esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Hyperparameter Optimization  
In developing an effective tomato leaf disease classification model, several key hyperparameters must be carefully selected and optimized. The main hyperparameters include the training epochs, loss function, optimizer, learning rate, and batch size.  
The selection of the learning rate and batch size is critical, as these hyperparameters have a significant impact on model performance. Given the multitude of possible combinations, this research combined Grid Search [40] with 5-fold cross-validation [41] to systematically identify the best learning rate and batch size for the model. Grid Search thoroughly examines all possible combinations of these hyperparameters to ensure that no potentially beneficial configurations are missed. To save time, a shortened dataset consisting of randomly selected images from the full dataset was used instead of the entire dataset. This focused approach helps efficiently manage computational resources.  
The heatmap in Figure 7 shows the results of testing different combinations of learning rates and batch sizes for the model based on 5-fold cross-validation. Each value in the heatmap represents the median performance across the five validation runs. The heatmap indicates that the best configuration for the model is a learning rate of 0.0001 and a batch size of 16, which was implemented in the proposed model.  
To achieve a balance between sufficient learning and the prevention of overfitting, 50 epochs were selected for training. This number of epochs allows the model to adequately learn from the training data while minimizing the risk of overfitting, which could reduce its ability to generalize to unseen data.  
The loss function measures how well the model's predictions match the actual labels. For this research, the sparse categorical cross-entropy loss function was chosen. This loss function is particularly suitable for multi-class classification problems like the one in this research.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer (Type)** & **Output Shape** & **Param \#** \\ \hline MobileNetV2 (Functional) & (None, 4, 1280) & 2,257,984 \\ reshape\_3 (Reshape) & (None, 4, 1280) & 0 \\ cf\_c\_4 (CfC) & (None, 36) & 217,568 \\ dense\_3 (Dense) & (None, 11) & 407 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The proposed model implements a layered structure for the tomato disease classification task.  
The Adam optimizer [42] was selected for training the model. Adam, which stands for Adaptive Moment Estimation, combines the advantages of two other popular optimizers: AdaGrad and RMSProp. It computes adaptive learning rates for each parameter, improving training efficiency and convergence speed.
## 4 Validation and Application
### Validation Example 2: Impact between Two Elastic-Plastic Blocks
The contact calculation time is about 43 s, and it takes up about 26% of the total calculation time (about 168 s). The contact calculation consumes a considerable part of the total calculation time. With the use of the striped point-in-box search algorithm [28] instead of the tree search algorithm [19] for neighboring particle searching, the total calculation time can be reduced to 96 s, and the contact calculation time takes up about 45% and becomes a major part of the total calculation time. A rough comparison of computational cost is also made between the FEM simulation and the SPH simulation, although there are differences in the time-stepping algorithm, contact searching domain, output of result files and other aspects. The computational cost of the FEM simulation with the previously mentioned settings is about 6 s of CPU time. Compared with the FEM simulation, the SPH simulation is more computationally expensive for the current example. The main reasons for this include expensive neighboring particle searching and considerable particles required for the calculation of a particle of interest in the SPH method. Another reason is that the surface reconstruction required in the present contact algorithm brings significant extra computational costs. Thus, to save the computational cost of the present contact algorithm, and consequently of SPH simulation, it is of interest to develop intermittent and/or local surface reconstruction techniques.
###### Abstract  
This article addresses the design of a robust H\({}_{2}\) state feedback controller (H\({}_{2}\)-SFC) for the effective temperature control in the heating zone of the steel slab reheating furnace. Based on the available field data and system identification procedures, a mathematical model of the heating zone is derived, which presents autoregressive-moving average with exogenous input (ARMAX) structure and fourth order. The design of an H\({}_{2}\)-SFC controller for the effective control of the heating zone temperature of the slab reheating furnace under study is developed. The simulation results of the designed control system showed its high effectiveness compared to the conventional PID control.  
