esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Machine Learning Predictive Model  
The risk factors collected were employed to train several classifiers with the aim of predicting the risk of developing cancer. Figure 2 presents the ROC curves of all the classifiers, along with their corresponding AUC values. The ensemble of decision trees was the best performer. Key metrics such as the AUC, accuracy, specificity, and sensitivity provide a comprehensive overview of the model effectiveness. An average AUC of 81% was found in distinguishing between those at risk of developing cancer and those not at risk, with 88% sensitivity, 57% specificity, 78% accuracy, 80% precision, and an F1 score of 0.84, as computed at the optimal cutoff point, which maximizes the classifier's accuracy. To assess the stability and reliability of our machine learning model, we conducted a 10-fold cross-validation, calculating the AUC for each fold. As depicted in Figure 3, the AUC values demonstrated consistent performance across all the folds, with only minor fluctuations  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{1}{c}{**Group A Healthy Subjects with a Family History of Breast**} & \multicolumn{1}{c}{**Group B Subjects with Cancer (n = 473)**} & \multicolumn{1}{c}{\(p\)**-Value**} \\  & **N (\%)** & **N (\%)** & **N (\%)** \\ \hline \hline
**Mutation outcomes** & & & \\ Negative & 162 (34.2) & 640 (69.9) & \\ Positive & 194 (41.0) & 115 (12.6) & \textless{}0.001 *** \\ VUSs & 8 (1.7) & 50 (5.5) & \\ Not screened & 109 (23) & 111 (12.1) & \\ \hline \hline
**Specific found** & & & \\
**mutations** & & & \\ BRCA1 & 118 (58.4) & 75 (45.4) & \textless{}0.001 *** \\ BRCA2 & 83 (41.0) & 87 (52.7) & \\ BRCA1/2 & 1 (0.5) & 3 (1.8) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mutational status of the respondents *** \(p<0.001\).  
observed. The AUC values ranged from approximately 0.78 to 0.88, indicating a robust and reliable model performance.  
The ROC and the PR curves, as depicted in Figure 4, are a graphical representation of the best classifier performance.  
Figure 3: Distribution of the AUC values across the 10-fold cross-validation.  
Figure 2: ROC curves for the different classifiers used to predict the risk of developing cancer. The classifiers tested include decision tree, support vector machine (SVM), naive Bayes, ECOC, discriminant analysis, linear, ensemble of trees, neural network, and k-nearest neighbor (KNN). The AUC values for each classifier are displayed in the legend, with the ensemble of trees achieving the highest AUC of 0.81, indicating the best predictive performance among the models tested.  
Using the predictor importance function from the decision tree ensemble, we identified the most significant predictors of the breast cancer risk. The analysis highlighted that the genetic mutation status (e.g., BRCA), menopausal status, BMI, age, years of smoking, and breastfeeding history were the top factors influencing the model's predictions.
## 5 Discussion  
According to the previous analysis, the proposed method outperforms most of the state-of-the-art dehazing approaches, in terms of haze-removal, visibility-enhancing, and edge-preserving, where it can overcome most of the shortcomings of those methods. This outstanding success emphasizes the robustness and effectiveness of both \(AL\) and transmission medium estimation strategies (\"A-Est\" algorithm and CMT\(net\) model), which affirms that the proposed \(AL\) estimation algorithm can calculate the atmospheric light accurately, and theproposed architecture CMT\(net\) estimates the haze thickness efficiently. These are the advantages of our proposed method.  
Even though the proposed CMT\(net\) is based on synthetic images training, the dehazing results prove that the proposed architecture can handle real-world images as well.
# Application of Improved LightGBM Model in Blood Glucose Prediction
## 1 Introduction
However, the random searching algorithm exhibits a poor performance when the dataset is small. Therefore, this paper proposes an improved LightGBM model for blood glucose prediction. The main contributions of this study are as follows: by preprocessing the data on various medical examination indicators of people receiving medical examination, this paper proposes a LightGBM model optimized by the Bayesian hyper-parameter optimization algorithm to predict the blood glucose level of people receiving medical examination. The LightGBM model optimized by the Bayesian hyper-parameter optimization algorithm achieves a higher accuracy than the XGBoost model, Catboost model, the LightGBM model optimized by genetic algorithm, and the LightGBM model optimized by random searching algorithm, which can help doctors give early warning to those potentially suffering from diabetes, so as to reduce the incidence of diabetes, increase the diagnosis rate, and provide new ideas for the in-depth study on diabetes. The proposed method was verified by the diabetes data from a grade-three first-class hospital in China from September to October 2017.
