esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Model Simulation Values  
The LPI-GUESS model has been widely used to estimate the impacts of climate on terrestrial ecosystems [36; 37; 38; 39]. In light of this, our study simulated NEE and GPP using the LPI-GUESS model, achieving a level of accuracy comparable to previous research reports, thereby ensuring the reliability of our results [26]. The NEE and GPP in our study area showed obvious seasonal changes (Figure 6). It is reasonable that the NEE values are negative during the growing season because vegetation growth can assimilate carbon [40]. The maximum carbon assimilation rates largely occurred in July or August because during this period, the air temperature was high, and the solar radiation was strong in the Arctic region. From April to July, the GPP values also increase rapidly, and this pattern clearly demonstrates the seasonal changes in vegetation growth. In the tundra ecosystem, GPP began to increase from early to mid-June and peaked from late July to early August. After the peak values in summer, GPP began to decline. In the tundra ecosystem, the seasonality of ecosystem respiration was less obvious [41]. Thus, the seasonality of NEE was determined by GPP rather than ecosystem respiration in tundra ecosystems. Compared with the tundra, GPP and ecosystem respiration in boreal forest ecosystems show similar seasonality, peaking between late June and mid-July. Although the specific peak times of GPP and NEE vary from site to site, they show similar seasonal variation [42].  
Our simulation results showed that the NEE at the needle leaf forest site was lower than that of the tundra, indicating that the needle leaf forest is a stronger carbon sink during the growing season. Similarly, the GPP of the needle leaf forest site was higher than that of the tundra site (Figure 5). Compared with tundra ecosystems, forest ecosystems have richer biodiversity, more complex structural levels, and higher productivity, and their annual  
Figure 10: Comparison of LAI value between model simulation and ESM output at the ATQ tundra site ((**a**) (SSP1-2.6), (**c**) (SSP2-4.5), (**e**) (SSP5-8.5)) and PRR needle leaf forest site ((**b**) (SSP1-2.6), (**d**) (SSP2-4.5), (**f**) (SSP5-8.5)) in SSP scenario.  
carbon sequestration capacity is higher [43]. [PERSON] et al. showed a comparison of NEE and GPP simulations using 10 NACP site synthesis models in 2002 and 2003 in Alaska tundra sites. NEE reached its lowest value, and GPP reached its highest value around July and gradually approached zero in winter. The terrestrial biosphere model was used for the simulation; GPP was 220 \(\pm\) 500 gC/m\({}^{2}\)/yr, and NEE was 10 \(\pm\) 190 gC/m\({}^{2}\)/yr, which were consistent with the results of this study [44].
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 5 Experimental Results and Comparison
### Lane Detection Dataset
#### 5.1.2 Gradient Boosting  
The classifier ran the model through the data after adjusting certain parameters. These parameters were specifically for the gradient boosting classifier used in Python. The number of estimators and the learning rate were the parameters that were run over a range of values, and the parametric values that gave the best accuracy results (number of estimators = 400 and learning rate = 0.015) for the gradient boosting classifier on the data were used to determine the remaining evaluation metrics.  
The precision-recall curve and ROC curve exhibit very good representations of the results. They are presented in Figure 4 and Table 2.  
Two characteristics stand out here: the classes have a consistency in their precision-recall curves and their relation is almost linear, giving a good transition from high precision to high recall. This therefore results in a good F1-score. More importantly, it is observed that the lowest precision-recall curve and correspondingly lowest F1-score are found in the aggressive class (class 1). This is understandable given the smaller set of samples in the aggressive state.  
The inclination of all the ROC curves in Figure 4b to the top left corner indicates a high performance. This is supported by the area values for each ROC curve. The high scores in each class also point to how well the algorithm has adapted to the different classes and show encouraging results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
**Class 0 (normal)** & 0.44 & 0.67 & 0.53 & 40,320 \\
**Class 1 (aggressive)** & 0.55 & 0.03 & 0.05 & 35,752 \\
**Class 2 (drowsy)** & 0.53 & 0.69 & 0.60 & 42,206 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-class evaluation metrics with logistic regression classifier for lane detection.  
Figure 4: Gradient boosting simulation results: (**a**) precision–recall curve; (**b**) ROC curve.  
The comparable precision, recall and F1-scores overall are very encouraging because they point to a high likelihood of having avoided both under-fitting and over-fitting.  
