esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
## 5 Conclusions  
Canopy structure is an important driver for canopy PAR modeling and is an important component in forest gross primary productivity (\(P_{gs}\)) models as it regulates how portions of diffuse and direct PAR are distributed throughout the canopy. In this study, a method was presented that provides for rapid updates of the canopy radiation regime, after an initial, computationally intense model initialisation phase. The model relies on the population of a look-up table that stores probabilities of absorbing PAR by individual scene elements and handles the dynamic changes in the canopy PAR that result from solar tracking and changes in atmospheric conditions. Once canopy radiation was simulated, a single leaf-level photosynthesis model was used to model canopy productivity for a time series of over 2000 measurements spread throughout a growing season. Through optimization of the model parameters to EC-derived \(P_{gs}\) it was found that photosynthetic assimilation was highest at 19 \({}^{\circ}\)C and humid conditions and during the mid-season, in agreement with reported values for this climatic zone. Improvements to the model representation, canopy structure, and the inclusion of the transfer of long-wave radiation, sensible and latent heat, and _in situ_ sensor network data have considerable potential to refine these estimates and may lead to a tighter coupling between eddy-covariance stand level estimates of gross primary productivity and leaf-level observations of photosynthesis. Such coupling can be used to gain insight in function-structure relationships at spatial and temporal scales where laboratory and field knowledge of photosynthesis meet. **Acknowledgments:** Parts of this research were funded with the NSERC Discovery grant to [PERSON]. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors an do not necessarily reflect the views of the funding body. We are thankful to [PERSON], [PERSON] and [PERSON] for their insightful comments and support throughout the research, [PERSON] and [PERSON] from the Commonwealth Scientific and Industrial Research Organisation (CSIRO) for their help and lending of equipment for data acquisition, members of the Biometeorology and Soil Physics Group, Faculty of Land and Food Systems (University of British Columbia) for maintenance of the research site. **Author Contributions:** [PERSON] designed and developed the simulation code, analyzed the simulation results, and provided the initial draft of the manuscript; [PERSON] was involved as [PERSON]'s advisor; Dr. [PERSON] provided the EC data along with key inputs as to the interpretation and analysis of these data; All co-authors were involved with the various stages of manuscript revision. **Conflicts of Interest:** The authors declare no conflict of interest. **List of Symbols**
# Identifying Influential Nodes of Complex Networks Based on Trust-Value
## 2 Methods
### Basic Idea  
PageRank is generated from web page sorting, which is not only the core of Google and other search engines, but also currently used to rank data in various network environments. PageRank is popular due to both its perceived effectiveness and its easy-to-understand philosophy, rather than ranking objects based on difficult-to-measure intrinsic qualities [27]. As mentioned above, in PageRank, pages are abstracted to nodes and hyperlinks are abstracted to edges. The influence indicator of nodes in PageRank is defined by the PR value. One node's PR value depends on the importance of other nodes pointing to this node. One node has a higher PR value if it has many edges from other high-PR nodes. In PageRank, each node has an initial PR value, which stabilizes after continuous iteration. In this process, a web user may not only jump to an adjacent web page from some web page with a random probability, but also jump to any web page with an additional probability. The PR value of node \(i\) at time \(t\) is defined as  
\[PR_{i}^{t}=\frac{1-\alpha}{n}+\alpha\sum_{j\in N_{i}}\frac{PR_{j}^{(t-1)}}{k_{j}} \tag{1}\]  
where \(n\) is the number of nodes in the network, \(N_{i}\) is the set of neighbors of node \(i\), \(\alpha\) is the jump probability, and \(k_{j}\) represents the count of nodes to which node \(j\) points.  
In the PageRank algorithm, each node distributes its own PR value evenly to neighboring nodes. However, the probability and amount of information exchanged between nodes in the actual network are largely not average allocations. In the process of information dissemination in the network, to simplify the description, it is assumed that only one node needs to transmit information through the network. To transmit the information to all nodes at a faster rate, the node spreads the information to all nodes in a set of its neighbors, and nodes that are more \"trustworthy\" will receive more information until the network reaches a steady state, or the algorithm reaches a certain number of iterations, which is consistent with our perception in reality.  
