esa-sceva/satcom-chunk-collection huggingface dataset:
# An Online Energy Management Control for Hybrid Electric Vehicles Based on Neuro-Dynamic Programming
## 2 Preparation of Knowledge about the NDP EMS
### Wavelet Transformation Theory  
In this section, we will give a brief introduction to a wavelet transformation theory, which is the basis of a CWNN.  
If a function \(\psi(t)\in L^{2}(R)\) satisfies the following item:  
\[C_{\psi}=\int_{R}\frac{\big{|}\hat{\psi}(\omega)\big{|}^{2}}{|\omega|}d\omega<\infty, \tag{6}\]  
where \(\hat{\psi}(\omega)\) is the Fourier transformation of \(\psi(t)\), which is called a wavelet or mother wavelet.  
Given a wavelet \(\psi(t)\), a family of wavelets \(\big{\{}\psi_{a,b}(t)\big{\}}\) can be derived with different \(a\) and \(b\) from the following equation:  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{a}}\psi\bigg{(}\frac{t-b}{a}\bigg{)}, \tag{7}\]  
where \(a>0\) is a dilation factor and \(b\) is a translation factor. \(\psi_{a,b}(t)\) is called the wavelet basis function.  
For a function \(f(t)\in L^{2}(R)\), we can get its wavelet transform \(WT_{f}(a,b)\) from the following equation:  
\[WT_{f}(a,b)=\frac{1}{\sqrt{a}}\int_{R}f(t)\psi^{*}\bigg{(}\frac{t-b}{a}\bigg{)} dt=\big{<}f(t),\psi_{a,b}(t)\big{>}, \tag{8}\]  
where \(\psi^{*}(t)\) is the conjugate function of \(\psi(t)\). \(WT_{f}(a,b)\) is the inner product of \(f(t)\) and a family of wavelets \(\big{\{}\psi_{a,b}(t)\big{\}}\).  
For a common input \(x\) defined in discrete state, and the dilation set at 2, we can get the following wavelets:  
\[\psi_{m,k}(x)=\frac{1}{\sqrt{2^{m}}}\psi\bigg{(}\frac{x-k\cdot 2^{m}}{2^{m}} \bigg{)}=\sqrt{2^{-m}}\psi\big{(}2^{-m}x-k\big{)}, \tag{9}\]  
where \(m\in Z\) and \(k\in Z\).
## 3 Results and Discussion
### Influence of Laser Treatment on Mechanical Properties
#### 3.2.1 Tensile-Stress Analyses
Very likely, it is related to both the thermal and mechanical processes the steel undergoes before reaching the shape of 200 \(\upmu\)m thick foil. For instance, the rolling process required to reduce the foil thickness and manufacture the coil increases the stiffness along the rolling direction (\(\beta\) = 0\({}^{\circ}\)), with the possibility of reducing the values of elongation parameters [26]. Figure 4: Results of standard tensile test—engineering stress vs. engineering strain diagrams of representative specimens cut from (**a**) untreated and (**b**) laser-treated sheets; designation system in the legend: T/NT—laser-treated/untreated, 0/45/90—specimen orientation with respect to the rolling direction of metal sheet. For laser-treated samples, specimens cut at 90\({}^{\circ}\) represent specimens where the strength is applied in the same direction as the LIPSS orientation.
## 5 Numerical Results  
In this section, we evaluate the proposed differentiated embedded pilot and multi-domain fusion-based OTFS automatic modulation classification approach from diverse aspects. Extensive experiments are carried out to investigate the modulation classification accuracy w.r.t. different SNRs, the effects of pilot symbols, different NN architectures for time-domain and DD-domain data streams, and maximum Doppler shifts. Moreover, the proposed approach is compared with state-of-the-art baselines.
## 3 Results
### Analysis of Temporal and Spatial Changes in August Corn NPP from 2011 to 2020  
A previous study revealed that corn NPP reaches its peak in August, providing a more representative insight into annual corn NPP development. Consequently, this study investigated the temporal and spatial fluctuations in August corn NPP in the Fen River Basin between 2011 and 2020. The aim was to gain a comprehensive understanding of the long-term trends in corn NPP variation.  