robust H\({}_{2}\) state feedback controller (H\({}_{2}\)-SFC); temperature controller; system identification; mathematical model; ARMAX structure
# Advances in the Metabolic Engineering of _Escherichia coli_ for the Manufacture of Monoterpenes
## 2 Engineering Endogenous MEP Pathway in Prokaryotic Chassis
Subsequently, they overexpressed DXS and IDI through plasmid transient transformation, which could enhance the production of limonene ultimately to 17.4 mg/L at 48 h. After a series of modifications to optimize the two-phase culture medium, the titer of limonene continuously elevated up to 35.8 mg/L, approximately 7-fold greater than the initial yield [30]. Despite the significant improvement in monoterpene biosynthesis, the wide applications of MEP pathway engineering are limited because of the presence of inherent regulation mechanisms and the unknown physiological control elements in the host cell, which cause the bottleneck of monoterpene production efficiency [45].
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 3 Experiments and Results
### Applicability Analysis of Methodology
With an increase in the resolution difference, the errors gradually increased, and the correct matches decreased. The experimental results indicate that the ETM+ images with a 15 m resolution are efficient candidates to match the ZY-1-02C 5 m resolution images and obtain good matching results. Thus, the ETM+ images can be used as georeference images to improve the global positioning accuracy of ZY-1-02C images.
## 2 Study Area  
### Location and Climate  
Adra town (36\({}^{\circ}\)44\({}^{\prime}\)30\({}^{\prime\prime}\)-36\({}^{\circ}\)45\({}^{\prime}\)30\({}^{\prime}\) N, 3\({}^{\circ}\)00\({}^{\prime}\)-3\({}^{\circ}\)02\({}^{\prime}\) W) is located at the outlet of the Adra River basin (ARB) in the province of Almeria in southern Spain (Figure 1a). This coastal town has a surface of 1.82 km\({}^{2}\) and is crossed by small temporary streams that flow to the sea (Figure 1b). From a geomorphological point of view, these urban basins delimit a peri-urban area (2.64 km\({}^{2}\) in the 50-436 m a.s.l. elevation range) that influences the hydrology of a fraction of the urban area (0.97 km\({}^{2}\) in the 0-50 m a.s.l. elevation range) downstream [15]. The peri-urban area is divided into a southern sector (1.41 km\({}^{2}\) in the 50-150 m a.s.l. elevation range) devoted to irrigation agriculture in greenhouses and a northern sector (1.23 km\({}^{2}\) in the 150-436 m a.s.l. range) with low human influence (Figure 1c).  
Climate is warm-summer Mediterranean according to the Koppen classification [36], which means a semiarid regime with hot dry summers and temperate rainy winters [14]. Insolation is high, 2900 h per year in low-lying places.  
The 0.01\({}^{\circ}\) (\(\sim\)1-km) resolution nodal daily precipitation and temperature (maximum and minimum) series from the Iberia01 grid over the period 1971-2015 [37] was used to deduce weather conditions. Precipitation (P) occurs in three distinctive phases, each about four-month long as (1) a predominant rainy phase from October to January which represents around 50% of annual P; (2) a moderately rainy phase from February to May which means around 40% of annual P; and (3) a dry phase from June to September which records around 10% of annual P. Extreme rainfall events over 80 mm per day have been documented. Temperature (T) shows a bimodal distribution, each period about six-month long as (1) a warm period from May to October with average minimum and maximum monthly T of 15 \({}^{\circ}\)C in October and 31 \({}^{\circ}\)C in July, respectively; and (2) a temperate period from September to April with average minimum and maximum monthly T of 9 \({}^{\circ}\)C in January and 20 \({}^{\circ}\)C in April, respectively.
