esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Evaluating the reliability of deep soft rock tunnels is a very important issue to be solved. In this study, we propose a Monte Carlo simulation reliability analysis method (MCS-RAM) integrating the adaptive momentum stochastic optimization algorithm (Adam), Bayesian inference theory and Gaussian process regression (GPR) with combined kernel function, and we developed it in Python. The proposed method used the Latin hypercube sampling method to generate a dataset sample of geo-mechanical parameters, constructed combined kernel functions of GPR and used GPR to establish a surrogate model of the nonlinear mapping relationship between displacements and mechanical parameters of the surrounding rock. Adam was used to optimize the hyperparameters of the surrogate model. The Bayesian inference algorithm was used to obtain the probability distribution of geotechnical parameters and the optimal surrounding rock mechanical parameters. Finally, the failure probability was computed using MCS-RAM based on the optimized surrogate model. Through the application of an engineering case, the results indicate that the proposed method has fewer prediction errors and stronger prediction ability than Kriging or XGBost, and it can significantly save computational time compared with the traditional polynomial response surface method. The proposed method can be used in the reliability analysis of all shapes of tunnels.  
soft rock tunnel; reliability analysis; Adam; Bayesian inference; Gaussian process regression; Monte Carlo simulation +
Footnote †: journal: Applied sciences
## 4 Experiments
### Data Preprocessing  
The first 1D-CNN layer of the proposed model acts as the input layer to receive one-dimensional traffic state data. The data features must be on the same scale for efficient convolution operations. The normalisation techniques facilitate the task of converting differently scaled feature points into an identical scale, guaranteeing each feature bears equal importance. Different normalisation algorithms were tested, such as the MinMaxScaler, MaxAbsScaler, StandardScaler and RobustScaler algorithms. After several trial-error tests, it was found that the MaxAbsScaler algorithm led to the best results. Data preprocessing also included the formation of training and test datasets. The traffic speed and flow datasets contain 1,855,589 and 99,359 data samples, respectvely, with 70% of them constituting the training dataset and the rest used for testing. Data preprocessing also included the conversion of training and test data into single-length vectors, each with a time step of 12.
# Ultraviolet-Assisted Modified Delignified Wood with High Transparency
## 4 Conclusions  
In this paper, the effect of UV- assisted delignification on the synthesis of optically \"transparent wood\" with balsa wood as a precursor was measured and analysed. Through the analysis of transmittance and haze results, it was found that under the same treatment time, the transmittance of \"transparent wood\" prepared by UV-assisted delignification was greatly improved. SEM and XRD results showed that the polymer penetrated the wood and filled the cell cavity under vacuum impregnation treatment. FTIR measurements showed that the chromophores in wood were removed from wood after oxidation by hydrogen peroxide, and UV irradiation greatly increased the rate of this chemical change. The results of transmittance-haze and chemical composition analysis show that the ultraviolet-assisted delignification accelerates the rate of lignin and chromogenic removal, enhancing the optical properties of the \"transparent wood\". UV-assisted delignification of DW-B-U resulted in 17% higher delignification than the DW-B group, all other things being equal, and the UV-assisted delignification for 2 h increases the transmittance of the \"transparent wood\" by 20%. After UV-assisted delignification for 4 h up to 45% of lignin is preserved, and then the wood is impregnated to obtain a clear wood with a transparency of 71% and a haze of 90%. At the same time, the \"transparent wood\" with high transmittance of 82% and haze of 75% was obtained after 8 h of UV-assisted modification of lignin and 3 h of vacuum impregnation-filled polymer. At the delignification stage of clear wood preparation, this paper firstly combines delignification under alkaline solution conditions with UV irradiation and provides a qualitative analysis of the effects produced by UV light. The results of this study show that such transparent materials with light transmittance and high haze can be rapidly manufactured with the help of sunlight with strong UV rays. The universality of the method is attractive for developing sustainable materials for potential solar cell substrate applications. At the same time, this method is enlightening for the high-quality, fast and green preparation of other derived functional materials based on delignified wood.  