In Figure 4, the temporal changes were analyzed, revealing that the mean August corn NPP in the Fen River Basin fluctuated between 94.23 gC/m\({}^{2}\) and 154.12 gC/m\({}^{2}\) during the period from 2011 to 2020. Overall, there was an upward trend in NPP, marked by more significant fluctuations between 2011 and 2016 and, particularly, a pronounced decline from 2015 to 2016. Conversely, a relatively smooth upward trend was observed from 2016 to 2020. This observed pattern could be attributed to factors such as the arid climate and abundant precipitation in the Fen River Basin. Furthermore, when considering the maximum and minimum NPP values, fluctuations in corn NPP during August were similar from 2011 to 2017. However, after 2017, the maximum NPP value steadily increased, whereas the minimum NPP value showed a consistent decline. In conclusion, the carbon sequestration capacity of corn crops in the Fen River Basin demonstrated a continuously increasing trend from 2011 to 2020, particularly after 2017. This was evident in the rising mean and maximum NPP values and the declining minimum NPP value, indicating a clear improvement in carbon sequestration capacity.  
In Figure 5, the spatial distribution of August corn NPP in the Fen River Basin between 2011 and 2020 ranged from 14.40 gC/m\({}^{2}\) to 181.59 gC/m\({}^{2}\). Notably, the lowest value of 14.40 gC/m\({}^{2}\) was recorded in the middle section of the Fen River Basin in 2014, whereas the highest value of 181.60 gC/m\({}^{2}\) was observed in the upper reaches of the Fen River Basin in 2019. Specifically, the August NPP in 2016 generally displayed lower figures, ranging from 16.96 gC/m\({}^{2}\) to 108.10 gC/m\({}^{2}\), while, in 2011, the August NPP was consistently higher, ranging from 34.54 gC/m\({}^{2}\) to 154.54 gC/m\({}^{2}\). Overall, the spatial distribution patterns of NPP remained consistent across the years from 2011 to 2020, with the middle section of the Fen River showing the highest corn NPP, followed by the upper and lower reaches. These observations suggested that the spatial distribution of corn NPP in other months of different years may exhibit a similar pattern.  
Figure 4: Time response of August corn NPP in the Fen River Basin.
## 4 Result and Discussion
### Potential Metrics from Literature and In-Depth Interview Result  
We identified several potential metrics after combining the literature review and in-depth interview process. Potential metrics obtained from the literature review are described in Table 2. There were several potential metrics obtained from the in-depth interview process: physical availability, use of availability, fuel consumption, hauling distance, productivity, truck capacity, coal losses, and mean time to repair (MTTR), etc. From the literature review and in-depth interview result, there were two similar metrics: truck availability and truck utilization. Both of them represented the time the truck was in operation. Time utilization is important since the truck is an expensive asset. The content analysis method was carried out following the coding process by determining the most consistent and frequently used metrics [8; 9]. Once the text was coded, the codes were then further categorized and combined with relation analysis to summarize the data. After conducting a content analysis, we identified the nine potential metrics described in Table 5, which were the most consistent and frequently used metrics for measuring mining  
Figure 2: Coal-hauling process.  
transportation overall vehicle effectiveness based on the literature review, combined with the in-depth interview results. For the next step, we initially used the metrics in Table 5 to develop the Delphi questionnaire.
# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 1 Introduction  
Optical remote sensing images have attracted increasing attention, due to their wide imaging range and high resolution [1]. In existing studies, panchromatic (Pan) images are a data source that have been most widely studied. Thanks to its ability to uncover subtle information, it has been continuously developed in military and civilian applications [2, 3], i.e., geological analysis, city planning, and military reconnaissance.  
However, it is still difficult to detect ships from remote sensing images due to complex and changeable marine backgrounds. Furthermore, huge waves and different types of clouds and fog all can reduce the accuracy of detection, as shown in Figure 1. Clouds may cast shadows on the target, thus substantially changing the illumination of the surface. Scattered clouds and big waves may also become false alarms and confuse the detector. As a consequence, solving how to increase the detection of the target ship and reduce the interference of cloud and fog is the key to improving the ship detection performance.  
In general, ship detection methods include the following steps: (i) image preprocessing, (ii) image feature extraction, and (iii) target ship detection. Early in the development of object detection, many methods focused on how to extract more features from the image and ignored the impact of image quality. However, the success of image processing relies upon the production of accurate imagery along with effective human interpretation. Consequently, the three aspects of ship detection all are important.  
Figure 1: Marine scenes under different types of interference, namely, cloudless areas (with no waves and big waves), scattered clouds areas, thin cloud areas, and thick cloud areas. The images in the first column are the original panchromatic images from the GF-1 dataset. Ship targets are displayed in red boxes. The histograms of the grayscale statistics are illustrated in the second column. The last column contains images that show the heat maps representing the visual saliency of each scene.