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
## 4 Advances in the Adsorbent Type Used in the Extraction Phase of the SPME Fiber
### Titanium Oxide Nanoparticles as Sorbents for SPME
To improve the analytical response, the study added 15% (\(m/v\)) salt and optimized the incubation (15 min), extraction (15 min), and desorption (10 min) periods. With dynamic ranges of 20-500 \(\upmu\)g/L for trichloromethane, 15-250 \(\upmu\)g/L for dichlorbromomethane and dibromochloromethane, and 10-100 \(\upmu\)g/L for tribromomethane, the method showed excellent linearity for THMs in water. The reliability of the approach was confirmed by the fact that all compounds had correlation coefficients more than 0.9900 [99]. Additionally, [PERSON] et al. used a sol-gel technique to create a graphene-supported ZnO SPME fiber. ZnO was nucleated by graphite oxide (GO), and the resulting ZnO/graphene composite was integrated into a long-lasting sol-gel network. Adsorption sites were improved by the addition of graphene, which raised the BET surface area from 1.2 m\({}^{2}\)/g to 169.4 m\({}^{2}\)/g. With an enrichment ratio of 1087 for 1-octanetthiol and less than 200 for non-sulfur compounds, the ZnO/graphene fiber demonstrated exceptional sensitivity and selectivity for sulfur compounds. It performed better than PDMS/DVB fibers and commercial PDMS. The fiber efficiently examined sulfur volatiles in Chinese chives and garlic sprouts when combined with GC-MS, achieving recoveries of 73.2-91.6%, RSDs of 3.6-9.1%, and detection limits of 0.1-0.7 \(\upmu\)g/L [100]. While many metal oxide nanoparticles (MONPs) are suitable for SPME applications, this review concentrates on three of the most notable. Many other MONPs have significant features for this purpose, but we focused on a few to emphasize their distinctive contributions.
## 1 Introduction
Currently, four methods are used to calculate the correction force. The free-resonance method establishes a mapping relationship between the free-resonance modes of the primary mirror and the correction force. Then, the free-resonance modes are used to fit the surface error of the primary mirror, thereby determining the correction force [21,22]. This method is generally applied to thin primary mirrors rather than lightweight honeycomb mirrors. The second method, called the direct least squares method, establishes a mapping relationship between the force and deformation of the primary mirror surface through an interaction matrix, thereby determining the correction force [23]. The correction force obtained with this method is typically relatively large. The third method, called damped least squares or constrained least squares, is an improvement on the direct least squares method, which introduces damping factors to limit the amplitude of the correction force [24]. This method is relatively complex and requires optimization to obtain the optimal damping factors. The fourth method, called the bending-mode method, generates several bending modes through the singular value decomposition (SVD) of the interaction matrix. Then, the primary mirror surface shape error is fitted with several lower-order bending modes, thereby determining the correction force [25]. Considering that the bending-mode method avoids the problems of the first three methods, we use this method to solve the correction force. However, current research on bending modes [25,26,27] has ignored the issue of how to reasonably select the orders of bending modes. Therefore, this article investigates the relationship between the orders of bending modes used in surface shape correction and the correction effect, enabling the system to achieve a higher primary mirror surface shape accuracy with a smaller correction force. The main contributions and innovations of this article are as follows: (1) The design and performance evaluation of an active support system for a 4m SiC primary mirror is achieved and the system can achieve good surface accuracy. (2) A method for compensating for the force on the hardpoints is proposed to solve the problem of protrusions and dents on the primary mirror surface caused by the force on hardpoints. (3) The bending-mode method is deeply studied, the relationship between the order of the bending modes used in the surface shape correction and the correction effect is investigated, and the active optics correction method based on the bending mode is improved. The remainder of this article is organized as follows: Section 2 explains the principle and structure of the designed 4m SiC primary mirror active support system. Section 3 discusses the surface and pose correction methods for the mirror and proposes a compensation method for the force on the hardpoints. Section 4 evaluates the performance of the designed system and tests its performance under conditions such as gravity, thermal load, and wind load. Section 5 provides the conclusion.
## 3 Case Study  
### Project Overview and Data Description  
The proposed method is verified using the monitoring data of a real concrete gravity dam, which has a maximum height of 107.5 m, a dam crest elevation of 230.5 m, and a total length of 1039.1 m. The upstream and downstream views of the dam are shown in Figure 4. As shown in Figure 5, the dam is divided into 56 dam sections. Sections 1 to 18 form the right bank non-overflow dam section, sections 45 to 56 form the left bank non-overflow dam section, sections 19 to 22 form the powerhouse dam section, and sections 23 to 44 form the overflow dam sections. In this study, nine measurement points are selected at the same elevation, and four measurement points are selected at different elevations, respectively. Among them, measurement points YZ15-1, YZ16-1, and YZ17-1 are selected on the right bank, measurement points YZ46-1, YZ47-1, and YZ48-1 are selected on the left bank, measurement points YZ20-1, YZ21-1, and YZ22-1 are selected on the intermediate power station dam section, and measurement points DC23-1, DC23-2, DC42-1, and DC42-2 are selected at different elevations on the overflow dam section. The dataset selected for this paper consists of 1461 sets of displacement, water level, and temperature measured from 1 January  
Figure 6: The process lines of environmental variables and measuring displacements.  