## 4 Experiment and Results
### Dataset Division  
The dataset comprises multiple CT image slices and XML-format annotation files for 1018 research instances, containing a total of 6532 annotated data points. This study focuses on the binary classification of benign and malignant lung nodules. Accordingly, the data labels were binarized based on malignancy levels: nodules with levels 1-2 were classified as benign (labeled as 0), while those with levels 4-5 were classified as malignant (labeled as 1). Nodules with a malignancy level of 3, indicating uncertainty, were excluded. The final dataset thus contained 6494 instances, as shown in Table 3. The data were split into training and testing sets at a 7:3 ratio based on experience. Notably, the malignant label data in Table 3 is significantly more numerous than the benign label data. However, we chose not to balance the dataset. This decision was driven by our goal to minimize false negatives in the final prediction model, thus preventing the misclassification of malignant cases as benign. While this approach ensures that the model is more suitable for practical medical diagnostic applications, it does have certain theoretical limitations, which we briefly discuss in the conclusion of the manuscript.
# Grouting Process Simulation Based on 3D Fracture Network Considering Fluid-Structure Interaction
## 2 Methodology
### Fluid-Structure Interaction Model
#### 2.2.3 Fluid-Structure Interaction Analysis Solution  
In this paper, Finite Element Analysis (FEA) software solves the structural domain (fractured rock mass) and CFD software solves the fluid domain (grout). The two domains are interconnected on the fluid-structure interface using the SIMULIA Co-Simulation Engine (CSE) [41] which is widely used to couple CFD software and FEA software for fluid-structure interaction simulation [43; 44; 45].  
As show in Figure 3, in the fluid-structure interaction solving process, CFD software initializes the fluid field and passes loads to the FEA software (pressure + wall shear stress), and the deformations in the structure can be computed by FEA software and passes displacements back to CFD software; this can provide a new deformed geometry for the CFD software to solve the fluid field. This iteration can be repeated until the end of the coupling process.  
On the fluid-structure interface, the stress (\(\tau\)) and the displacement (\(d\)) of the fluid and structure should be equal:  
\[\tau_{g}n_{g}=\tau_{f}n_{f} \tag{19}\]  
\[d_{g}=d_{f} \tag{20}\]  
where the subscript \(g\) and \(f\) represent grout and fractured rock mass.
# 400 GbE Technology Demonstration Using CFP8 Pluggable Modules
## 5 Conclusions  
In this article, we review the current status of the 400G Ethernet standard. The essential techniques to implement 400G client-side optics, like PAM4, FEC and CTLE, are discussed. The PIC in Juniper's PTX5000 platform has been developed with the CFP8-LR8 modules being the optical front-end. The optical signal transmits through 30 km SMF. The pre-FEC BER is well below the threshold for UCW and there is no post-FEC error. This demonstration represents the successful implementation of a 400G Ethernet interface in the core IP/MPLS router using the CFP8-LR8 pluggable modules.  
Conceptualization, Y.Y. and J.A.; methodology, Y.Y.; software, Y.Y. and Q.W.; validation, Y.Y.; formal analysis, Y.Y. and Q.W.; investigation, Y.Y., Q.W. and J.Y.; resources, Y.Y., Q.W., J.Y., J.O. and D.P.; data curation, Y.Y.; writing--original draft preparation, Y.Y. and Q.W.; writing--review and editing, Y.Y., Q.W., J.Y., J.O., D.P. and J.A.; visualization, Y.Y. and Q.W.; supervision, J.A.  
This research received no external funding.  
The authors gratefully acknowledge the vigorous encouragement and strong support for innovation from [PERSON] at Juniper Networks.  
The authors declare no conflicts of interest.  