Conceptualization, X.C. and J.W.; methodology, X.C. and Y.H.; software, X.C.; validation, X.C. and S.G.-Z.; formal analysis, H.Y. and X.C.; investigation, X.C.; resources, X.C.; data curation, X.C.; writing--original draft preparation, X.C. and J.W.; writing--review and editing, X.C. and S.G.-Z.; visualization, X.C. and Y.H.; supervision, J.W. and J.H.; project administration, H.Z. and W.O.-Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the Fundamental Research Funds for the Central Universities, grant number 2572020 BC02.  
Not applicable.  
The authors declare no conflict of interest.
# RSS Indoor Localization Based on a Single Access Point
## 3 Proposed Approach
### High-Level Description of the Proposed Approach
Depending on the Scenario set-up (different combination of antenna orientations/polarization), the RSS values were expected to vary significantly. The candidate radiomaps were then exported to a standardized template format, and their localization accuracy assessed. In order to perform the assessment, a testing dataset sample was required, which is a set of RSS fingerprints at a number of a priori known locations within the study area. Such a sample could either be retrieved during the online phase, as described in Section 2.1, or can be generated through a simulation. The size of the aforementioned sample and the distribution of the test locations were both selected in such a way as to ensure reliable and objective testing, as per [13]. For the purpose of the radiomap assessment, two types of localization algorithms were implemented: a deterministic (WKNN) and a probabilistic (MMSE) algorithm. Each candidate radiomap was tested  
Figure 1: Typical antenna orientation set-ups: Part 1. (**a**) Tx-01 Vertical -Tx-02 Vertical; (**b**) Tx-01 Vertical -Tx-02 Horizontal 90 deg. Figure 2: Typical antenna orientation set-ups: Part 2. (**a**) Tx-01 Vertical -Horizontal 0 deg; (**b**) Tx-01 Vertical -Horizontal 45 deg. and its performance evaluated with respect to the achieved localization accuracy. Lastly, the correlations between all pairs of fingerprints in the dataset were examined, and an overall correlation score for the candidate radiomaps was calculated [15]. A high correlation score designates a higher probable localization error, due to high similarities between different fingerprint pairs (locations). After the completion of the aforementioned iterative procedure, the most suitable radiomap among the candidates was selected. The selection was based on two performance metrics:'minimum mean localization error' and'minimum correlation score'.
## 4 Single DKS Stability Analysis
We repeat this process 30 times and calculate the average value. To evaluate the stability of singleDKS at each point, initially we determine the number of peaks by computing the optical intensity [8]. If the value of the number of peaks is one, it indicates that the wave may be stable. Otherwise, the wave will be unstable and will converge to a different type of wave. When the number of peaks is one, it indicates that the wave is a single DKS. Its stability is evaluated by computing the eigenvalue solution of the eigenvalue of the \(\mathcal{L}\) matrix and validating that \(\lambda_{i}<0\). This process is necessary because a single soliton wave may not be inherently stable and can exhibit oscillations or breathing, which are considered unstable. The red area in Figure 7 shows the regions where the single DKS is still stable at the end of the simulation for the four parameter sets \((a,b)\) that we investigated proposed. This result shows that the deterministic generation of a single DKS is correlated with a greatly enhanced stable region. We also observe that the parameter space \((a_{1},b_{1})\) exhibits a small stable region, suggesting that while a substantial number of single DKSs are produced, they are unstable, rendering them unreliable for deterministic generation (see Figure 7a). Furthermore, the other parameter spaces have a potential area for soliton generation (see Figure 7b-d). All the stable zones lie below the chaotic zone, which facilitates the generation of single DKS, as proposed in this research. Note that configuration \((a_{4},b_{4})\) presents a value greater than \(a=2\pi\times 10\). This situation poses a challenge in the design and manufacturing process of DCM. Therefore, it is more feasible to utilize the configurations proposed in \((a_{2},b_{2})\) and \((a_{3},b_{3})\).