Figure 7: Specific hysteresis time during which the temperature affects the displacements.  
Correlation analysis is the process of examining the relationship between two or more variables, with the aim of quantifying the strength of their association. This analysis is employed to explore the underlying patterns or rules that govern the variation of these variables. Figure 8 presents a correlation heat map, illustrating the displacements of disparate measurement points. These displacements have been calculated utilizing the maximum information coefficient method [57]. As evidenced by the figure, the correlation coefficient between each measurement point is positive. This suggests that the displacements of the measurement points exhibit a positive correlation. However, the correlation between the measurement points of different regions is expected to be low. It is evident that a high correlation coefficient exists between the displacements of measurement points in the same block, which can be maintained above 0.9, indicating a high degree of positive correlation between the displacements of the same block.  
All the simulation calculations covered in this paper are computed on a Windows 11 computer equipped with an AMD Ryzen 7 6800H @ 3.20 GHz CPU, 16.0 GB of RAM, and a 64-bit processor.
## 1 Introduction
A secondary high-energy neutron collides with an atom of the semiconductor, which produces an ionization charge that can cause an unfavorable reaction of a semiconductor device. To prepare adequately for such errors, a reliable, well-verified model that can predict the probabilities of the hits is needed. Regarding observations of cosmic radiation intensities related to the fields above, ground-based neutron monitors have played an important role for up to 70 years. The ground-based neutron monitors detect secondary particles produced in the atmosphere as a product of spallation reactions caused by primary cosmic-ray particles. While two types of standardized detectors (IGY and NM64) are generally operated, both types consist of gas-filled proportional counters surrounded by a moderator, lead producer, and reflector [39]. The incident cosmic protons and neutrons cause nuclear reactions in the lead, and the secondary neutrons slow down by the moderator, and thermal-energy neutrons are detected by the proportional counter tubes. Many stations of the ground-based neutron monitors were built worldwide since the 1950s and have been operated for space sciences and space weather applications [40]. The monitoring data acquired at these stations are publicly available in some repositories and data sources to observe the cosmic-ray variations and incident energetic solar particles, and also to estimate the global-scale change of cut-off rigidity, i.e., the minimum magnetic rigidity that a vertically incident particle can have and still reach a given location above the Earth [18]. While the ground-based neutron monitors have provided accurate and stable data, these large-scale instruments can hardly be carried and thus have been employed for fixed-point observations. It should be noted that, in addition to the solar activities and global geomagnetic field condition, local meteorological factors as well as the atmospheric pressure, such as clouds and snowfall, could affect the cosmic-radiation intensities at a specific site. Moreover, a notable change in the Earth's geomagnetic field originatingfrom the core has lately been observed [41; 42]. According to the awareness on the spatial variation of cosmic-radiation intensity, we have investigated the application of a portable neutron-monitoring system composed of an extended-energy-range neutron monitor and a small data logger for model verifications, with the on-site measurements covering a wide area of interest. The designed monitoring system is preferably to be tested in a polar region where the cosmic-radiation intensities are the highest on the Earth.