It can be further noticed that the scores are also consistent among each class. However, the one anomaly that remains is the relatively low scores of recall and precision for class 1 (aggressive). They are still much improved compared to the results from logistic regression. The lower scores in this class, as discussed above, most likely stem from the fewer sample points that were available for this class.  
The high micro-average precision-recall score, in Figure 4b, helps us to understand that the model has performed well on such an imbalanced data in term of sample points per class.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 4 Network Traffic Forecasting Based on ARIMA-BPNN
### IGWO-Based BPNN Parameter Optimization  
In the proposed ARIMA-BPNN hybrid model, numerous hyperparameters of BPNN may result in unstable results for residual forecasting. The grey wolf optimization (GWO) algorithm has been proven to have a reasonable global optimum search mechanism for parameter optimization problems [12]. Therefore, GWO is utilized to optimize the hyperparameters of the BPNN in this paper. However, standard GWO has the disadvantages of an uneven initial population distribution and slow converge rate. To address these issues, GWO is improved by adjusted the population initialization strategy and convergence factor to accelerate the convergence. Finally, the improved GWO (i.e., IGWO) is utilized to optimize the hyperparameters of the BPNN.  
Figure 5: Modeling steps of the adaptive fixed\(-\)order optimization searching operator.
## 2 Materials and Methods
### Methods
#### 2.2.3. Sliding Wear Test  
As illustrated in Figure 4, sliding wear was tested using a pin-on-disk device under dry sliding conditions to measure the coefficient of friction and wear rate. Three identical specimens were tested on each examined condition. It is noteworthy that friction and wear are properties of the whole system, not just the materials. In particular, the surface roughness greatly influences the results. However, in this work, the roughness for all sliding surfaces of testing specimens, as well as the sliding disk, was the same to allow the influence of other parameters to be investigated. Therefore, the specimens had been polished and smoothed to obtain a surface free of scratches. The surface roughness of the testing samples after polishing was 0.15 \(\upmu\)m, while the surface roughness of the sliding disk after fine grinding was 0.35 \(\upmu\)m. The roughness was determined using the Tayd surf Talyor Hobson device for surface roughness measurement. Different loads with varying  
Figure 3: Bainitic microstructure (Magnification: 136\(\times\)).  
Figure 2: Ferrite microstructure (Magnification: 136\(\times\)).  
devices, times, and microstructure parameters were applied to the specimens, after which the friction coefficients were calculated.  
To calculate the friction coefficient, Equation (1) was used [19]  
\[\mu=\frac{F}{L} \tag{1}\]  
In this equation, \(F\) represents the friction force (N), whilst \(L\) denotes the applied normal load (N)  
The wear rate was determined using the weighing method by calculating the lost weight of the samples according to the following steps:  
* Weighing the sample before each test using a sensitive balance with a sensitivity of 0.0001 g, where the weight of the specimen before the test was (3.0195 g);
* Determining the parameter whose effect is to be studied on the wear rate (e.g., sliding time) and fixing all other parameters (normal load and sliding speed);
* Installing the sample to be tested with the wear device, placing it perpendicular to the sliding disk, and then operating the device for specified periods. The piece is weighed after the test and to calculate the wear rate; Equation (2) was used [20]  
\[WR=\frac{\Delta W}{ds} \tag{2}\]  
In this equation, \(\Delta W\) denotes the losses weight (\(\Delta W=W_{1}-W_{2}\)), \(W_{1}\) represents the specimen weight before the test, \(W_{2}\) represents its weight after the test (g), and \(ds\) denotes the sliding distance (cm). Table 2 demonstrates the weight loss for one of the samples tested under specific operating conditions.  
Figure 4: Schematic diagram of the pin-on-disk device.
## 2 Materials and Methods
### Classification  
Previous preliminary analyses revealed that the Extreme Gradient Boosting (XGB) [18] algorithm performs better for the classification of plant electrophysiological data than the other tested algorithms, namely Logistic Regression, Decision Trees, Random Forest and Deep Learning [13,14]. Hence, the classification models in this study were built using XGB, which represents a gradient boosted technique optimized for speed and used computing resources. The modelling task was implemented with the XGBoost library in Python.  
