esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validations of the Two-Phase and Porous Media Model
### Working Conditions of the Small- and Full-Scale FGD  
For the small-scale tower, the inlet gas flow Reynolds number, \(Re_{inle}\), is around 8 \(\times\) 10\({}^{4}\) according to the gas mixture density (1.067 kg/m\({}^{3}\)), velocity (3.2 m/s), and the diameter of the pipeline (0.4 m) at the inlet as shown in Figure 3a. With different \(L/G\) values, the inlet Reynolds numbers for the liquid are about 1.6 \(\times\) 10\({}^{4}\) for Case 1 and 1.07 \(\times\) 10\({}^{4}\) for Case 2. These flow conditions of the gas and liquid inlets of the small-scale tower are highlighted in Tables 2 and 3. Moreover, the concentration of SO\({}_{2}\) gas at inlet is 152 and 145 ppm, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Porosity \(f_{h}\)**} & \multirow{2}{*}{\(t_{h}/D_{h}\)} & \multicolumn{2}{c}{\(Re_{h,g}\)} & \multicolumn{2}{c}{\(Re_{h,l}\)} \\ \cline{3-5}  & & **Case 1** & **Case 2** & **Case 1** & **Case 2** \\ \hline
34.3\% & 0.75 & 5130 & 5130 & 860 & 573 \\ \hline \end{tabular}
\end{table}
Table 1: This table displays the important parameters of the sieve tray.  
Figure 5: This figure illustrates the top view of the perforated structure of the sieve tray.  
As for the large-scale tower, the Reynolds number at inlets is 6.35 \(\times\) 10\({}^{5}\), 1.35 \(\times\) 10\({}^{5}\), and 1.44 \(\times\) 10\({}^{5}\) for gas at inlet, liquid at the lower, and higher spray array, respectively. The corresponding value of \(L/G\) is 5, and the SO\({}_{2}\) gas at inlet is 359 ppm.
# Fiber Sensing in the 6G Era: Vision Transformers for \(\phi\)-OTDR-Based Road-Traffic Monitoring
## 2 Deep-Learning-Based Traffic Parameter Estimation
### Preparation, Training, and Performance Evaluation
#### 2.3.2 Model Training and Performance Evaluation  
A summary of the training process is depicted in Figure 5. The dataset is initially divided into training and validation sets inside the stratified \(k\)-fold cross-validation loop. We use stratified \(k\)-fold to address class imbalances and have statistically represented classes in training and validation sets. The train-validation split is set to 0.2 for vehicle counting and approximately 0.15 for average speed estimation (\(k=5\) and \(k=7\) for \(k\)-fold cross-validation for car counting and speed estimation, respectively). The preprocessing layers are empirically tested, and the performance of the validation set is assessed. The model is first trained for 25 epochs for hyperparameter optimization, including a number of FC layers, FC neurons, and loss functions. The evaluated metrics cover the area under the receiver operating characteristic curve (AUC-ROC, adapted for multi-class classification), accuracy, validation loss, F1 score, precision, root mean squared error (RMSE), and recall. Following testing with multiple metrics, accuracy was selected as the primary evaluation criterion, yielding the best performance on previously unseen data. The other metrics are only used for further analysis. After training and validation, inference is applied over unseen data to assess the traffic behavior during arbitrary days.  
For the entire training and performance assessment, we use the Keras library and NVIDIA GeForce GTX 1660 graphic processing unit (GPU). The GPU driver 556.12 is used, with Python 3.10.13, Tensorflow 2.10.1, CUDA 12.5, and cudnn 8.1.0.77 versions. Table 1 shows a comparison of the MobileNetV2 and ViT models for both classification tasks during training regarding the number of parameters, multiply-accumulate (MAC) operations, and total memory occupation. The ViT requires a substantially higher number of parameters (40-fold). However, despite having fewer parameters, MobileNetV2 requires more MAC operations due to the spatial nature of the convolution. In terms of random access memory,  
Figure 5: General process of DL model training for MobileNetV2 and ViT. Waterfall traces are labeled regarding car quantity and average speed, preprocessed, and submitted to DL models for vehicle density and average speed estimation. After hyperparameter tuning, inference is done over unseen images.  
