esa-sceva/satcom-chunk-collection huggingface dataset:
# An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming
## 3 Proposed NDP for Parallel HEVs
### NDP Implementation Procedure  
The NDP implementation procedure is presented as follows:1. Select an appropriate structure for the critic network and action network separately, which means determining the number of hidden layers. In this design, the number of hidden layers of the critic network and action network is 30 and 11, respectively.
2. Initialize the weights and parameters of both networks according to a rule-based EMS. Initialize the output of the critic network, the maximum training error of critic network \(E_{c}\), the maximum of the norm for weights of action network variation \(\varepsilon\), and maximum times for training both networks, \(N_{c}\) and \(N_{a}\). In this research, the maximum training times for the action network and critic network are all 50.
3. Set the whole iteration time \(i=0\) and \(L(x(0),u(0))=0\).
4. Set \(i=i+1\). Calculate the current control vector \(u(i)\).
5. Set the iteration time of critic network to \(m=0\).
6. Set \(m=m+1\). Calculate the error of critic network \(e_{c}^{m}(i)\) and \(E_{c}^{m}(i)\) according to Equations (32) and (33). Update the weights and parameters of the critic network according to Equations (13) and (14);
7. When \(m\geq N_{c}\) or \(E_{c}^{m}\leq E_{c}\), save the weights and parameters of the critic network as the weights and parameters at time step \(k\), and proceed to step 8. Otherwise, return to step 6.
8. Set the iteration time of action network to \(m=0\).
9. Set \(m=m+1\). Calculate the error of action network \(e_{a}^{m}(i)\) and \(E_{a}^{m}(i)\) according to Equations (34) and (35). Update the weights and parameters of the action network according to Equations (13) and (14);
10. When \(m\geq N_{a}\) or \(E_{a}^{m}\leq E_{a}\), save the weights and parameters of the critic network as the weights and parameters at time step \(k\), and proceed to step 11. Otherwise, return to step 9.
11. When the norm of weights for action network variation is less than \(\varepsilon\), save the weights of both networks as the weights at the current time step; otherwise, return to step 4.
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 2 Materials and Methods
### The Method Proposed in This Paper
#### 2.4.1 Network Structure  
A one-stage plant disease detection method based on MFF-CNN is proposed based on YoLov5s with a one-stage detector, as shown in Figure 5. The MFF-CNN consists of three parts, i.e., the backbone, neck, and detection head.  
The backbone is based on CSP Darknet53 and mainly uses the Conv module, CBL module, and CSP1_X module to obtain the disease information characteristics of maize leaves.  
The Conv module consists of a convolutional layer, a batch normalization operation, and a SiLU activation function. Its kernel size is 3 \(\times\) 3, and the step size is 2. The CBL module is similar to the Conv module in that it also uses convolutional layers with batch normalization operations, except that it uses Leaky ReLU as the activation function.  
The MFF-CNN uses two different CSP structures. The CSP1-X with residual components (X bottlenecks) in the backbone is shown in Figure 6, while the neck uses convolutional layers (X CBLs) instead of residual components, shown as CSP2-X in Figure 7. The  
Figure 4: Activity diagram of the proposed network.  
Figure 5: The structure of the one-stage maize leaf detection network based on multi-scale feature fusion.  
cross-layer design of the CSP reduces computation, improves inference speed, reduces memory cost, and guarantees accuracy.  
The MFF-CNN model also adds the coordinate attention (CA) attention module (see Section 2.4.2) and the spatial pyramid pooling (SSP) improvement module (see Section 2.4.3) to the backbone.