Figure 11: Interoperation of two CFP8-LR8 modules through single mode fiber (SMF) spool. The signal is transmitted from the Tx of the first module to the Rx of the second module: (**a**) BER vs. fiber length for 16 individual electrical lanes; (**b**) total BER vs. fiber length. The signal is transmitted from the Tx of the second module to the Rx of the first module: (**c**) BER versus VOA2’s attenuation for 16 individual electrical lanes; (**d**) total BER vs. VOA2’s attenuation. The threshold for the UCW is shown as the red line.
## 4 Circuit Design
### ADC Circuit and Auxiliary Circuit  
The ADS1100 (Texas Instruments, Dallas, TX, USA) was selected as the core of the ADC circuit. This circuit was assembled compatible with I\({}^{2}\)C serial interfaces, and has a simple circuit structure. The auxiliary circuit (Figure 12) contains two levels of voltage conversion: The primary and secondary voltage regulators. The primary voltage regulator (LT1129, Analog Devices Inc., Norwood, MA, USA) generates 3.3 V at supply of 12 V by a plurality of battery units. The secondary voltage regulator (LM2596, Texas Instruments, Dallas, TX, USA) generates 5 V at a supply of 12 V to fulfill the requirements of the iridium circuit.
## 4 Sensing Material Characterization
### UV-Vis Optical Absorption Spectra  
Figure 8 illustrates the absorption spectra of ALD-deposited HfO\({}_{2}\) thin films. Significant intensities were observed around 220 nm, indicating that HfO\({}_{2}\) exhibits promising characteristics for corona detection within the solar blind UV-C range. Additionally, an increase in annealing temperature is associated with a slight reduction in the bandgap. This reduction can be attributed to a minor decrease in defect states due to the higher annealing temperatures. The inset of Figure 8 presents a Tauc plot, derived using Equation (2) which is explained in detail [22], which provides a detailed analysis of the material's optical bandgap:  
\[(alv)^{2}=A(hv-Eg) \tag{2}\]  
where \(A\) represents a constant, \(\alpha\) denotes the absorption coefficient, and \(hv\) and \(Eg\) correspond to the photon energy and optical bandgap, respectively. The optical bandgap for HfO\({}_{2}\) annealed at 500 \({}^{\circ}\)C is calculated to be 5.71 eV, which is closely align with the previous studies [25; 26], making it highly suitable for applications in the UVC range.
## 4 Discussion  
### Interpretation of Results  
As we show in Figure 11, when comparing two pixels, the pixel similarity measures NCC and NEBF are closely related to each other. However, the measure margins between correct and incorrect correspondences are better for the NCC than the NEBF (Figure 12). We think that this is a consequence of the reduction of information content in the binary features compared to the original brightness values. Therefore, it cannot be expected that an NEBF-based algorithm can produce better quality than the NCCCOS algorithm. This leads to more false matches and non-matches compared to the NCC-based correspondence search. The additional median filter step in BICOS(+) mostly compensates for this lower initial matching quality.  
The effect of the signal to noise ratio (Figure 9c) on the coarse correspondence search result can be observed in Figure 13. The dark areas on the metal objects in the front of the scene have slightly increased error rates. A similar effect can be expected for a weaker projection light because it reduces the signal to noise ratio.  
In comparison with the original BICOS algorithm, BICOS+ works on a higher number of binary features for sequence lengths of 10 and lower. This results in significant improvements at short sequence lengths of 7 and lower. The different way of calculating the binary features shows no disadvantages in the results.  
Other ways of calculating binary features are possible, for example, comparing the sums of three or more brightness values. As the two types of binary features which we tested already show very similar results, we do not expect significant improvements with differently calculated binary features.
# Effect of Postharvest UVB Irradiation on the Fruit of cv. Dottato (_Ficus carica_ L.)
## 2 Materials and Methods  
### Plant Materials and Ultraviolet Irradiation  
Fig fruits of the cultivar Dottato at commercial maturity were harvested in September 2021 in Southern Italy (Potenza, Basilicata), and immediately carried to the laboratory and stored in a refrigerator for 1 day at 4 \({}^{\circ}\)C. Fruit uniform in size and appearance, undamaged, was selected for the experiments. The experiment was conducted in a climatic chamber (air temperature and relative humidity settled to 5 \({}^{\circ}\)C and 80%, respectively.)  