# The Study of Thermal Properties of Blackberry, Chokeberry and Raspberry Seeds and Oils
## 3 Results and Discussion  
### Study of Seeds  
#### 3.1.1 Water Activity of Seeds  
Water activity is a useful parameter to evaluate the effectiveness of the drying process. In the case of food material, spoilage disqualifies the product from possible usage. It is claimed that under a value of 0.8 for water activity, microorganisms are not able to grow [22]. Presented in Table 1, measurements of water activity showed the lowest values for blackberry seeds, followed by similar values for chokeberry and raspberry seeds. All of the examined samples reached lower values than the determined limit value, so it may be concluded that studied seeds are microbiologically safe.  
Moreover, what is important when considering seeds as a source of oils, is that the increased moisture may increase the oxidative reactions in the material. That may result in a shorter shelf-life of the product and a lower stability. Products containing high values of PUFA may especially be susceptible to oxidation during storage in conditions of high a\({}_{w}\) values [23].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source of Seeds** & **a\({}_{w}\)** \\ \hline Blackberry & 0.156 \(\pm\) 0.002 a \\ Chokeberry & 0.227 \(\pm\) 0.001 b \\ Raspberry & 0.237 \(\pm\) 0.004 c \\ \hline \hline \end{tabular} a\({}_{w}\)—water activity; mean values with different letters (a\({}^{-}\)) in the columns are significantly different at \(\alpha=0.05\).  
\end{table}
Table 1: Water activity of studied berry seeds.
## 2 Theoretical Background
### Posterior Distribution  
In the BLI inversion process, the resulting distribution (a posteriori distribution) is the probability of the model parameters that are conditioned, which is based on the observed data and a prior model. The multivariate analysis, developed in the work of [35], together with a posterior distribution, results in a measure of uncertainty of the parameters based on the observed data and the a priori model. This distribution shows that the probability distribution, which represents the elastic parameters \(\mathbf{m}\) conditioned on the observed data \(\mathbf{d}\), results in a multivariate Gaussian distribution representing different values of model parameters:  
\[P(\mathbf{m}|\mathbf{d})=\mathcal{N}\Big{(}\boldsymbol{\mu}_{m|d},\boldsymbol {\Sigma}_{m|d}\Big{)}, \tag{14}\]  
where the covariance matrix is \(\boldsymbol{\Sigma}_{m}|\boldsymbol{d}\) and the mean is \(\boldsymbol{\mu}_{m}|\boldsymbol{d}\), developed by:  
\[\boldsymbol{\mu}_{(m|d)}=\boldsymbol{\mu}_{m}+\boldsymbol{\Sigma}_{m}\mathbf{ G}^{T}\Big{(}\mathbf{G}\boldsymbol{\Sigma}_{m}\mathbf{G}^{T}+\boldsymbol{ \Sigma}_{d}\Big{)}^{-1}\mathbf{d}-\mathbf{G}\boldsymbol{\mu}_{m}, \tag{15}\]  
and  
\[\boldsymbol{\Sigma}_{(m|d)}=\boldsymbol{\Sigma}_{m}-\boldsymbol{\Sigma}_{m} \mathbf{G}^{T}\Big{(}\mathbf{G}\boldsymbol{\Sigma}_{m}\mathbf{G}^{T}+ \boldsymbol{\Sigma}_{d}\Big{)}^{-1}\mathbf{G}\boldsymbol{\Sigma}_{m}. \tag{16}\]  
Moreover, the direct linear operator, denoted by \(\mathbf{G}\) and derived from the convolutional model, is given by \(\mathbf{G}\) = \(\mathbf{SAD}\). As the posterior distribution of elastic properties follows a Gaussian distribution, the maximum a posteriori (MAP) solution involves maximizing the logarithm of the posterior probability density function, which is proportional to the product of the prior probability and the likelihood function. Mathematically, this solution is expressed as:  
\[MAP\{(\alpha,\beta,\rho)^{T}\}=\exp\Big{(}\boldsymbol{\mu}_{m|d}-\boldsymbol{ \sigma}_{m|d}^{2}\Big{)}, \tag{17}\]  
where \(\boldsymbol{\sigma}_{m|d}\) is the vector with the posterior variances, obtained from the diagonal of the covariance matrix \(\boldsymbol{\Sigma}_{m|d}\). The computation of the solution to Equation (17) is efficient and stable due to the availability of the analytical solution for the inverse problem.
# Cooperative Localization Using Distance Measurements for Mobile Nodes
## 3 Resolving Rotation and Flip Ambiguities  
In this section, the rotation ambiguity is analyzed mathematically in the noise-free scenario and an analytical solution to the rotation and flip ambiguities presented.