Inspired by this phenomenon, we propose a trust-value concept that consists of the similarity ratio and the degree ratio. In this section, we explain similarity ratio, degree ratio, and trust-value in detail. Finally, we introduce the proposed trust-PageRank algorithm.  
Herein, the network is represented by \(G=(V,E)\), where \(V\) represents the nodes set of networks and \(E\) represents the edge set. The adjacent matrix of the network \(G\) is \(A=\{a(i,j)\}\). In the undirected network, if there is an edge between the nodes \(v_{i}\) and \(v_{j}\), then \(a(i,j)=1\). \(n=|V|\) is the number of nodes in the network and \(m=|E|\) is the number of edges in the network. Table 1 provides the definitions of symbols that will be used below.
## 4 Applying the Pade Approximants for Approximate Computation of IG Modes
 \tag{42}\]  
Differences between the exact functions \(C_{3,0}(\varepsilon)\) and \(C_{1,2}(\varepsilon)\) and their Pade approximants are depicted in Figure 1. In particular, the maximal difference \(|C_{3,0}(\varepsilon)-C_{3,0}(\varepsilon\big{|}[L/(L+1)]\,)|\) is \(0.0187\) for \(L=2\) and \(0.0063\) for \(L=3\); while the maximal difference \(|C_{1,2}(\varepsilon)-C_{1,2}(\varepsilon\big{|}[L/L]\,)|\) is \(0.0631\) for \(L=2\) and \(0.0224\) for \(L=3\). Here, the expansions \(C_{3,0}(\varepsilon\big{|}[L/(L+1)]\,)\) coincide with the expansions \(C_{3,0}(\varepsilon)\) up to the terms \(\varepsilon^{L}\) and \(1/\varepsilon^{L+1}\), inclusively, whereas for \(C_{1,2}(\varepsilon\big{|}[L/L]\,)\) and for \(C_{1,2}(\varepsilon)\) the coincidence is up to the terms \(\varepsilon^{L}\) and \(1/\varepsilon^{L-1}\). Increasing the parameter \(L\) makes it possible to obtain increasingly accurate approximations of the IG mode, although this approach is accompanied by more cumbersome fractional rational expressions for the coefficients. It is worth noting that using the Pade approximants does not require knowing the exact expressions for the functions \(C_{3,0}(\varepsilon)\) and \(C_{1,2}(\varepsilon)\). Instead, it suffices to know the first few terms of the series expansions of these functions (in powers of \(\varepsilon\) at \(\varepsilon\to 0\) and in powers of \(1/\varepsilon\) at \(\varepsilon\to+\infty\)). These expansions are obtained from the expansions of the roots of the characteristic equation; to expand the roots by the powers of \(\varepsilon\) or \(1/\varepsilon\), well-known conventional methods are used. In this section, the use of the Pade approximants is demonstrated for mode \(\mathbf{IG}^{\varepsilon}_{3,1}(\mathbf{r},\varepsilon)\), which is expanded into the series of HG modes. It is easy to see that this approach is suitable for any other IG mode. Its value for numerical computations of modes \(\mathbf{IG}^{(\varepsilon,\varepsilon)}_{p,q}(\mathbf{r},\varepsilon)\) becomes obvious for large values of the index \(p\) because, in this case, roots of the characteristic equation cannot be explicitly expressed in radicals.