MobileNetV2 requires half the capacity. The results indicate equivalent computational complexity for both models.
# Designing and Implementing Weather Generators as Web Services
## 3 Design and Implementation
### Web Service Server
#### 3.3.2 Core Weather Generator Service  
This component is to provide weather generator as services to clients. Legacy weather generator programs DisAg and predictWTD are wrapped into web service framework. This component initially wires to HW SOS Servers for retrieving long-term climatological data to be an input to the weather generator programs. Each weather generator web service has its own service endpoint for users to invoke. The complicated structure of legacy weather generator models is hidden behind the web service framework. The essential parameters described in Figure 5 (e.g., param_WTD.txt) for running the model are translated into service parameters. Concatenation of service endpoint and URL parsing technique is applied for inputting climatological data into the models. Weather realizations as output from these weather generator services are in WTD or WTDE format. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Service Endpoint** & [[http://ec2-52-69-188-223.ap-northeast-1.compute.amazonaws.com](http://ec2-52-69-188-223.ap-northeast-1.compute.amazonaws.com)]([http://ec2-52-69-188-223.ap-northeast-1.compute.amazonaws.com](http://ec2-52-69-188-223.ap-northeast-1.compute.amazonaws.com)): \\  & 8080/HW\_SOS\_2/service? \\ \hline
**HTTP GET Parameter** & **Example Value** & **Description** \\ \hline service & SOS & To identify the service \\ \hline version & 2.0.0 & To identify the version of SOS API \\ \hline request & GetObservation & To identify the SOS operation \\ \hline \multirow{2}{*}{
\begin{tabular}{c} MergeObservationIntoDataArray \\ \end{tabular} } & \multirow{2}{*}{true} & \multirow{2}{*}{Optional parameter to request result in data array} \\  & & \\ \end{tabular}
\end{table} 
Table 2: HW SOS and GetObservation request. We exposed two testbed services for DisAg service and predictWTD. Each service and its parameters are described in Table 3.  
1. DisAg weather generator web service (DISAGWS) This is a web service, which wraps DisAg weather generator program into the framework. The complicated structure of legacy DisAg program is hidden behind. We simplified the service by allowing users to invokes the program using HTTP GET. The service parameters are minimized and described in Table 3.  
Example request for requesting 10 scenarios of weather realization from weatherhistory_urn:IBUNYA: AMeDAS-NIAES:GAMAGOORI-51281 is as follows.
## 4 The Proposed Methodology--A Demonstration
### Choosing the Optimal Pseudoknot  
Various techniques have been proposed in the scientific literature to predict RNA base pairing, including (i) the minimum free energy (MFE) method [43], based on the second law of thermodynamics, which identifies the RNA sequence with the lowest free energy, though it may not be often found in nature; (ii) the maximum pairing principle [44], which relies on the count of base pairs around the essential stems of the pseudoknot, and the dot-bracket representation with the highest count of base pairs surrounding the pseudoknot usually corresponds to the minimum free energy; (iii) the partition function method [45], which assumes that the true base pairs should have a high probability of forming in the estimated minimum free energy distribution, thus improving accuracy by considering the free energy of their nearest neighbors at a given temperature; (iv) comparative sequence analysis [46], which involves analyzing the substitution pattern in a pairwise alignment of two homologous sequences; (v) physical experiments [47], which entail conducting wet experiments.  
In this research, we propose a hybrid optimal tree selection model that combines principles from the two most common methods, maximum pairing and MFE methods, to accurately and efficiently predict the RNA secondary structure, including the complex motif of the L-type pseudoknot. The MFE approach is computationally efficient. All trees are initially ranked by the count of base pairs surrounding the recognized pseudoknot, and MFE is applied only to the trees with the highest base pair count. This heuristic surpasses the original MFE approach.  