# Stabilization of Transverse Modes for a High Finesse Near-Unstable Cavity
## 1 Introduction
As a proof of principle, we locked the cavity at the wavelength of 605.78 nm, which matches the hyperfine energy-level transition \({}^{3}\)H\({}_{4}\leftrightarrow^{1}\) D\({}_{2}\) of Pr\({}^{3+}\) ion in Pr\({}^{3+}\):Y\({}_{2}\)SiO\({}_{5}\) crystal and has many potential applications such as quantum storage and quantum information processing [18; 19]. Such cavity is also potentially useful to generate sub-megahertz narrow-band degenerate single photons and to improve the single-photon generation rate of SPDC. When locking the cavity in different transverse modes, one can generate single photons within the same transverse modes, which can be used in quantum lithography and quantum imaging.
## 2 Research Methodology
### Data Clustering
#### 2.1.2 ILI Data Clustering Using DBSCAN  
While DENC is specifically designed for the feature matching problem, alternative techniques such as DBSCAN can also be employed to capture spatial variability in ILI data. The integration of DBSCAN into the proposed model differs slightly from DENC due to DBSCAN's lack of explicit support for circumferential continuity and labeling data as anchors and nodes. Instead, the moving set P is double unrolled, and all features in the extended set \(\widetilde{\text{P}}\) and reference set Q are stacked in one set. Next, this set is clustered using DBSCAN, as initially introduced by [PERSON] et al. [28]. Then, the clustered data are mapped back to their original sets, P and Q. Finally, a post-processing step is introduced, where features of clusters formed exclusively from P or Q are filtered out as outliers.  
When configured appropriately, DBSCAN can exhibit similar clustering behavior to DENC, but it relies on isotropic thresholds rather than anisotropic (directional) ones. To capture the direct correspondence of isolated features, such as the two pairs within the first 0.75 m in the example problem shown in Figure 9, the parameter minPts is set to 2. In DBSCAN, this configuration enables the formation of dense regions when a feature has at least one neighboring feature within proximity threshold \(\varepsilon\), as well as allowing for the identification of isolated outlier features. The obtained clusters for the given example using DBSCAN with minPts = 2 and \(\varepsilon=0.240\) m are identical to the clusters obtained by DENC and illustrated in Figure 11. DBSCAN implementation in the Python 3.0 package'sklearn' is employed in this study.  
Methodologically, when minPts = 1, DBSCAN becomes incapable of identifying outliers, as dense regions are formed by as few as one feature (the feature itself). However, the post-processing step will result in equivalent clusters to those of minPts = 2, as clusters containing a single feature from either P or Q are also filtered out as outliers.
## 9 Fundamental Concepts Underlying Adaptive Filters
### Deciphering Recursive Least Squares (RLS): Advanced Insights  
The relationship between the desired signal, \(d(t)\), the reference signal, \(n(t)\), and the filter output, \(y(t)\), can be expressed as [109; 110]:  
\[y(t)=\mathbf{w}^{T}(t)\mathbf{n}(t), \tag{20}\]  
where \(\mathbf{w}(t)\) is the vector of filter weights at the time \(t\), and \(\mathbf{n}(t)\) is the vector representing the reference signal and its delays until '(M-1)-th order, with \(M\) indicating the filter order. The error between \(d(t)\) and \(y(t)\) is given by:  
\[e(t)=d(t)-y(t). \tag{21}\]  
The objective of the RLS is to minimize the weighted sum of squared errors up to time \(t\), denoted as  
\[J(t)=\sum_{i=1}^{t}\lambda^{t-1}e^{2}(t), \tag{22}\]  
where \(\lambda\) is the forgetfulness factor, a constant between 0 and 1 that determines how quickly the weight of past errors decreases.  
RLS updates the filter weights using the following rule:  
\[\mathbf{w}(t+1)=\mathbf{w}(t)+\mathbf{k}(t)e(t), \tag{23}\]  
where \(\mathbf{k}(t)\) is the gain vector of the filter at the time \(t\), calculated as:  
\[\mathbf{k}(t)=\frac{\mathbf{P}(t-1)\mathbf{n}(t)}{\lambda+\mathbf{n}^{T}(t) \mathbf{P}(t-1)\mathbf{n}(t)}, \tag{24}\]  
and \(\mathbf{P}(t)\) is the inverse of the prediction error covariance matrix updated as follows:  
\[\mathbf{P}(t)=\frac{1}{\lambda}\Big{[}\mathbf{P}(t-1)-\mathbf{k}(t)\mathbf{n} ^{T}(t)\mathbf{P}(t-1)\big{]}. \tag{25}\]  
Initially, \(\mathbf{P}(0)\) is typically set as an identity matrix multiplied by a large scalar value, assuming limited knowledge of the initial state of the system.  
