esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Feature Importance  
#### 4.2.1 Tree-Based Feature Importance  
Tree-based feature-importance methods rely on how often and effectively a feature is used to split the data within an ensemble of decision trees. Each node split is chosen to best separate the data based on a particular feature in a random forest or gradient-boosted tree. Consequently, if a feature repeatedly reduces prediction errors across the ensemble, it is deemed more significant, resulting in a higher importance score. One strength of this method is its ability to uncover complex relationships that simpler methods might miss. However, it may also be influenced by how a feature is encoded, particularly if it has many distinct values, necessitating careful interpretation of the results.  
In this study, the random forest algorithm used for feature-importance measurement consisted of 100 estimators (trees). Additionally, the maximum depth of each tree and the maximum number of features used for splitting were tuned via a grid search to determine the most effective parameter values for this dataset. The model can strike an optimal balance between simplicity and flexibility by systematically testing different combinations of these hyperparameters. Once the best combination was identified, the random forest was restricted to these dominant parameter values in subsequent training sessions. Limiting the maximum depth, in particular, helps prevent overfitting because shallow trees are less likely to memorize training data. Instead, they learn more general patterns that can be applied to new, unseen examples. Likewise, an appropriate choice of max features can reduce the chance of focusing on a small subset of overly dominant parameters, thereby enhancing the diversity and robustness of the forest. These tuned hyperparameters help produce a more accurate and generalizable model while preserving interpretability through more stable feature-importance scores.
## 2 Materials and Methods
### Machine Learning Classifiers
#### 2.3.1 Decision Tree  
Decision trees are a variety of ML algorithms inspired by the human brain's decision-making process. Decision trees belong to the supervised machine learning category and have been popularly used for classification problems. The decision tree usually begins with a node that branches into potential outcomes, giving it a tree-like structure [37; 38]. It begins with a root node and ends with a leaf node, representing the attribute that best divides the data and the outcome, respectively [39]. Furthermore, the learning process begins with attribute selection, achieved by an attribute selection measure (ASM) that splits the data and assigns weight and rank to the various features. The Gini index and entropy are the commonly used ASM techniques, and they compute the information gain of the various features and build the tree in a top-down recursive manner [40]. The Gini index and entropy estimate the degree of impurity of a node. Different decision tree-based algorithms exist, including ID3, C4.5, and classification and regression tree (CART). However, the CART algorithm is employed in this study. The CART algorithm uses the Gini index measure in building the decision tree [20]. Given a dataset with \(J\) labels, and \(i\in 1,2,\ldots,J\), the Gini index is computed using:  
\[Gini=1-\sum_{i=1}^{J}P_{i}^{2} \tag{1}\]  
where \(P_{i}\) is the probability that a sample would be classified into a specific class.
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 2 Creation of Kinematic Pair Clearance Model
### Establishment of a Kinematic Model for the Clearance in Prismatic Joints
#### 2.3.2 Establishment of a Tangential Friction Model  
The friction force within the clearance of prismatic joints can be modeled using the friction model investigated in Section 2.2.2.  
The forces and torque on centroid of slider can be represented as:  
\[F_{mn}=F_{n}\mathbf{n}_{T}+F_{t}\mathbf{t}_{T}=\left[F_{mn}^{x}\ F_{mn}^{y}\right]^{T} \tag{32}\]  
\[M_{mn}=-\left(y_{m}^{A}-y_{m}\right)F_{mn}^{x}+\left(x_{m}^{A}-x_{m}\right)F_{ mn}^{y} \tag{33}\]  
where \(F_{mn}\) represents the active force on centroid of slider and \(M_{nm}\) represents the torque on the centroid of slider.
## 5 Conclusions  
This paper proposes a new network structure, DRFENet, for image denoising. To improve the global perception ability of the model and more fine-grained features, we constructed DRFENet based on two proposed strategies: an interval transmission strategy and a combination strategy of concatenated dilated convolution [15] and skip connection. Four modules--DEB, RDB, FEB, and SIRB--are used for DRFENet. In the DEB, a short-range skip connection is proposed to enhance information transmission. In the RDB, a progressive skip connection is proposed to improve the effect of dilated convolution. DRFENet better reproduced local details and made a more realistic global restoration. An ablation experiment and a comparison experiment of two sets of gray images and three sets of color images showed the superiority and robustness of DRFENet, and the denoising effect of our network model algorithm was better than that of other competitive algorithms. DRFENet can restore the details of images with a high noise level, which meets the practical demands in different intelligent applications, such as mobile devices and real-time image denoising systems. In the future, we will explore integrating the proposed method into other fields.  
Figure 5: Dunnett t-test and 95% confidence interval at noise level of 25 in the SetI2 dataset.  
**Supplementary Materials:** The supporting code can be downloaded at: [[https://github.com/zhong](https://github.com/zhong)]([https://github.com/zhong](https://github.com/zhong)) ruizhe123/DRFENet.  