To identify the optimal secondary structure, our approach utilizes the minimum free energy as a criterion. We integrated a module from HotKnots [48] that calculates the energy of each structure and provides it to our framework for the ultimate choice. It uses the energy calculation algorithm proposed by [PERSON] [49] and modified for pseudoknots by [PERSON] [50].
## 3 Results
### Data Balancing: Application of Enhanced Sampling Methods  
In reservoir exploration, lithologies such as siltstone (S), fine sandstone (FS), and coarse sandstone (CS) represent crucial oil-bearing reservoirs. However, based on the preprocessing results above, the sample counts for these three lithology types are less than half of the total dataset size. To obtain more comprehensive information at the data level and train models for more accurate predictions of these relevant reservoirs, it is essential to synthesize abundant and reliable synthetic data for these lithology classes through data balancing methods. Therefore, synthesizing enriched and dependable simulated data for these classes via data balancing methods is a crucial task.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lithology** & **Label** & **Count** & **Proportion (\%)** \\ \hline Mudstone & M & 13,307 & 46.26 \\ Fine Sandstone & FS & 6830 & 23.75 \\ Silstone & S & 4716 & 16.39 \\ Pebbled Sandstone & PS & 2855 & 9.93 \\ Coarse Sandstone & CS & 1055 & 3.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of samples for each lithology label in the dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **GR** & **CAL** & **RD** & **RS** & **AC** & **DEN** & **CNL** \\ \hline count & 28,763 & 28,763 & 28,763 & 28,763 & 28,763 & 28,763 \\ mean & 79.00 & 10.23 & 4.00 & 3.49 & 82.62 & 2.36 & 23.32 \\ std & 15.36 & 1.27 & 2.58 & 1.81 & 10.26 & 0.12 & 6.
## 3 Data and Method
### Spatial Lag Model (SLM) with Fixed Effects  
The Hausman test indicates the need to incorporate a fixed effect into the selected model. Additionally, the [PERSON], Song, and Koh LM2 marginal test suggests the essential inclusion of a spatial lag. Consequently, this study adopts the Spatial Lag Model (SLM)([PERSON] et al., 2021). The SLM, also known as the Spatial Autoregressive Model (SAR), is a fundamental component of spatial econometric models. It focuses on incorporating spatial dependence by including a spatially lagged dependent variable. SLM or SAR is a robust econometric model that has been insightfully used to analyze a variety of outcomes, including economic growth ([PERSON] et al., 2016; [PERSON] et al., 2020), crime ([PERSON] et al., 2023), COVID-19 cases ([PERSON], 2020), and pollution ([PERSON] et al., 2019). In this model, the dependent variable for each spatial unit is regressed not only on the independent variables but also on the values of the dependent variable of nearby provinces (see Equation (1)).  
\[Y_{it}\ =\lambda WY_{it}\ +X_{it}\beta+\alpha_{i}+\gamma_{t}\ +\varepsilon_{it} \tag{1}\]  
where \(Y_{it}\) is the dependent variable for province \(i\) at time \(t\). \(\lambda\) is the coefficient for the spatial lag. \(W\) is the spatial weights matrix. \(X_{it}\) is a matrix of independent variables for province \(i\) at time \(t\). \(\alpha_{i}\) is the province-specific fixed effect. \(\gamma_{t}\) is the time-specific fixed effect. \(\varepsilon_{it}\) represents the error term for each province at each time point.  
The inclusion of temporally lagged dependent and independent variables was considered. However, this approach posed a potential risk of exacerbating multicollinearity. To address these concerns while still exploring the temporal dynamics of the data, the SLM model was adapted to include temporally lagged independent variables, resulting in SLM\({}_{t}\). In the SLM\({}_{t-1}\) model, sets of independent variables from previous time periods (\(t-1\)) were used to predict outcomes in subsequent periods. For instance, in SLM\({}_{t-1}\), sectoral Gross Provincial Product (GPP) data from 2020 were utilised to model the average household income of 2021. This approach of employing separate temporally lagged models allows for an enhanced understanding of the temporal impact of economic sectors on average household income, providing valuable insights into the temporal dimension without further complicating the model structure. Additionally, one-period temporally lagged independent variables are used as instrumental variables, a technique that is elaborated upon in the following section. This method further incorporates the temporal dimension into the model without presenting more independent variables.