## 3 Preliminaries  
### CS Theory  
CS is a novel technique that is used to compress and recover an original signal when it has sparse representation in some domains. As above, we consider the sparse signal \(X\in\mathbb{R}^{N}\), describing the sensor node readings in WSNs with \(N\) nodes. is represented as follows:  
\[X=\Psi S=\sum_{i=1}^{N}\psi_{i}s_{i}, \tag{1}\]  
where \(\Psi=[\psi_{1},\psi_{2},\ldots,\psi_{N}]\in\mathbb{R}^{N\times N}\) is some basis matrix, and \(S\in\mathbb{R}^{N}\) is coefficient vector. When can be represented as a linear combination of \(K\) column vectors of \(\Psi\) with \(K\ll N\), it means that the signal is \(K\)-sparse, which demonstrates that it has only \(K\) nonzero components or \((N-K)\) smallest components can be ignored. In this case, \(\Psi\) is referred to as the sparse basis. Thus, the information can be compressed by simple linear projection. This projection matrix \(\Phi\in\mathbb{R}^{M\times N}\) is an \(M\times N\) measurement matrix, \(Y\in\mathbb{R}^{N}\) is the measurement vector, and \(K<M<N\). The compressed measurements can be described as  
\[Y=\Phi\mathbf{x}=\Phi\Psi S=AS, \tag{2}\]  
where \(A\) denotes the equivalent sensing matrix.  
The measurement matrix needs to satisfy the RIP conditions [24].  
**Definition 1**: _(RIP [10; 11]): A matrix \(\Phi\) satisfies the restricted isometric property of order \(K\) if there exists a \(\delta_{K}\in(0,1)\) such that_  
\[(1-\delta_{K})\|\mathbf{x}\|_{2}^{2}\leq\|\Phi\mathbf{x}\|_{2}^{2}\leq(1+ \delta_{K})\|\mathbf{x}\|_{2}^{2} \tag{3}\]  
_for all \(K\)-sparse vectors \(S\in\mathbb{R}^{N}\)._  
However, the reconstruction of the original signal \(x\) from \(y\) is a problem. [PERSON] and [PERSON] [9] and [PERSON] [10] have shown that signal \(X\) can be reconstructed via \(\ell_{1}\) as  
\[\overset{\wedge}{\mathrm{x}}=\underset{x}{\mathrm{argmin}}\|\mathbf{x}\|_{1} \ \ s.t.\ \mathrm{Y}=\Phi X, \tag{4}\]  
where \(\Phi\) satisfies RIP and \(M\geq O(K\cdot\log(N/K))\), \(X\) can be recovered successfully with high probability. In addition, there are a large number of reconstruction algorithms such as the basis pursuit (BP) algorithm [24], orthogonal matching pursuit (OMP) algorithm [25], CosaMP [26], Stage-wise Orthogonal Matching Pursuit (StOMP) [27], gOMP [28], and so on.
## 4 Numerical Examples
### Discussion of the Proposed Method  
It can be concluded that the proposed method generally has higher efficiency and at least equivalent accuracy compared to the NRBM. However, the proposed method still has a major drawback, i.e., it can only identify most of the redundant failure modes instead of all them. For example, for a subsystem with four failure modes, the proposed method cannot identify the redundant failure mode when its corresponding set satisfies \(A_{i}\in\overset{4}{\cup}\overset{4}{\cup}A_{j},A_{i}\
otin A_{j},A_{i}\
otin A _{j}\cup A_{k}(i\
eq j\
eq k,k=1,2,3,4)\). This drawback inevitably reduces the efficiency of the proposed method. Therefore, it appears to indicate that other efficient techniques are required for the identification of redundant failure modes.  
It should be noted that the non-probabilistic convex model represents a series of convex models instead of a single model, and the interval model has also been widely used to quantify the uncertainty; the latter needs to be further considered to widen the applicability of the proposed method. On the one hand, the interval model utilizes a multidimensional box to describe the uncertainty domain, and the redundant failure modes are also more likely to occur. This implies that the proposed method is also suitable for the interval model only after some minor modifications. On the other hand, the multidimensional box and the multidimensional ellipsoid have different geometric shapes and properties, implying that the identification criteria of the proposed method need to be modified for the interval model. However, a more detailed modification of the interval model is beyond the scope of this paper.