**Author Contributions:** Conceptualization, Q.Z.; methodology, R.Z. and Q.Z.; software, R.Z.; validation, R.Z.; formal analysis, R.Z.; investigation, R.Z.; resources, R.Z.; data curation, R.Z.; writing--original draft preparation, R.Z.; writing--review and editing, R.Z.; visualization, Q.Z.; supervision, R.Z.; project administration, Q.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study is supported by National Key Technology R&D Program of China (No. 2021 YFD2 100605), Beijing Natural Science Foundation (No. 4202014), and the Humanity and Social Science Youth Foundation of Ministry of Education of China (No. 20 YJCZH229).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Thanks to [PERSON] and [PERSON] for their technical support.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 2 State-of-the-Art
### Global Networking of Process Data  
For integration of the PLC controllers in supervising, management and coordination systems (e.g., SCADA or MES systems), which are partly based on web technologies, additional modules are integrated in the PLC controllers, enabling a bidirectional and event-based process data transmission between the controller, supervisor and management system. Those include solutions such as the web connector with the MQTT broker in WAGO controllers [2], or access to controllers that already contain an OPC UA server (e.g., Siemens PLC S7-1500).  
These solutions also involve proprietary and closed control-integrated modules. Although the modules utilize web technologies, they cannot be transferred to other controllers. The global process data communication is used for HMIs (human machine interfaces) and/or supervisor and coordination functions in the higher level of the automation hierarchy (e.g., plant management level). Authoritative statements regarding the time response of the process data transmission are not available. However, different statements result from the reaction times (latencies) of 200 to 500 ms, or greater. Open and consistent web interfaces are not available. The above I40 requirements can only partly be fulfilled, sometimes with high adaptation expenses for integration into a CPPS.
# The Impact of Optogenetics on Regenerative Medicine
## 1 Introduction  
The central nervous system (CNS) consists of many different types of neurons interconnected and able to manage the entire physiopathology of the human body. Neuroscience researchers constantly work to find optimal strategies. Optogenetics has revolutionized the study of the nervous system by providing adequate tools to analyze the functions of neurons within the brain without changing their interconnections [1]. Optogenetics is a novel approach that integrates optical (opto-) and genetic (genetic) methods to control the activity of excitable cells and neuronal circuits. The stimulation of a neuron can be followed by the depolarization of its membrane and the generation of an action potential that travels as an electrical signal along its axon. Using genetic manipulation, optogenetics may induce the coding of photosensitive ion channels on specific neurons: this non-invasive technology combines several approaches that allow users to achieve improved optical control, and higher resolution [2]. This technology works on genetic targets to control specific neurons for the study and the modulation of complex neuronal networks in humans. The first experiments were carried out on the opsins, a natural protein that can convert light into an electrochemical signal. More precisely, through specificwavelength stimulation of specific proteins, it is possible to activate entire areas of the human brain [3]. The potential of this technique could affect many fields of science, including the medical field. In tissue engineering and regenerative medicine (TERM), optogenetics can be used in several applications, such as in signaling communications between cells, in the cell-to-materials interaction, and in the photo-modulation of biomaterials [4]. In this review, we have described the optogenetics method and its applications: specifically, we reported the main medical applications, such as retinal therapies, ventricular fibrillation, and pain therapy. Finally, we have described the main tools developed in the field of optogenetics, which may be applicable to the field of regenerative medicine (RM).
# A Tunable Metamaterial Joint for Mechanical Shock Applications Inspired by Carbon Nanotubes
###### Abstract  
The significant developments of additive manufacturing and especially 3D-printing technologies have broadened the application field of metamaterials. The present study aims at establishing the main design parameters of a novel 3D-printed polymer-based joint. The proposed joint can efficiently absorb impact energy, relieving the material components from extensive plastic deformations. The design of the machine element is inspired by the molecular structure of carbon nanotubes and appropriately adjusted in such a way that it has the ability to partially transform translational motion to rotational motion and, thus, provide axial structural protection from compressive shocks. The utilized material is a photosensitive resin that is typically utilized in 3D manufacturing processes. Experiments are utilized to characterize the mechanical performance of the raw material as well as the static compressive behavior of the joint. Finally, finite element simulations are performed to test the developed design under impact loadings characterized by different frequencies. The damping capabilities of the metamaterial-based joint are revealed and discussed.  
metamaterial; nanotube; damping; impact; joint; 3D printing; additive manufacturing; LCD; UV; resin Article
## 4 Scheduling Optimization Framework
### Ad Hoc Production and Preventive Maintenance Policies
#### 4.3.1 Production Policies  
KANBAN [61] dictates that every item stored in the storage facilities is coupled with a KANBAN card. Figure 2 depicts the functionality of KANBAN in a two-stage manufacturing system. In this figure, \(K_{0}\) and \(K_{1}\) represent the queue of KANBAN cards in \(MF_{1}\) and \(MF_{2}\). Analytically, KANBAN operates as follows. When an item is moved to \(MF_{2}\) or sold to a customer, its card is detached and transferred to \(MF_{1}\), beginning the production in that facility. It is sensible to assume that the KANBAN cards represent the ongoing customer demand for new products. On the other hand, under the \((s,S)\) policy [62],the manufacturing system produces items in respect to defined thresholds associated with the product inventory. In this regard, the thresholds are \(s\) and \(S\), denoting the minimum and maximum allowable inventory, respectively. When the product inventory is less than \(s\), the production process is initiated in the facilities. If the inventory reaches \(S\), then the facilities stop producing items.  