## 1 Introduction
Diverging from previous designs, we incorporate the invertible neural network (INN) for image encoding and decoding, treating image hiding as a forward process and secret information reconstruction as a backward process within a single trained network. Additionally, we introduce a novel chaotic mapping permutation algorithm to enhance security. Through extensive experiments, we validate the superior performance of our network in image steganography tasks. Key contributions include:  
* Designing CHASE, a novel invertible high-capacity image steganography network capable of hiding a multi-channel colour secret image within a single-channel grey cover image in a single steganography process. * Proposing an image permutation algorithm based on Logistic chaotic mapping, utilising encrypted secret images in the steganography process to enhance security. * Combining encoder-decoder steganography structure and the adversarial learning concept of generative adversarial networks to improve image quality in stego and revealed secret images at large-capacity scales, with generated stego images exhibiting high resistance to steganalysis. The subsequent sections are structured as follows: Section 2 provides an overview of relevant research and existing methodologies in image steganography and INN. Section 3 comprehensively outlines our proposed image steganography network, based on the INN and GAN, elucidating the implementation of the image permutation algorithm. Section 4 presents and scrutinizes the experimental outcomes. Finally, Section 5 encapsulates the paper's findings and offers a forward-looking perspective.
# Data Driven Leakage Detection and Classification of a Boiler Tube
## 2 Methods and Procedures for Leakage Classification of the Boiler Tube
### Support Vector Machine (SVM)  
The support vector machine (SVM) belongs to the supervised learning models category of machine learning. SVM is a discriminative algorithm that separates the instances of different classes using a hyperplane. It calculates an optimal hyperplane that separates the label data in the best possible way. In this work, in addition to k-NN, SVM with a gaussian kernel was used as a second classifier for comparison purposes.
# AI-Based Approach to Firewall Rule Refinement on High-Performance Computing Service Network
## 5 Conclusions  
This study addresses the critical challenges in network security within HPC environments by analyzing firewall event logs using machine- and deep-learning models. We collected and integrated events from Supercomputer Nurion's network security equipment over a four-year period. This allowed us to significantly contribute to improving network security in the HPC service environment. Our analysis process involved parsing, extracting, and pre-processing vast firewall event logs to create labeled datasets for training. We developed a tailored database for efficient big-data storage and processing, ensuring that the high-speed demands of real service networks were satisfied. Our method successfully detected and classified human error-caused firewall rule policy errors by identifying patterns when the firewall event state changes.  
We implemented various machine-learning algorithms (naive Bayes, KNN, OneR, J48, logistic regression, and SVM) and a deep-learning model (NN with cross-entropy loss function) by experimenting with different feature sets and data-scaling techniques. The results demonstrate the effectiveness of these models in classifying security events and improving their accuracy. Our comparative analyses showed that models such as KNN, J48, and the NN model performed exceptionally well with expanded feature sets, achieving high accuracy in classifying firewall event logs. An SVM with a polynomial kernel also showed significant performance improvements with increased features.  
The findings of this study provide valuable insights into the effective use of machine-learning and deep-learning models for network security analyses in HPC environments. Our proposed solutions and methodologies provide the basis for enhanced network security measures, the proactive identification of policy errors, and automated threat-detection and  
Figure 10: Accuracy and loss (both in training and validation): comparison of deep-learning model with different feature selections.  
-response capabilities. Future studies will focus on refining the models, exploring additional classification algorithms, and implementing real-time security-monitoring systems based on these findings.  
Conceptualization, G.L.; Formal analysis, J.-K.L.; Writing--original draft, J.-K.L.; Writing--review & editing, G.L.; Project administration, T.H. All authors have read and agreed to the published version of the manuscript.  
This research has been performed as a project of Project No. K24L2M1C1 (The national flagship supercomputer infrastructure implementation and service) supported by the Korea Institute of Science and Technology Information (KISTI).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
This research was supported by the Korea Institute of Science and Technology Information(KISTI) (No. K24L2M1C1).  
The authors declare no conflicts of interest.