## 3 Experimental Results and Discussion
### Damage Model of Stress-Strain Relationship
#### 3.6.2 Analytical Model of CA Mortar  
In order to develop an analytical constitutive model for CA mortar capable of characterizing the stochastic behaviors, the expressions for the mean and STD. of the damage variable \(d\) should be initially determined. By observing the calculated results illustrated in Figure 10, a logistic type function was adopted to depict the mean of the damage variable \(\mu_{d}(\varepsilon)\):  
\[\mu_{d}(\varepsilon)=\frac{A_{1}-A_{2}}{1+(\frac{\varepsilon}{\varepsilon_{0} })^{p}}+A_{2} \tag{23}\]  
Figure 10: The damage variable \(d\) of CA mortar specimens: (**a**) Damage variable \(d\) of specimens; (**b**) Mean and STD. of damage variable \(d\); (**c**) Mean \(\pm\) STD. of damage variable \(d\).  
where \(A_{1}\), \(A_{2}\), \(\varepsilon_{0}\), \(p\) denote the parameters relating to the mean of the stochastic damage evolution process. The parameters of the proposed model are determined by using a fitting method based on the experimental results shown in Figure 10b. The determined values of the parameters are listed in Table 8.  
An Extreme type function was used to characterize the STD. of the damage variable \(STD_{d}(\varepsilon)\) in terms of total strain \(\varepsilon\):  
\[STD_{d}(\varepsilon)=A_{3}e^{(-\varepsilon^{-2}-Z+1)} \tag{24}\]  
\[Z=\frac{(\varepsilon-A_{4})}{w} \tag{25}\]  
where \(A_{3}\), \(A_{4}\), and \(w\) denote the parameters related to the variation of the stochastic damage evolution process. The parameters of the proposed model were determined by using a fitting method based on the experimental results shown in Figure 10b. The determined values of the parameters are listed in Table 9.  
Therefore, the mean stress-strain curve can be addressed in conjunction with Equation (19):  
\[\mu_{\sigma}(\varepsilon)=(1-\mu_{d}(\varepsilon))E_{0}\varepsilon \tag{26}\]  
where \(\mu_{\sigma}(\varepsilon)\) denotes the mean stress corresponds to strain \(\varepsilon\).  
For characterizing the standard deviation of the stress-strain curve, the STD. of the stress \(STD_{\sigma}(\varepsilon)\) that corresponds to the strain \(\varepsilon\) is obtained by direct fitting of the experimental results in Figure 5b by adopting the Nelder type function:  
\[STD_{\sigma}(\varepsilon)=\frac{\varepsilon}{b_{0}+b_{1}\varepsilon+b_{2} \varepsilon^{2}} \tag{27}\]  
where \(b_{0}\), \(b_{1}\), and \(b_{2}\) represent the parameters related to the stochastic behaviors of the stress-strain curve that appeared during loading. The determined values of the parameters are listed in Table 10.
## 7 Conclusions  
The CRS protocol addresses clustering and routing as interlinked issues. The performance of routing in the CRS protocol is compared with the S-LEACH and LEACH-C protocols. The assessment criteria include the number of stable connections among nodes throughout the network operations, energy consumption efficiency, and network lifespan. To ensure even communication loads on the network, DD-TDMA scheduling is used. The network lifetime was improved using the CRS method, which displayed almost 100% stable connection in every cycle/round. For LEACH-C and S-LEACH, these were observed as 96% and 88%, respectively.  
Furthermore, the sensing node survival rate is greater for the smart infrastructure through CRS than for S-LEACH and LEACH-C. The experimental findings show that the suggested method will be effective in micro and complex network systems, especially in smart city infrastructures. Further, in the future, we plan to examine the same strategy with maximum heterogeneity services.  