The RLS algorithm dynamically updates the filter weights to minimize the mean square error, adapting in real time to changes in signals and artifacts. This ability for rapid and precise updates makes RLS particularly effective at removing artifacts from EEG signals, where characteristics can change quickly. Table 6 summarizes the strengths and weaknesses of the filters presented here.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## Abbreviations  
* **Word Abbreviations** SMOTE Synthetic Minority Over-sampling Technique SGDC Stochastic Gradient Descent SVC Support Vector Classifier GB Gradient Boost NPK Nitrogen Phosphorus, Potassium MAC Medium Access Control RFID Radio Frequency Identification UHF Ultra high frequency PLS Physical Layer Signalling PS Packet Switching CLS Connectionless Mode Service KNN K-Nearest Neighbor RF Random Forest SVC Support Vector Classifier DT Decision Tree XGBoost Extreme Gradient Boosting pH Potential of Hydrogen CART classification and regression tree NB Naive Bayes ANN Artificial Neural Network RFR Random Forest Regression GPM Generalized Poisson Models CNN Convolution Neural Network ITE&C Information Technology, Electronics and Communications Department MAE MAE Mean Absolute Error MSE Mean Squared Error R\({}^{2}\) score R-squared score AI Artificial Intelligence REP Tree RRepeated Incremental Pruning Tree RMSE Root mean squared error CV coefficients of variation IMD, Pune India Meteorological Department, Pune DT, NN Decision Tree, Neural Network MLR Multiple Linear Regression PCA and LDA principal component analysis, linear discriminant analysis GBM Gradient Boosting Machine MARS Monitoring Agricultural Resource LSTM long short-term memory GBDT model Gradient-Boosted Decision Trees XAI Explainable Artificial Intelligence VTC Voting Classifier PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses MNB Multinomial Naive Bayes HGB Histogram Gradient Boosting AUC ROC Area under the Receiver Operating Characteristic Curve
## 4 Results and Discussion
### Linear Static Finite Element Analysis Results  
In linear static analyses, axial forces and bending moments of critical members are obtained based on different load cases. The maximum values in the results are considered to dominate the member sizing process. The member capacity is calculated by establishing an Excel spreadsheet. The maximum segment lengths and associated critical axial forces and bending moments for each structural component are applied as length, design load, and moment parameters. Thus, the appropriate member size can be selected accordingly by referring to the capacity calculation results. Combined bending and axial actions are also assessed for beam members. For a simplification of design in this project, the member sizes for members in each structural component are the same. Although this will compromise on a lower structural efficiency, a more conservative result can be obtained following the general design principle of BCE. A summary of the analysis results, including the axial forces and bending moments for each structural member under ULS load combinations, is presented in Tables 12-27. In those tables, bending moments are taken in absolute value. Compressive and tensile forces are negative and positive, respectively. For the truss members, 1.2G + 1.5Q dominates the axial force design, and the wind case dominates the flexural design; however, for the beam members in transverse bracing components, although the 1.2G + 1.5Q load case is still significant, the effect of the wind load case dominates the member design of some components. This is reasonable as the truss members are primarily vertical to the ground surface and support the whole structure, subjected to the gravity and imposed loads, while the transverse beam members are in the horizontal direction and withstand wind loads from various directions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Primary Arch (Main Arch) Transverse Bracing**} \\ \hline  & \multicolumn{3}{c}{**Chord**} & \multicolumn{3}{c}{**Web**} \\ \hline Size & 400 WC303 & \multicolumn{3}{c}{150 UC30.0} \\ Length (mm) & 8000 & \multicolumn{3}{c}{8000} \\ Axis & X & Y & X & Y \\
1.35G & 255 & 47 & 3 & 0 \\
1.2G \(+\) 1.5Q & 726 & 147 & 7 & 1 \\
1.2G \(+\) & 509 & 104 & 5 & 1 \\
1.2G \(+\) & 519 & 107 & 5 & 20 \\
1.2G \(+\) & 484 & 102 & 4 & 20 \\ \hline
**Maximum** & **726** & **147** & 7 & **20** \\
**Flexural Capacity** & **1603** & **847** & **29** & **22** \\
**Utilisation** & **0.45** & **0.17** & **0.24** & **0.92** \\ \hline \hline \end{tabular} Note: Bending moments are presented as absolute values.  
\end{table} 
Table 14: Analysis results of axial forces and member selection for secondary arch (sub-arch) component (kN).