## 1 Introduction
There are several reviews in the literature on the use of _LLM_ in healthcare, but they often focus on specific topics. For example, ref. [7] focused on creating models specific to medicine, ref. [6] examined the use of _LLM_s in medical education, ref. [8] addressed applications limited to image analysis, ref. [9] explored ethical considerations, and and others have presented a general taxonomy, but focused on specific models like ChatGPT [10; 11]. However, none of these reviews provide an overall view of the temporal development of the topic or a geographical analysis of how interest in the use of _LLM_ in healthcare has evolved, as proposed in [12]. This paper aims to fill this gap by presenting an analysis of the temporal, geographical, and item trends in interest for _LLM_s in the healthcare field. In a second phase, the most relevant works will be analyzed to highlight the key themes of interest, from the perspective of technological solutions and practical applications. For these purposes, classical techniques from natural language processing, artificial intelligence,and complex network analysis will be used. To this end, data extracted from Scopus [13] will be used, and several distinct datasets will be created to examine various aspects of the topic. This study presents a comprehensive analysis of research articles and review-type articles across several years, focusing on temporal, geographical, and thematic trends. The temporal analysis reveals significant shifts in research activity, including periods of increased or decreased publication output and the emergence of new areas of interest. Geographically, the results identify regions and countries with higher concentrations of publications, as well as regions experiencing growing or stagnant international collaboration. The thematic analysis highlights the key research areas addressed in the reviewed papers, tracking evolving topics and changes in research focus over time. Additionally, the collaborative analysis sheds light on key networks of international collaboration, revealing changes in the distribution of affiliations across subperiods and publication types. Finally, an investigation of the most cited papers highlights the works that have had the greatest impact on the scientific community, identifying enduring themes and methodologies that continue to shape the field of study. In Sections 2 and 3, we briefly introduce _LLM_s and the methodologies and technologies used for the review. Section 4 introduces the structures of the datasets while in Section 5, the results of the quantitative analysis are presented, and some of the most significant papers are discussed. Finally, in Section 6, some conclusions are drawn, and potential future improvements are outlined.
## 3 Results and Discussion of CNN Models Classification
### Framework and Implementation Details
#### 3.2.2 Implementation of CNN Models  
The VGG16, VGG19, and ResNet101 models were implemented using the Keras API, which provides pre-trained models accessible from the keras.applications module. The models were fine-tuned to the task of classifying drilled holes into three classes: Green, Yellow, and Red.  
The training process involved applying data augmentation techniques such as random rotations, flips, and scaling to increase dataset variability. Stochastic Gradient Descent (SGD) with momentum was used as the optimizer, while categorical cross-entropy was applied as the loss function.
## 2 Climbing Scaffold Status Monitoring Method
### Climbing Scaffold Status Warning Model  
To ensure the operational safety of climbing scaffolds in super-high-rise construction, this study introduces a real-time warning model based on the Cumulative Sum (CUSUM) anomaly detection algorithm. The model identifies abnormal displacement trends by continuously monitoring GNSS-based displacement data and issuing warnings when cumulative deviations exceed predefined thresholds. Compared with traditional threshold-based monitoring, CUSUM is more sensitive to small but sustained deviations, making it particularly suitable for early warning applications in dynamic scaffold monitoring.
## 4 Conclusions  
This study was designed to evaluate the potential for developing an inexpensive device for accurately estimating SO\({}_{2}\), regardless of skin type, via narrowband spectroscopy with a small number of wavelengths. The proposed simulated annealing-based wavelength selection and EMLB-based SO\({}_{2}\) estimation algorithm was found to yield SO\({}_{2}\) estimates with low error (\(<\)1%), using as few as five wavelengths (524, 542, 553, 585, and 588 nm), in both simulated and in vivo validation datasets. Furthermore, these results were consistent across skin types of low, medium, and high melanin fractions. Additional testing proved that the proposed algorithm is robust to slight fluctuations in the peaks of the selected wavelengths, as might be experienced in a device constructed with cost-effective LEDs. Similarly, the algorithm proved to be robust to data convolution with a 15 nm FWHM Gaussian filter, another test conducted to model a more realistic device. However, the in vivo testing showed discrepancies between the estimated SO\({}_{2}\) values for the ideal and convolved cases of \(\sim\)10% on average. Future work will include in vivo testing with a larger group of subjects representing a more diverse set of skin types. Further algorithm development will account for varying peak wavelength uncertainties and LED bandwidths for the selected wavelengths and will include the expansion of the wavelength range of interest to include near-infrared.  