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
###### Abstract
According to the above ideas, this article comprehensively improves ball-tracking accuracy from multiple perspectives, such as sampling strategy, feature extraction, ball object detection trajectory generation, and trajectory correction. The contributions of our approach are summarized as follows:  
1. We propose a heuristic composite sampling strategy. In our method, local region sampling and global image sampling are used for data augmentation;
2. We propose an anchor-free detection network with a classifier. Among them is the backbone network, a new multiscale feature enhancement and context information fusion network specially designed for balls;
3. We propose a tracking algorithm for multilevel collaborative trajectory matching and correction. Automatically correcting the trajectory for different generated trajectory states can provide more stable tracking;
4. We propose a ball-tracking data set specifically for sports match videos. The data set involves three sports, soccer, basketball, and volleyball, and the annotation information is all manually produced.
# Enhancing Supercapacitor Simulation Accuracy Through a Novel Hybrid Modeling Approach
## 2 SC Modeling and Parameter Estimation Methods  
### Supercapacitor Modeling Approaches  
A supercapacitor is an electrochemical system that holds a charge electrostatically and integrates two porous electrodes that are immersed in an electrolyte solution. The effective area of the plates, the distance between the plates, and the value of the dielectric constant of the separation medium determines the capacitance. For a supercapacitor, the plate area is obtained from a porous carbon-based electrode material that has a much larger effective surface area than a typical plate structure due to its porous nature. Recent advancements, as presented in [35; 36], aim to design high-performance electrode materials beyond traditional carbons, enhancing the behavior and efficiency of SCs. The electrolyte, a conductive solution, contains both anions (negatively charged ions) and cations (positively charged ions). During charging, anions migrate towards the positive electrode, and cations move towards the negative electrode, thereby storing electrical energy as charges accumulate on each of the two electrode surfaces. This operating principle of supercapacitors from the perspective of energy exchange between electrodes and electrolytes is presented in Figure 1[13].  
The literature contains various approaches that describe the dynamics of the above-described process, such as equivalent electrochemical modeling, circuit modeling, or data-driven models. Selecting the right method and model is not a straightforward process. The model should be created for a specific purpose because it can only serve as a substitute for real systems, and its accuracy depends on the assumptions and criteria [21]. Next, the most used methods presented in the literature are discussed.
# Equivalent Modeling and Verification of a High-Steepness and Lightweight Elliptical Aluminum Mirror
## 3 Equivalent Design Theory of a High-Steepness Mirror
### Flat Plate Equivalent Method for High Steepness Circular Mirror  
The primary mirror of modern large telescope optical system is made with a relatively fast primary focal ratio and high steepness, whose center thickness \(T_{min}\) and edge thickness \(T_{max}\) greatly differ. Since the radius of the curvature of a mirror with a fast focal ratio is relatively short, the sagittal depth is quite large.  
The combination of a fast focal ratio and thin mirror leads to a large change in the thickness of the mirror from the center to the edge. Whether \(T_{min}\) or \(T_{max}\) is selected for the equivalent plate thickness, the self-weight deflection obtained in the analysis has a large error, which is not conducive to the rapid convergence of the initial structure of the mirror. Therefore, it is vitally important to establish a plate equivalent model with a smaller initial error to facilitate rapid modeling and performance estimation.  
For high-steepness flat-concave mirrors, the mirrors can be divided into two parts, the mirror body and the back plate, as shown in Figure 3. The back plate is used as the basic thickness \(T_{min}\) of the equivalent plate, the equivalent thickness of the mirror part is \(T_{mirror}\), and the final equivalent thickness of the plate is \(T_{EQ}\):  
\[T_{EQ}=T_{\min}+T_{mirror} \tag{2}\]  
We focus on the equivalent thickness of the mirror body. Since the center of mirror weight of high steepness mirror changes with the changes in mirror curvature and aperture, we propose that the distance between the center of gravity of the mirror body \(T_{CG}\) and the back plate is taken as the equivalent plate thickness \(T_{mirror}\) of the mirror body. The following relationships are satisfied:  
\[T_{\min}\leq T_{CG}\leq T_{\max} \tag{3}\]  
Figure 3: Equivalence of high-steepness circular mirror plate.  