The model was built on the learning set and evaluated on a separated test set. The splitting of data in training and test set followed two constraints. On one side, regarding the relatively high number of samples forming the explored dataset, the common practice suggests the training set to enclose around 80% of the dataset. On the other side, we aimed at including in both sets, an equal distribution of each stressor as well as the integrity of samples obtained from an individual plant with an objective of minimizing an eventual bias in the evaluation. Therefore, data from 30 plants were incorporated in the training set, as opposed to the remaining six in the test set. To be more precise, two represented the drought, two the infestation with spider mites, and the last two the deficiency in Mn2+ and Ca2+, respectively. For each set, the plants representing a specific stressor were randomly chosen.  
The learning procedure included tuning of several XGB parameters:  
* _max_depth_ representing the maximum depth of a tree; it took a value from the set: [3, 4, 5].
* _min_child_weight_ indicating the minimum sum of weights required for creating a new node in the tree. The tested values were [1, 2, 3, 4, 5].
* _gamma_ identifying the minimum of the loss reduction that is needed to realize a split (leading to a more conservative algorithm). It was tuned for the values [0, 0.1 and 0.2].
* _lambda_ defining the L2 regularization term on weights; it was tuned within the set: [0, 0.01, 0.1, 1]
* _alpha_ representing the L1 regularization term on weights with tested values: [0, 0.1, 1]
* _subsample_ determining the fraction of randomly selected training samples used to train each three. It was set within the values from the set: [0.8, 0.9, 1]
* _colsample_bytree_ denoting the fraction of randomly selected features used to train each tree. It took values from the set [0.8, 0.9, 1].  
Initially, the optimal number of trees was determined with a learning rate set to 0.1 whilst keeping the default values of the other parameters. In a second step, the other parameters were tuned by applying a grid search that used a custom cross validation [14] to avoid eventual bias in the evaluation of the model performance. This cross validation used 10 folds, where each one comprised the samples of three different plants. At the end, the learning rate was reduced 10 times and the final number of trees was determined by employing the already tuned parameters.
# Second-Order Text Matching Algorithm for Agricultural Text
## 3 Related Concepts and Algorithms
### BM25 Algorithm  
BM25 algorithm is the traditional method of text similarity calculation. It has been widely used in information retrieval, search engines and other fields because it is simple to implement with good effect. BM25 algorithm can calculate the similarity about all sentences. The formula for BM25 is shown by Equation (1). \(TF\) stands for word frequency. \(IDF\) stands for inverse document frequency. Specifically, \(IDF\) reflects the ubiquity of a term across the entire document collection. If a term appears in many documents, its \(IDF\) value is relatively low, indicating a weaker discriminative power across the entire document set. Conversely, if a term appears in fewer documents, its \(IDF\) value will be higher, indicating greater discriminative power and information value within the document collection. \(N\) denotes the number of documents in the index. \(Score(D,Q)\) denotes the similarity score between the query \(Q\) and the set of all documents \(D\). \(|D|\) denotes the number of documents and \(wglen\) denotes the average length of all documents. Both \(k_{1}\) and \(b\) are adjustable parameters. \(k_{1}\) limits the value of relevance, usually taken as 2 and \(b\) adjusts the effect of document length, usually taken as 0.75. In the paper, \(k_{1}\) takes the value of 2, and \(b\) takes the value of 0.75. BM25 algorithm is fast and the results are accurate, but it only takes into account the text's similarity at the word level and does not include the semantic information of the words, which does not capture the semantic similarity of the sentences well.  
\[score(D,Q)=\sum_{i=1}^{n}IDF(q_{i})\times\frac{TF(q_{S_{i}})\times(k_{1}+1)}{ TF(q_{S_{i}})+k_{1}\times(1-b+b\times\frac{|D|}{wglen})} \tag{1}\]  
where \(IDF(q_{S_{i}})=\log\frac{N-n(q_{S_{i}})+0.5}{n(q_{S_{i}})+0.5}\), \(TF_{ij}=\frac{n_{ij}}{\sum_{k}n_{ij}}\), \(q_{S_{i}}\) represents the segmentation of words in the query, \(n(q_{S_{i}})\) denotes the number of documents containing the word \(q_{S_{i}}\). \(TF_{ij}\) denotes the frequency of occurrence of the word \(i\) in the document \(j\). \(n_{ij}\) denotes the number of occurrences of \(i\) in \(j\) and the denominator denotes the sum of all occurrences of the word in \(j\).