Conceptualization, G.B.; data curation, R.B. and A.H.; formal analysis, J.C. and A.H.; investigation, J.C. and R.B.; methodology, J.C., A.A. and A.H.; resources, A.H.; software, J.C.; supervision, A.A., F.V. and K.T.; visualization, J.C.; writing--original draft, J.C.; writing--review and editing, A.A. and A.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the edical Executive Committee in the clinical center of the Universiti Tun Hussein Onn Malaysia (UTHM).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
## 2 Improved Whale Optimization Algorithm
### Numerical Experiments on Algorithm Performance  
The performance of IWOA needs to be proved by numerical experiments and compared with WOA and other algorithms. Six typical test functions are selected for the experiment to test the algorithm's optimization ability. The mathematical expression, search range, and theoretical minimum value of each test function are shown in Table 1. Sphere, Rosenbrock, and Quartic are single-peak test functions. Single-peak functions, characterized by a solitary extremum, are commonly employed for evaluating the optimization efficiency and search precision of algorithms. Rastrigin, Ackley, and Griewank belong to the category of multi-peak test functions, containing multiple extreme points and frequently employed to assess an algorithm's capacity to get rid of local optima.  
Figure 1: Variation in convergence factors with number of iterations.  
The population size of WOA and IWOA was set to 30, while the number of iterations simulating each test function was fixed at 500. In order to visualize the optimization effect, the iterative process curves of WOA and IWOA for the six test functions are plotted, and the optimization processes of Gray Wolf Optimization (GWO) and Sparrow Search Algorithm (SSA) under the same test conditions are added. Figure 2 presents the convergence process curve.  
As illustrated in Figure 2, IWOA converges rapidly at the beginning of the iteration. This is attributed to the fact that the good point set method enhances the diversity of the initial whale population, consequently leading to a notable acceleration in the convergence rate. In comparison with the other three swarm intelligence algorithms, IWOA exhibits  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function Name** & **Mathematical Expression** & **Search Range** & **Optimal Solution** \\ \hline Sphere & \(F_{1}(x)=\sum\limits_{i=1}^{n}x_{i}^{2}\) & \([-100,\ 100]\) & 0 \\ Rosenbrock & \(F_{5}(x)=\sum\limits_{i=1}^{n-2}\left[100\big{(}x_{i}-x_{i}^{2}\big{)}^{2}+(x_ {i}-1)^{2}\right]\) & \([-30,\ 30]\) & 0 \\ Quartic & \(F_{6}(x)=\sum\limits_{i=1}^{n}x_{i}^{4}+random[0,1)\) & \([-1.28,\ 1.28]\) & 0 \\ Rastrigin & \(F_{7}(x)=\sum\limits_{i=1}^{n}\left[-x_{i}^{2}-10\cos(2\pi x_{i})+10\right]\) & \([-5.12,\ 5.12]\) & 0 \\ Ackley & \(F_{8}(x)=-20\exp\left(-0.2\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}x_{i}^{2}} \right)-\exp\left(\frac{1}{n}\sum\limits_{i=1}^{n}\cos(2\pi x_{i})\right)+20+\epsilon\) & \([-32,\ 32]\) & 0 \\ [PERSON] & \(F_{9}(x)=\frac{1}{400}\sum\limits_{i=1}^{n}x_{i}^{2}-\int\limits_{i=1}^{n}\cos \left(\frac{x_{i}}{V_{7}}\right)+1\) & \([-600,\ 600]\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test Functions.  
Figure 2: Convergence process curves of test functions: (**a**) Sphere; (**b**) Rosenbrock; (**c**) Quartic; (**d**) Rastrigin; (**e**) Ackley; (**f**) Griewank.  
superior accuracy and convergence speed. These results indicate that the improvement strategies of IWOA are reasonable and effective, and the efficiency and quality of the computation will be greatly improved in the inverse analysis calculation.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 3 Data Preprocessing  
The current-voltage (I-V) curves generated through MATLAB Simulink must undergo data preprocessing to be converted into a suitable input format for the multi-input model prior to model training.