Data curation, A.I. and I.I.; writing--original draft, V.K.V. and J.P.; supervision, V.K.V. and I.I.; project administration, V.K.V.; conceptualization, V.K.V. and J.P.; methodology, A.B. and I.I.; validation A.I. and M.T.R.; visualization, J.P. and M.T.R.; resources. V.K.V.; writing--review and editing, A.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 20: T: Number of hop counts vs. number of experimental rounds.
## 2 Materials and Methods
### Validation Procedure  
The study was conducted following the guidelines for the translation and cultural adaptation of patient-reported outcomes (PROs) as outlined in the ISPOR report--The Professional Society for Health Economics and Outcomes Research [37], along with the quality standards for the measurement properties of health status questionnaires [38]. The validation procedure consisted of 5 stages.  
_Stage 1:_ The PRIA-RS was forward-translated from English to Greek by two Greek physiotherapists who both spoke English fluently. According to the ISPOR rules [34], it is ideal that the forward translators are citizens of the target nation and have excellent knowledge of PRO measures interpretation. The first translator was acquainted with the objectives that the questionnaire had been developed to measure, in order for his translation to closely match the original instrument. The second translator, unaware of the questionnaire's purpose, produced the second translation to highlight subtle differences between the two versions of the original questionnaire. Any inconsistencies between the two translators were then reviewed and resolved, resulting in a single translation version of the PRIA-RS.  
_Stage 2:_ A different translator, a native speaker of the original language (English) and proficient in the target language (Greek), with no prior experience with the original PRIA-RS, conducted a back translation of the first Greek version of the PRIA-RS. The initial two translators, along with the other authors and the back translator, assessed these translations to ensure conceptual equivalence between the original and the Greek versions;Stage 3: a multilingual committee, consisting of the three translators, the two remaining authors, and one methodologist, reached a consensus on an acceptable pre-final version of the PRIA-RS in Greek by comparing all translations with the original version; Stage 4: The pre-final version of the PRIA-RS was initially distributed to a group of 30 volunteers to ensure its clarity. The volunteers were asked in person whether they fully understood the scale. These individuals met the same inclusion criteria as the study's main sample. Based on their feedback, several adjustments were made to the instrument's language, syntax, and overall comprehension; Stage 5: The final version of the PRIA-RS, along with the other three valid questionnaires, was administered to 113 volunteers. The researchers provided instructions and clarifications as needed while monitoring the correct completion of the questionnaires (Appendix A).
# Aggregate Type and Concrete Age Effects on Anchor Breakout Performance: Large Database and Insights
## 2 Materials and Methods
### Overview
#### 2.1.6. Concrete Age Influence  
Typically, most of the concrete-related experimental investigations are executed at concrete aged approximately 28 days. Twenty-eight-day concrete material properties, mostly the concrete compressive strength (_f_\({}_{\textit{c}}\)), are the standard input parameters for structural design. As a result, both groups performed their first tests at 28 days. In general, design codes and fastening approval documents provide only minimum requirements for concrete age (see earlier discussion), and specific rules concerning the ambient moisture and temperature conditions during curing and storage are missing completely. This is rather problematic as the temperature and humidity history determine the development of hydration and other chemical reactions, which in turn cause the development of mechanical properties such as compressive strength, tensile strength, and Young's modulus. The different mechanical properties develop at different rates as described by the evolution models of ACI [28] and fib [18]. Depending on the investigated problem, this may alter  
\begin{table}
\begin{tabular}{c c c} \hline
**Item/Group** & **CDL** & **CSTB** \\ \hline Number of concretes: low strength & 3 & 4 \\ \hline Number of concretes: high strength & - & 4 \\ \hline Number of different aggregates & Agg. 1–3 & Agg. 4–7 \\ \hline Tests performed at (days): & 28 and 70 & 28 and 180 \\ \hline Tension tests & Headed studs & Headed studs \\ \hline Shear tests & Bonded anchors & Headed studs \\ \hline \end{tabular}
\end{table}
Table 4: Summary of experimental campaigns.  