## 2 Materials and Methods
### LR-115 Detectors  
For measurement, a cellulose nitrate detector (LR-115 provided by Dosirad, France) was used, placed in diffusion chamber. At the end of each measurement, the detectors were brought to the radioactivity laboratory at Department of Physics \"[PERSON]\" of Federico II University (Naples), where the students performed the procedure for the traces analysis left by the alpha particles emitted on the LR-115 films [18]. As such, the students proceeded step by step to (i) disassemble the detectors, (ii) label them, (iii) prepare the chemical etching (NaOH 2.5M), (iv) stripping, (v) dry the films and prepare them for reading, (vi) acquire the traces, and finally (vii) calculate the radon concentration. Further details on the procedure are given in [19]. For each LR-115, an area of 2.25 cm\({}^{2}\) was selected and the residual thickness, in terms of brightness (due to chemical etching) and trace count (due to alpha decay) were then measured. For these parameters, an optical method with a dual illumination scanner was used and the processing of the acquired images was performed with the image processing software ImageJ (Image Processing and Analysis in Java, version 1.46r, National Institutes of Health, (Bethesda, MD, USA). The brightness was converted into residual thickness through a previously determined calibration curve, while the density of the detected traces was calculated by subtracting a number of background traces equal to 10 traces/cm\({}^{2}\). Finally, the radon concentration was computed using Equation (1):  
\[C_{Rn}=\frac{R_{6.5}}{e\times h} \tag{1}\]  
where \(C_{Rn}\) is the radon activity concentration measured (expressed in Bq/m\({}^{3}\)), \(R_{6.5}\) is the track density corrected by background track density and normalized to the nominal thickness of 6.5 \(\upmu\)m, \(e\) is efficiency (see [19] for more details), and \(h\) is time (expressed in hours).
## 4 Accident Control Strategy
### The Movement toward Accident Countermeasures
#### 4.3.3 Level 3 Accident  
The core principle for dealing with a Level 3 accident is to eject the sludge feedstock, and then use large amounts of water to clean the pipelines and facilities. If there is no electricity supply, the ejecting and cleaning process is hard to achieve.  
1. Pipeline-Leak Accident  
The rupture of pipes is owing to the corrosion of component materials. Differently from the accident caused by the opening of the relief valve, the rupture of a pipe leads to the irreversible pressure loss of the system. As a result, large amounts of feedstock are ejected from the cracking place. To handle this issue, the valves at the inlet and the outlet of the pipe should be closed immediately. Meanwhile, the valves V3, V4, V6, V9 and V10 are closed, to divide the system into three high-pressure parts, as shown in Figure 4. The next step is to discharge the high-temperature and high-pressure feedstock and effluent to the underground tank (16), slowly opening the valves V11, V12 and V13, one by one, releasing the feedstock from every part. The opening degree of valves V11, V12 and V13 can be controlled, and the decreasing rate in the pressure of each part is controlled to about 5 MPa/min. When the discharging process is finished, the valves V11, V12 and V13 are closed and the valves V3, V4, V6, V9 and V10 are opened; then the pump (3) is started, to transport cool water into the system to clean pipelines and facilities.  
2. Power Outage Accident  
Generally, a power outage accident could not happen in the commercial SCWO plant because it is equipped with a dual power-supply system. However, some extreme situation such as an earthquake, typhoon or flood may lead to the failure of both ways of power supply. Therefore, a special process is designed to handle the power outage accident.  