# Ru(terpy)-Based Conducting Polymer in Electrochemical Biosensing of Epinephrine
## 2 Materials and Methods
### Influence of Interfering Substances  
The influence of common interfering substances (ascorbic acid (AA), uric acid (UA), L-cysteine (L-cys), and tryptophan (Trp)) on the determination of 1 \(\times\) 10\({}^{-5}\) mol dm\({}^{-3}\) EP was investigated. The concentration of the interfering agents was varied in a ratio (EP:interferent) between 1:1 and 1:10.
## 3 Methods
### Calculation of the Performance Loss Rate Using the YoY Approach  
We calculated PLR based on monitoring data (using sensors) and a clear sky model using NREL's RdTools. Figure 4 shows an example of degradation calculations taken from a CIGS PV module in Alice Springs in daily resolution. The time-series charts on the left-hand side show the year-on-year energy production normalized using baseline AC energy values from PVWatts calculation according to the following equation [55].  
\[\mathrm{P=}\frac{\mathrm{G_{POA}}}{1000*\mathrm{P_{0}}}(1+\gamma_{\mathrm{Pmp }}(\mathrm{T_{m}}-25^{\circ}\mathrm{C}) \tag{17}\]  
PVWatts is a free software provided by NREL. It uses typical meteorological data to generate predicted energy production.  
As shown, the annual PLR value was observed to be large enough to be visually detectable when calculated using sensor data compared to using the clear sky model. The histograms on the right-hand side present the distribution of the daily PLR values calculated by comparing a renormalized energy value of one particular date in a particular year to another PR value on the same date from the previous year. Depending on the comparison result of the energy output of two consecutive days, PLR values can be both positive and negative, as shown in the histograms. The median of the PLR values becomes the final single annual PLR value to be reported.
## 1 Introduction
[34] conducted a numerical study of Al\({}_{2}\)O\({}_{3}\)-air nanofluid laminar flow in pipes, taking into consideration the velocity slip and temperature jump. A correlation for the average Nu was developed under a constant surface temperature. The study showed that an increase in temperature jump and slip velocity led to a reduction in average Nu. [PERSON] et al. [35] conducted a numerical study of a laminar gaseous flow through a pipe under constant wall heat flux and slip flow conditions. They linked the resulting Nusselt number with Knudsen and Brinkman numbers at the developing and fully developed regions. In their comprehensive study, the effects of the addition of nanoparticles to the base fluid was not investigated. Moreover, [PERSON] et al. [36] conducted an analytical study on a fully developed laminar slip flow inside an elliptical microchannel with constant wall heat flux conditions. Their fully developed Nusselt number values were corelated to an air-specific heat ratio, as well as Knudson and Prandtl numbers. Their work failed to report effects of nanofluids at the entrance region of a circular pipe. Although there are many studies that have dealt with heat transfer enhancement through nanofluids in pipes, the use of slip flow in engineering applications is not widely accepted by the academic community. This is due to the small length scale of the slip flow regime compared with the length scale of the flow/system. Therefore, the rarefied flow of gaseous nanofluids under slip boundary conditions at the entrance region is a fundamental study on a rarely addressed limitation of the fluid flow and requires further attention. Recently, sustainable development goals (SDGs) have dictated that energy efficiency must be increased from its current level to almost double. Enhancing heat transfer within engineering systems directly supports energy efficiency goals by reducing energy consumption and fostering technological innovation across industrial sectors. In this research, a mixture of air and Al\({}_{2}\)O\({}_{3}\) flow at the circular pipe entrance region under slip boundary conditions was investigated. Developing laminar flows with different values of constant wall heat flux were simulated using the CFD approach based on the Ansys-Fluent 2018 software package. The CFD model investigated the effects of Pr, Re, wall heat flux (q\({}^{\prime\prime}\)), Kn, aspect ratio (x/D\({}_{\text{h}}\)--the distance traveled by the nanofluid along the pipe axis to the hydraulic diameter), and \(\phi\) of Al\({}_{2}\)O\({}_{3}\) on the Nusselt number (Nu), velocity ratio (U/U\({}_{\text{m}}\)),and skin friction coefficient presented by (ReC\({}_{\text{f}}\)). Based on the comprehensive CFD simulations conducted, the correlations of Nu, U/U\({}_{\text{m}}\), and ReC\({}_{\text{f}}\) as functions of other parameters were presented.