the prediction of failure loads or even trigger different failure mechanisms. Appreciating the complex aging nature of concrete is an important factor, especially when tests from different laboratories/countries are compared, even if they are supposedly executed at the same age. Ultimately, the concrete age is an arbitrary number and largely depends on the history of ambient temperature and humidity conditions. What should be defined is the actual concrete maturity level or a combination of age and storage conditions. Recently, a comprehensive experimental analysis performed by [PERSON] et al. [26] demonstrated an age and cure dependence of the concrete cone breakout capacity and suggested that the age effect in some cases might be more dominant than other mix and composition effects.
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 2 Microbial and Enzyme-Based Degradation of Plastic
### Microbial Degradation of Plastics
#### 2.1.3 Algal Biodegradation  
Algae, particularly microalgae, are involved in biodegradation because of their ability to enhance the breakdown of plastic [61; 62]. For example, studies have shown that microalgae can grow on the surface of PE, facilitating degradation. When isolated from waste plastic bags colonised by green algae, _Uronema africanum_, a microalga, was reported to initiate the breakdown of low-density polyethylene (LDPE) sheets in 30 days [60]. Only a few algae have been reported to break down plastics, specifically those that release ligninolytic and exopolysaccharide enzymes. While algae show the potential to degrade plastic, it is important to note that biodegradation in the natural environment is slower, which leads to concerns about accumulation of microplastics [63], and algae are difficult to grow as they require specific light conditions and appropriate nutrient levels [64]. Maintaining such conditions consistently can be more complex than for other microorganisms.
# Free Vibration Analysis of Curvilinearly Tapered Axially Functionally Graded Material Beams
## 4 Results
### AFGM Timoshenko Beams with Curvilinearly Tapered Cross-Sections
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**BCs**} & \multirow{2}{*}{\(\delta_{1}\)} & \multicolumn{3}{c}{**0.2**} & \multicolumn{3}{c}{**0.4**} & \multicolumn{3}{c}{**0.6**} \\ \cline{3-13}  & & **Mode** & [23] & **FEM** & **DTM** & \(\Delta^{*}\)**, \%** & [23] & **FEM** & **DTM** & \(\Delta^{*}\)**, \%** & [23] & **FEM** & **DTM** & \(\Delta^{*}\)**, \%** \\ \hline \multirow{6}{*}{C-C} & 1 & 18.4750 & 18.8461 & 18.5033 & 0.15 & 16.423 & 16.7328 & 16.4583 & 0.21 & 14.1591 & 14.7828 & 14.2013 & 0.30 \\  & 2 & - & 53.7872 & 51.5889 & 4.09 & - & 47.1474 & 46.5676 & 1.23 & - & 42.3017 & 40.8931 & 3.33 \\  & 3 & - & 105.999 & 101.332 & 4.40 & - & 93.1729 & 92.1155 & 1.13 & - & 84.2618 & 81.5879 & 3.17 \\  & 4 & - & 175.772 & 167.090 & 4.94 & - & 154.114 & 152.461 & 1.07 & - & 139.905 & 135.640 & 3.05 \\  & 5 & - & 261.850 & 248.400 & 5.14 & - & 229.652 & 227.231 & 1.05 & - & 208.997 & 202.736 & 3.00 \\ \hline \multirow{6}{*}{C-F} & 1 & 2.5715 & 2.71073 & 2.57115 & 0.01 & 2.7594 & 2.94885 & 2.75933 & 0.00 & 3.0151 & 3.17381 & 3.0146 & 0.02 \\  & 2 & - & 18.8484 & 18.0533 & 4.22 & - & 18.3961 & 17.5276 & 4.72 & - & 18.0266 & 17.0113 & 5.63 \\ \cline{1-1}  & 3 & - & 54.3519 & 51.7463 & 4.79 & - & 49.5819 & 48.3800 & 2.42 & - & 46.8092 & 44.6929 & 4.52 \\ \cline{1-1}  & 4 & - & 107.161 & 101.628 & 5.16 & - & 95.5807 & 94.1178 & 1.53 & - & 88.8564 & 85.6122 & 3.65 \\ \cline{1-1}  & 5 & - & 177.343 & 167.573 & 5.51 & - & 156.448 & 154.637 & 1.16 & - & 142.196 & 139.622 & 1.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Dimensionless natural frequencies of truncated AFGM beams with parabolic taper in height and constant width. Analogously, Figure 5a,b illustrates the bending mode shapes of the same truncated AFGM beams, constructed based on the FEM results, which are given in Table 11.  