For the sake of simplicity and generality, the best-fitting sphere can be used to replace the aspheric surface for the initial structure modeling of the aspheric surface with a small deviation, thus reducing the complexity of modeling. The parsing expression is as follows:  
\[\left\{\begin{array}{l}S=R-\left[R^{2}-\frac{D^{2}}{4}\right]^{1/2}\\ T_{CG}=\frac{5\left(3D^{2}-85.R+2S^{2}\right)}{6D^{2}-24S\cdot R+8S^{2}}\\ T_{EQ}=T_{\text{min}}+T_{CG}\end{array}\right. \tag{4}\]  
where \(R\) is the best fitting spherical curvature radius of the high-steepness mirror, \(D\) is the diameter of the mirror, and \(S\) is the sagittal depth difference between the outer edge of the mirror and the center of the mirror.
# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 4 Experiment and Discussion
### Ablation Experiments  
A series of contrast experiments were conducted to verify the superiority of our proposed approaches. In order to make it more intuitive, we defined ORIG to denote the baseline model CenterNet [36]. The ablation experiments cover the evaluation of the dynamic gradient adjustment (DGA) and the united bounding regression (UBR), as well as their combination DGANet.
## 4 Simulation Results
### Clock Phase Noise
Similar with Allan Deviation induced phase jitter, it can be seen from Figure 10 that vibration induced phase jitter becomes larger with decrease of the bandwidth. It can exceed the tracking threshold (45 ) [2] when the bandwidth is smaller than 1 Hz. The differential phase jitter mainly depends on the slave loop due to its much smaller bandwidth. However, in the assisting tracking loop structure, phase jitters of the two loops are almost equivalent, and the differential phase jitter is almost zero. Hence, the vibration induced phase jitter in the slave loop could be eliminated by the assistance from the master loop. Therefore the slave loop in this structure could adopt a very small bandwidth to suppress thermal noise. Meanwhile, the differential phase jitter could be greatly reduced, resulting in a more precise relative positioning.
## 4 Problem Statement and Methodology Definition
### Methodology  
The experimentation phase of this research was carried out using popular Python libraries such as Scikit-Learn for common ML algorithms and Pytorch for tensor handling and more advanced deep learning algorithms, in conjunction with Pytorch Geometric (PyG) for the implementation of GNNs and other graph-related applications.  
Due to the high volume of the data and the complexity of the utilized models, specifically the GNNs, it was necessary to apply a mini-batch training technique to avoid Out Of Memory (OOM) errors. To split the transactions graph into separate batches without losing structural information, it was utilized as a unit for batching the subgraph corresponding to each timestep, since the transactions from different timesteps were not linked to each other. Formally, each subgraph can be defined in the following manner:  
\[G_{t}=(V_{t},E_{t})\quad\text{where}\quad\begin{cases}V_{t}=\{v\in V\mid\text {TIMESTEP}(v)=t\}\\ \text{and}\\ E_{t}=\{(s,t)\in E\mid s,t\in V_{t}\}\end{cases}\quad t=1,\ldots,49 \tag{1}\]  
Then, the \(G_{t}\)s could be grouped to form training batches of a specific size.  
Concerning benchmarking and the evaluation of the models' performance, selected metrics were chosen to account for the imbalance of the data. Thus, the Accuracy metric was discarded since it only measures the number of successful predictions, which can be very high for the licit class but low for the illicit class. Instead, other evaluation functions such as the Precision, Recall and F1 were selected, since they penalize the amount of false positives, false negatives and both, respectively. Additionally, for the sake of displaying an accurate picture of the predictions performed by the models, a confusion matrix is provided for each experiment.  
Finally, as a complementary measure, the ROC curve graphic is also provided for each experiment, showing the proportion of true positives to false positives as the decision threshold grew. This threshold could be a probability in the case of supervised learning models or an outlier factor in the case of anomaly detection experiments. The Area Under the Curve (AUC) is also provided as another metric.