The inside of the climatic chamber contained an aluminum structure with 4 Q-Panel UVB 313 fluorescent lamps (Q-Panel, Cleveland, OH, USA) on the upper part, while below there was a shelf 70 cm away from the lamps on which the fruits were placed for irradiation. During the experiment the lamps were used in 2 ways: unfiltered UVB treatment (+UVB) and filtered, with a polyester film to eliminate the UVB component as control (\(-\)UVB); in Figure 1 the spectral irradiance of unfiltered and filtered fluorescent lamps is shown. The UV irradiance from the lamps, in the spectral range of 260 to 400 nm, was assessed by a double monochromator spectroradiometer (model. SR9910-PC, Macam Photometrics Ltd., Livingstone, UK). In the +UVB treatment, the supplemental UVB in the spectral interval of 280-315 nm was 2.26 W m\({}^{-2}\), while in terms of the ultraviolet biologically effective UVB dose (UVB\({}_{\text{BE Caldwell}}\)) weighted using the generalized plant action spectrum [38], it was 1.91 Wm\({}^{-2}\), respectively, 1.5 and 6/7 times larger than what is measurable from the sun in the central hours of a clear summer day in central Italy. These differences are explained by the fact that lamps also emit wavelengths absent in the sun (below 300 nm); the potential greater biological effects of these wavelengths are well considered by the Caldwell's action spectrum, with respect to a simple irradiance integral between 280 and 315 nm. Two irradiation times were applied: unfiltered lamps as UVB treatment for 10 (+UVB10) and 60 min (+UVB60) and - UVB filtered lamps as a control for 10 (-UVB10) and 60 min (-UVB60). The UVB doses irradiated to the fruits were 1.4 and 8.4 kJm\({}^{-2}\) for +UVB10 and +UVB60, respectively (Table 1).  
The supplemental dose in 1 h of UVBBE Caldwell in the +UVB60 treatment represent the radiation which could be a little higher than what could be received from the sun in July on a day with clear skies in central Italy. Before each treatment, figs of similar weight were randomly distributed on the shelf under the lamps. Each treatment was repeated on both sides of fig fruits. Eight figs per replicate were settled in a randomized complete block design with three replications per treatment. After treatment, the fig fruits were stored in the dark at 5 \({}^{\circ}\)C and were analyzed at different times: immediately after treatment (T0), after 7 days (T1) and 10 days (T2).
## Appendix A Results of Five Algorithms on Validation Datasets  
In this study, we used random forest (RF), multivariate adaptive regression splines (MARS), gradient boosting regression trees (GBRT), light gradient boosting machine (LightGBM), and long short-term memory (LSTM) algorithms to build GPP estimation models. In addition, the testing dataset was a randomly selected 30% of the generated dataset used to validate these models. Based on the training and testing results for the five algorithms. The validation results for the five models are given in Table A1. We determined the optimal models by comparing their respective R\({}^{2}\) and root mean square error (RMSE) values. Based on a comparison of results, we found that the GBRT model was better than the other four models in this study. The R\({}^
# Gaining Insights into Conceptual Models: A Graph-Theoretic Querying Approach
## 4 Mapping an OPM Model to a Graph
### From an OPM Model to a Cypher Query
#### 4.2.1 The Neo4j Cypher Query Language  
Cypher is an easy-to-master pattern-matching language, enabling querying (by matching patterns in the graph), construction and modification of the graph, and specifying schemata for the graph. A Cypher statement is comprised of several phrases, starting with a graph-specific matching part, followed by optional SQL-like phrases to facilitate further post-processing of the matching outcome, and generating the result to be returned.  
Cypher was originally intended to be used only with Neo4j, but in 2015 Cypher was opened up for more general use through the openCypher [36] project. The patterns follow the basic \"triple\" construct, whereby a graph is a collection of triples, each defining a link between two nodes and their properties. Like OPL, Object-Process Language, which is the textual representation of the OPM model, the Cypher query language is easy to read due to its graphics-oriented asci-art syntax.  