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Efficiency Analysis  
In order to evaluate the real-time performance of the proposed method, we conduct efficiency analysis from two aspects: theoretical computational complexity and actual running time test.
## 3 Validation of the Coupling Model
### The Establishment of Numerical Model  
Reference [33] studied the problem of sand production in the wellbore during oil extraction. A two-dimensional calculation model is adopted. The radius of the wellbore (inner boundary) is 0.1 m, and the outer boundary is 5 m. The calculation model is shown in Figure 3.  
The standard atmospheric pressure value is taken as the calculation reference value. The initial porosity of the model was 0.25, and the initial volume fraction of fine particles was 0.001. The boundary conditions adopted by the model are as follows: the fluid pressure at inner boundary is 5 MPa, and the fluid pressure at outer boundary is 8 MPa. The inner boundary is the outflow boundary for fine particles, and the external stress acting on the outer boundary is 20 MPa. The physical parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Wellbore(inner boundary) radius \(r_{a}\)/(m) & 0.1 \\ Outer boundary radius \(r_{a}\)/(m) & 5.0 \\ Initial porosity \(\phi_{0}\) & 0.25 \\ Maximum porosity \(\phi_{\text{max}}\) & 1 \\ Initial volume fraction of fine particles \(C_{0}\) & \(1.0\times 10^{-3}\) \\ Critical volume fraction of fine particles \(C_{cr}\) & 0.30 \\ Initial permeability \(k_{0}\)/(m\({}^{2}\)) & \(3.73\times 10^{-13}\) \\ The dynamic viscosity coefficient of the fluid \(\mu_{l}\)/(Pa-s) & \(4.2\times 10^{-3}\) \\ The density of fluid \(\rho_{l}\)/(kg/m\({}^{3}\)) & 1000 \\ The density of fine particles \(\rho_{p}\)/(kg/m\({}^{3}\)) & 2650 \\ The erosion coefficient of fine particles \(\zeta\)/(m\({}^{-1}\)) & 5.0 \\ Fluid pressure at inner boundary \(p_{l-a}\)/(MPa) & 5.0 \\ Fluid pressure at outer boundary \(p_{l-a}\)/(MPa) & 8.0 \\ Elastic model \(E\)/(MPa) & \(2.0\times 10^{3}\) \\ Poisson’s ratio \(\
u\) & 0.3 \\ External stress \(\sigma_{0}\)/(MPa) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters for numerical calculation.  
Figure 3: Illustration of wellbore erosion.
## 2 Fundamentals of Shape-Memory Polymers (SMPs) and Shape-Memory Polymer Nanocomposites (SMPNCs)  
### Definition of SMPs  
SMPs are a type of smart material and fall under the category of stimuli-responsive materials, which can change shape in response to environmental conditions, with temperature changes being the most prevalent trigger. The ability to remember their original shape is an intrinsic property of SMPs, allowing them to return to their memorized shape upon exposure to external stimuli multiple times without material degradation. This unique property of SMPs mainly arises from the capability to transition between shape-fixed and reversible phases, maintaining a temporary shape at lower temperatures and a permanent shape at higher temperatures [29]. The molecular structures responsible for these transitions are incorporated into the polymer matrix with different thermal properties to design SMP matrices. The matrix of SMPs comprises several materials, such as epoxy, polyvinyl alcohol, polystyrene, polylactic acid (PLA), thermoplastic polyurethane (TPU), polycaprolactone (PCL), polyimide (PI), and others. The combination of the fixed phase and reversible phase gives SMPs unique properties tailored to meet specific application needs [25].  
The addition of nanofillers into neat SMP matrices results in SMPNCs, which exhibit enhanced mechanical, thermal, and shape-memory properties compared to their pure polymer counterparts due to the synergistic effects of the polymer matrix and the incorporated nanofillers. To achieve better dispersion of the nanostructures, researchers have developed various techniques to incorporate them into the polymer matrix, such as melt mixing, in situ polymerization, solution mixing followed by casting, chemical functionalization followed by cross-linking reactions, and ultrasonic dispersion [19; 32; 33]. As researchers delve into the synthesis and optimization of these SMPNCs, their applications span a wide array of fields, showing potential for innovative solutions in shape-changing and responsive materials.