The first step is to close the valves V3, V4, V6, V9 and V10 by hand, to divide the system into three high-pressure parts. Then, slowly open the valves V11, V12 and V13 to discharge the high-temperature and high-pressure feedstock and effluence into the underground tank (16). This procedure should be carried out by hand because of the outage of electric power. Finally, open the valves V15, V16 and V17 to let the cooling water from the overhead water tank (15) clean the pipelines and facilities. The cleaning water is injected into the system via the entrance after the pump (3). Finally, the cleaning water flows in o the underground tank (16) for storing.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
## 5 Effect of Other Elements
### Influence of Ce and Nb Content  
It is widely accepted that the deoxidization and desulfurization function of Ce may coarsen inclusions, and thus lead to a decrease in the number of inclusions. [PERSON] et al. stated that in NGO electrical steels, Ce and Al have a significant influence on grain size during stress relieving annealing of NGO electrical steels [81]. [PERSON] and [PERSON] have observed that Ce also affects the texture evolution of the material [23]. The relations between cerium on the intensity of <100>/RD texture and <111>//ND texture are shown in Figure 10.  
Figure 10: Effect of Cerium (Ce) on the intensity of (**a**) <100>//RD texture, and (**b**) <111>//ND texture. Hot-rolling finishing temperature: 890 \({}^{\circ}\)C; and hot band annealing temperature: 700 \({}^{\circ}\)C [23].  
Figure 9: Boron (B) effect on annealed cold-rolled steel sheets: (**a**) grain size, (**b**) flux density and core loss [77].  
Based on the electronegativity difference of Nb, Fe, and Si, Nb-rich precipitates will destroy the ordered rearrangement between adjacent Fe and Si atoms in the matrix, thereby inhibiting an ordered transformation in high silicon electrical steel. This will also cause strong lattice distortion when Nb atoms enter the lattice of the Fe-Si matrix. As a result, the addition of a small amount of Nb element can significantly improve the plasticity of high silicon electrical steel [82; 83].
## 5 Conclusions  
1. The aim of this paper was to address the issue of the difficulty in balancing the detection accuracy and speed of traditional obstacle detection algorithms. This paper proposes an improved YOLOv8s lightweight obstacle detection network model, which optimizes and improves the actual-time and accurate detection of obstacle detection during the driving of unmanned engineering machinery in open-pit mine environments. The improved CGC2f module was added to the YOLOv8s backbone to strengthen the context characteristic fusion and reduce the model calculation load. The ESE module was embedded in the neck to improve the model's attention to key features. The DyHead module was used in the head to raise the model's ability to perceive. Finally, the improved YOLOv8s network model was pruned using the LAMP algorithm to further reduce the redundancy of model parameters and improve the model's detection speed.
2. Experiments were conducted on a constructed open-pit mine driving obstacle dataset. The findings demonstrated that the improved algorithm's mAP50 reached 95.3%, an increase of 1.1% compared to the original network. At the same time, the number of model parameters decreased by 71.2%, the quantity of computation was lowered by 73.7%, and the FPS was increased by 40.2%. The algorithm can meet the real-time and accurate detection of obstacles during open-pit mine driving and is more conducive to deploying the model in the obstacle avoidance system of unmanned engineering machinery.
3. Although the model meets the requirements of real-time high-precision detection and is conducive to deployment on mobile terminals, the conditions in real open-pit mines are complex. Different degrees of light and dust will affect the detection effect. At the same time, there is interference from environmental changes and factors such as the vibration of the deployed equipment itself, which are uncontrollable. This poses a challenge to the computer vision algorithm model. In order to overcome these challenges, in the future, we will collect real open-pit mine environment sample data in different seasons, periods, and operating areas, covering light, dust, weather, and camera vibration parameters, build a dataset to train and verify the model, optimize its parameters and structure, and combine multi-sensor fusion technology to enhance adaptability to complex environments.  
Methodology, B.L. and W.X.; software, B.X.; resources, B.X. and H.Z.; data curation, Y.X. and W.Z.; writing--original draft preparation, B.X.; supervision, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Science and Technology Major Special Project, grant number AA23062077; Special Fund for Central Guidance of Local Science and Technology Development, grant number 2023 PRJ0101; Guangxi Science and Technology Project, grant number AD22080042.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.