Figure 6: The result of applying the Flattening Algorithm on the **Passenger Securing** system.  
Each Cypher statement starts with a verb declaring its purpose: MATCH, CREATE, and MERGE, the triples-matching phrase, the optional follow-up SQL-like modification phrases, additional update and alteration phrases, and finally the optional RETURN part.  
The pattern matching syntax, demonstrated in examples and algorithms in this paper, is of particular interest: Nodes are enclosed in round parentheses, e.g., (<node>), and links are enclosed in brackets, e.g., -[<link>]->. Basic triples are 0-0 for a non-directional triple, 0->() for a right directional triple, and 0<-0 for a left-directional triple.  
Within the parentheses, each <node> or <link> consists of three optional parts: <var name>:<type><[property: 'value',... ]>. A variable name <var name> helps to refer to matched elements further in the statement, as well as in the RETURN phrase. The <type> specifies a classification of the element or link. In our case, these are OPM element types, such as process and object. Properties further enrich the element with a data record that can be used for matching and insertion of their values in statements.  
A full example of a triple in the Cypher query language is:  
(node1:TypeA [propertyKey: 'Value'])-[rell:ReITypeA]->(node2:TypeB).  
The variables node1, rell, and node2 hold references to the respective elements of the triple and can be further used in optional follow-up phrases. If there is no use for a variable, it can be omitted, as we do in some of the examples in this paper to reduce clutter.  
As all of the graph content comes from the logical model structure in OPCCloud, we reference these values using a macro-like $-NOTATOION, such as $OPM_THING_TYPE in Code 1.
# A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
## 3 PV Model Architecture
### Error Evaluation  
The normalized Root Mean Square Error (\(nRMSE\)) and Mean Absolute Percent Error (\(MAPE\)) are used to evaluate the prediction methods, which are calculated as follows:  
\[nRMSE=\frac{1}{P_{rated}}\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}{\left(P_{mi}-P_{pi} \right)}^{2}} \tag{19}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}}\frac{\left|P_{mi}-P_{pi}\right|}{P_{mi}} \tag{20}\]  
where \(n\) is the number of samples for PV power generation time periods; \(P_{rated}\) is the rated power; \(P_{pi}\) is the predicted power in the \(i\)th time period; and \(P_{mi}\) is the measured power in the \(i\)th time period. In order to avoid the effect of night data, the time periods in which the predicted power and measured power are both zero, is the non-power generation period of the PV power station.
###### Abstract  
To investigate the bioconcentration and potential health risks of metal(loid)s in a particular agro-ecological area, 230 pairs of soil and corresponding crop grain samples were collected from typical corn and wheat plants. The geo-accumulation index (Igeo), bioconcentration factors (BCF), health risk assessment (the target hazard quotient), and Receptor Prediction Model (PC\({}_{\text{S}}\)-SMLR) analysis were adopted to study the spatial distribution, assess the health risks, and predict the relationship between metal(loid)s and soil properties. It was found that the mean concentrations of Cu, Zn, Pb, Hg, and Cd in the study area's agricultural soils exceeded the background soil concentrations, especially for Cd (0.2 mg/kg), Meanwhile, the corresponding Cd concentration in wheat samples was higher than the food quality limit. The results of the Igeo showed that the samples with a value higher than 0 for Cd and Hg accounted for 47.83% and 33.48%, respectively. The results of BCF of Cu, Zn, Cd, and As were higher in wheat than in corn, except for Ni. The target hazard quotient (TTHQ) of health risk of wheat, corn, and soil were higher for children (2.48) than adults (1.78), showing a potential health risk for individuals who mostly consume wheat. In addition, the PC\({}_{\text{S}}\)-SMLR analysis of the BCF prediction model for Cu, Zn, As, and soil properties showed differences in terms of the influences from wheat and corn. These results provide valuable information that not only can help local residents improve the staple food structure, but also can get provide a reference metal(loid)s concentration level for agricultural soils in the study area and restore a sustainable agro-ecological environment.  
H ebei plain; metal(loid)s; accumulation; health risks; PCs-SMLR Article