## 3 Illustration of the Proposed Method  
The proposed methodology is illustrated in this section by application to a small generating system with four units committed for a lead time of 1 h. The unit capacity rating \(MCR\), loading capacity \(c_{L}\), failure rate \(\lambda\), and ramp rate \(\gamma\) are shown in Table 1. The regulating margin (RM) shown in the last column is calculated using (4) considering a response time \(T_{R}\) of 10 min. The total regulating margin is 30 MW. This will however be reduced by disturbances caused by unit outages. The unit commitment shown in Table 1 meets the N-1 criterion, as the spinning reserve of 36 MW is equal to the MCR of the largest unit.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Units** & **MCR** & **Failure Rate** & **Ramp** & **Loading \(CL\)** & **Spinning Reserve** & **RM** \\ \(j\) & **(MW)** & **\(\lambda\) (\(f\)/yr)** & **Rate \(\gamma\) (MW/m)** & **(MW)** & **(MW)** & **(MW)** \\ \hline
1 & 20 & 1 & 2 & 7 & 13 & 13 \\
2 & 36 & 1 & 1 & 20 & 16 & 10 \\
3 & 11 & 1 & 1 & 4 & 7 & 7 \\
4 & 33 & 1 & 1 & 33 & 0 & 0 \\ Total & 100 & & & 64 & 36 & 30 \\ \hline \end{tabular}
\end{table}
Table 1: Example operating system consisting of four generating units.  
There exist \(2^{4}=16\) contingences of unit outages, as shown in Table 2, considering 2-state generation models. The ORR for each unit in Table 1 is calculated using (1) for a lead time of 1 h and used in (2) to obtain the discrete probability distribution of capacity outages shown in the second and fourth columns of Table 2. The reduction in operating capacity \(X_{k}\) obtained from (3) are shown in the third column, and system response capacities or available regulating margins \(\text{RM}_{k}\) are obtained from (4) on the fifth column of Table 2. The conditional response risk for each capacity outage condition shown in the sixth column are obtained using (6), and the CGRR shown is calculated using (7).  
The obtained CGRR value of 0.00022837 is the total probability that generating units committed for the given operating decision will not be able to respond with adequate generation capacity to satisfy the system load within acceptable response time following major disturbances that can occur, considering all contingencies of generation unit outages. It should be noted that this risk can be calculated prior to making the operating decision on unit commitment and load dispatch, and the risk can be varied by an alternate decision to meet an acceptable criterion.
## 4 Methodology
### Boosting Approaches
```
XGBoost Algorithm Input: \(\left\{(x_{j},y_{j})\right\}_{j=1}^{M}\) training set Input: N tree (weak learners) Input: \(L(y_{j},\ F(x))\) differentiable loss function Input: a learning rate Model initialization by using a constant value: \(F_{i}^{(0)}(x)=\frac{\text{arg}min}{\phi}\sum_{j=1}^{M}L(y_{j},\ \phi)\) for\(k=1\) to \(N\)
1. Compute gradients and hessians: \[g_{k}(x_{i}) =\left[\frac{\partial L(y_{j},f(x_{i}))}{\partial f(x_{i})} \right]_{f(x)=f_{k-1}(x)}\] \[h_{k}(x_{i}) =\left[\frac{\partial^{2}L(y_{j},f(x_{j}))}{\partial f(x_{j})^{2} }\right]_{f(x)=f_{k-1}(x)}\]
2. Fit a tree by means of the training set \(\left\{\left(x_{j},\frac{\theta(x_{j})}{h_{k}(x_{j})}\right)\right\}_{j=1}^{M}\) by solving the following optimization problem: \(\phi_{k}=\frac{\text{arg}min}{\phi\in\Phi}\sum_{j=1}^{M}\frac{1}{2}h_{k}(x_{j })\bigg{[}-\frac{\theta(x_{j})}{h_{k}(x_{j})}-\phi(x_{j})\bigg{]}^{2}\) \(f_{k}(x)=a\phi_{k}(x)\)
3. Update the model: \(F_{i}^{(k)}(x)=F_{i}^{(k-1)}(x)+f_{k}(x)\) end Output: \(F_{i}^{(N)}=\sum_{k=0}^{N}f_{k}(x)\)
```  
**Algorithm 1** Pseudocode of the unregularized XGBoost algorithm XGBoost is a high-performance algorithm, in fact, it can be implemented on parallel architectures and its training is very fast [21; 35; 36; 37]. Additionally, XGBoost has an embedded routine that optimizes the management of missing values.