## 5 Future Prospects
This involves developing robust communication networks, deploying smart meters, and establishing interoperability standards to ensure seamless interaction between various components of the energy system. Such measures will enable real-time monitoring and control of energy flows, enhancing system efficiency. Government policies and regulatory frameworks will play a vital role in shaping the future of EMSs. Anticipated policies are expected to incentivize EMS adoption, promote cross-sector interoperability, and encourage innovation in clean energy technologies. Supportive policies, such as subsidies for renewable energy projects, tax incentives for energy storage, and regulations promoting smart grid development, will help create an environment conducive to advancing sustainable energy systems. Lastly, improving consumer engagement will be a key element of future EMSs. Technologies such as smart meters and intuitive energy management apps will provide consumers with real-time insights into their energy usage. This transparency will empower users to make informed decisions about their energy consumption and participate in demand-side management initiatives, enhancing overall energy efficiency and sustainability. For instance, mobile apps can offer detailed energy usage reports and suggest ways to reduce consumption, fostering energy conservation. The future of EMSs is poised for significant advancements in technological integration, smart grid capabilities, hybrid energy systems, optimization strategies, supportive legislation, and increased consumer involvement. These developments will drive improvements in efficiency, resilience, and sustainability across global energy networks, playing a crucial role in transitioning to a more sustainable energy future.
# Heterologous Production of Flavour and Aroma Compounds in _Saccharomyces cerevisiae_
## 1 Introduction  
Synthetic biology is one of the most rapidly evolving branches of the biological sciences. It allows the introduction of custom made genetic pathways into organisms that provide them the ability to display biological and/or biochemical properties over-and-above the original, wild type background. Numerous examples have been recently reported of the successful implementation of synthetic biology in microorganisms such as _Saccharomyces cerevisiae_, such as for the production of valuable biomedical compounds [1] or biofuels [2, 3]. Many different microorganisms, like _Yarowia lippolitica_ or _Escherichia coli_, have also been utilized as hosts for various synthetic biology applications [4, 5]. However, because of the availability of a vast array of well-established molecular and genetic techniques, baker's yeast _S. cerevisiae_ continuously remains one of the most frequently used microorganisms in this rapidly growing field. In this minireview, attention will be focused on the utilization of _S. cerevisiae_ for the heterologous production of flavour and aroma compounds, and their potential applications in food and beverage industries.
## 4 Numerical Simulation Results
### Check Valve Motion Characteristics  
The difference in the movement of the check valves can be seen when the oscillation frequency of the actuator changes. In the previous example on flow rate performance of the piezoelectric-hydraulic pump in Section 2.2, the effect of phase shift on the opening and closing process of the check valves is carefully described. However, as mentioned earlier, the interaction between the check valves and the fluid is an FSI problem, which is nonlinear. In turn, assuming that the check valves' movements in a fluid medium follow a sinusoid form [21], would not be completely correct as in practice. The FSI simulation in Ansys will generate the result of the displacement between the two valves in the most realistic way. The obtained movement of the inlet check valve and outlet check valve will provide a better understanding of the effect of the phase shift on the results of the obtained flow rate. The movements of both check valves corresponding to the driving frequency domain of the actuator are shown in Figure 9.  
Figure 8: The transient flow rate result at 120 Hz.  
Figure 9: _Cont._The difference between the flow obtained at the low and the high operating frequency of the piston comes from the phase shift value between the valve movement and the piston, clearly exemplified in Section 2.2. At low frequencies, the inlet and outlet alternately open and close when the piston moves up and down, respectively. However, at high frequencies, the regular opening and closing in this way are no longer available. Although the piston goes up, the inlet check valve can be opened, and the outlet check valve can be closed. Due to this phenomenon, the amount of fluid flowing through the inlet and the outlet changes.  
The result of the total flow pumped out is shown in Figure 10. The linear relationship between the total flow and the frequency of the piston can be seen at low frequencies. However, at high frequencies, this relationship changes. The greater the frequency, the lower the flow value. This is explained by the fact that, when the actuator operates at a high frequency, the opening and closing of the inlet and the outlet check valves changes. When the piston goes up, the liquid is ejected and the inlet and outlet open. This reduces the amount of liquid exiting the outlet, which in turn reduces the obtained flow rate.