplex ;
10 Compute the center of the unit simplex using Equation (10);
11 Perform Reflection mutation strategy based on sine cosine using Equations (11) and (12) to generate the trial individual \(V_{i}\);
12 Perform crossover operator using Equation (13);
13\(\backslash\) Boundary control mechanism;
14 forj=1 to D do
15 if\(V_{i,j}<low_{j}\) or \(V_{i,j}>up_{j}\)then
16\(V_{i,j}=low_{j}+rand\cdot(up_{j}-low_{j})\)
17
18 end if
19 Evaluate the trial individual \(V_{i}\); Perform Selection-II using Equation (7) to save the global best individual;
20 end for
21\(gen\gets gen+1\);
22
23 end for
24 Output the best individual
```  
**Algorithm 1**Framework of the Proposed Algorithm
# An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
## 2 ISF Coefficients Quantization in AMR-WB  
In a quantization process of AMR-WB [1], a speech frame of 20 ms is firstly applied to evaluate linear predictive coefficients (LPCs), which are then converted into ISF coefficients. Subsequently, quantized ISF coefficients are obtained following a VQ encoding process, which is detailed below.