It is worth noticing that the geometric non-uniformity and the variation of material parameters along the beam length significantly affect the mode shapes. The figures clearly show that the positions of the nodal points shift along the beam length, and this shift is more pronounced as the truncation factor \(\delta_{1}\) becomes smaller. Figure 4: Mode shapes of truncated AFGM beams with parabolic taper in height and constant width obtained from DTM analysis: (**a**) C-C boundary conditions; (**b**) C-F boundary conditions. Figure 5: _Cont._
## 2 Optimal Design Methodology
### Genetic Algorithm  
Genetic algorithm (GA) are widely used in the field of optimization design as a method that mimics the natural selection process of biological evolution. Utilizing this, GA can be used to improve the efficiency of centrifugal compressor impellers [16], optimize motor design [17], identify lithium-ion battery parameters, and validate data [18].  
Therefore, the flowchart of the genetic algorithm is shown in Figure 2, and the operating method can be summarized in five steps as follows.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Description** & **Parameters** & **Description** \\ \hline \(A_{f}\) & Front flow area & \(P\) & Pressure \\ \(A_{ff}\) & Free flow area & \(\dot{\mathcal{O}}\) & Heat flow rate \\ \(\mathcal{C}\) & Heat capacity & \(R_{h}\) & LFHX height \\ \(C_{p}\) & Specific Heat & \(R_{l}\) & LFHX length \\ \(\rho\) & Density & \(R_{w}\) & LFHX width \\ \(F_{d}\) & Fin depth & \(T_{d}\) & Tube depth \\ \(F_{l}\) & Fin length & \(T_{h}\) & Tube height \\ \(F_{t}\) & Fin thickness & \(T_{l}\) & Tube length \\ \(F_{p}\) & Fin pitch & \(T_{p}\) & Tube pitch \\ \(m\) & Mas flow rate & \(T_{t}\) & Tube thickness \\ \(L_{a}\) & Louver angle & \(N_{T}\) & Number of tubes \\ \(L_{h}\) & Louver height & \(N_{f}\) & Number of fins \\ \(L_{l}\) & Louver length & \(NTU\) & Number of transfer unit \\ \(L_{p}\) & Louver pitch & \(T\) & Temperature \\ \(RW\) & Total weight & \(\varepsilon\) & Effectiveness \\ \(efin\) & Fin efficiency & & \\ \hline \end{tabular}
\end{table}
Table 1: Nomenclature of design parameters.  
Figure 1: Geometry representation: (**a**) core and louver fin; (**b**) tube and fin.  
(1) Initialization: Creation of the first-generation population;  
(2) Selection: Choose a model from the smallest objective function value among the population, and the model is defined as the initial model before optimization;  
(3) Mutation: The variables of the initial model are randomly chosen, and the values of the selected variables are mutated. At this moment, the number of variables selected is randomly determined, and, on average, around 50% of all shape variables are selected for variation;  
(4) Crossover: Create a population with a new value in the next generation based on information from the previous generation;  
(5) Replace: When a model with a lower objective function value compared to the optimal model of the previous generation is observed among the models of the next generation population, the value of the current generation evaluated as the objective function is selected from the variable information of the previous generation.