An alternative approach to production control is Base Stock (BS) [63], authorizing manufacturing activities in every facility integrated in the system after an order is placed by customers. Figure 2 illustrates BS. In terms of notation, \(A_{0}\) and \(A_{1}\) denote the queue of pending orders, and \(B_{2}\) represents the queue of customer demand. According to Figure 3, \(B_{2}\) receives orders from the customers. Later, these are sent to \(A_{0}\) and \(A_{1}\) in order to begin the production process.  
Lastly, the Extended KANBAN (EK) [64] is formed on the basis of BS and KANBAN policies. The functionality of this policy is presented in Figure 4, and can be described as follows. After the placement of new orders, \(A_{0}\) and \(A_{1}\) receive information regarding the customer demand. To begin the production process in \(MF_{1}\) and \(MF_{2}\), a product must be removed from the facilities and its KANBAN card must be sent to the respective \(K_{0}\) and \(K_{1}\).
# Estimation of Rock Load of Multi-Arch Tunnel with Cracks Using Stress Variable Method  
[PERSON]  
1 Department of Geotechnical Engineering, Saman Corporation, Gyeongggi-do 13837, Korea; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Civil and Environmental Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Korea; [EMAIL_ADDRESS]  
[PERSON]  
3 Department of Civil Engineering, Kangwon National University, Samcheok 25913, Korea  
3 Department of Architectural Engineering, Kangwon National University, Samcheok 25913, Korea  
method for concrete lining, various estimation methods [5; 6; 7] that can reflect the interaction between the rock and lining, complex construction steps, geometric conditions, and plastic behavior of the rock have been employed. It is necessary to select a criterion to estimate the rock load for concrete lining and then compare it with the numerical analysis results. Such criteria for single tunnels have been developed in a few studies [8; 9], but they must be extended to multi-arch tunnels. The concept of development of rock load during the excavation of a multi-arch tunnel is explained below.  
A typical characteristic of multi-arch tunnels is that the tunnel width is typically greater than the tunnel height. In contrast to single tunnels, multi-arch tunnels contain combined cross-sections of more than one tunnel, and the excavation of each cross-section involves a sequence of complex steps. For the construction of a three-arch tunnel, the initial support is typically provided in the form of shotcrete and rock bolts. Subsequently, the central tunnel is provided with a concrete lining and side walls. Next, the adjacent left and right tunnels are excavated. The rock load continues to develop uniformly during the excavation of the central tunnel, which is usually stable owing to the resistance induced by the rock mass and supports. The non-uniformly increasing rock load, which is usually not considered in the design stage of concrete lining and side walls, starts to develop on the central tunnel when the adjacent tunnels are excavated. The side walls, which were once a part of the rock mass surrounding the central tunnel prior to being excavated, are exposed eventually when adjacent tunnels are excavated. This can lead to additional risks to the structural stability. Therefore, to reflect the design of the central tunnel, it is important to consider the increase in rock load according to the construction steps involved in the excavation of adjacent tunnels, as illustrated in Figure 1.  
This study selects an appropriate criterion for estimating the rock load of multi-arch tunnels through a parametric study using a numerical approach and also estimates the reasonable rock load according to rock mass classification and overburden. An accurate estimation of rock load is expected to provide tunnel engineers with more realistic/scientific insights into the selection of a support system in a more engineered way and to potentially advance the current three-arch tunnel design.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 3 Methodology
### Infrastructure Setup  
This section describes the configuration of a multi-node Apache Spark cluster hosted on the DIOGENIS service at the Computer Engineering and Informatics Department at the University of Patras, in Greece. The cluster consists of one leader node and one follower node, represented as \(N=\{M_{1},S_{1}\}\), where \(M_{1}\) is the leader node and \(S_{1}\) is the follower node. The primary objective of this setup is to distribute computational tasks efficiently for large-scale data processing using Spark's in-built distributed computation model.  
Given a machine learning operator \(O\), the goal is to calculate its performance in a specific scenario \(P\). We aim to evaluate the performance by measuring two metrics: total processing time and memory usage. These are modeled as functions of the underlying infrastructure and data distribution across the nodes, and will be evaluated later using the performance space \(\mathbb{R}^{2}\), where each point \(p\in\mathbb{R}^{2}\) is a tuple of (execution time, memory usage).
# Real-Time Image Analysis for Intelligent Aircraft De-Icing Decision Support Systems
###### Abstract  
Aircraft icing and snow accumulation are significant threats to flight safety and operational efficiency, necessitating rapid and accurate detection methods. The aim of this study was to develop and comparatively evaluate artificial intelligence (AI) models for the real-time detection of ice and snow on aircraft surfaces using vision systems. A custom dataset of annotated aircraft images under various winter conditions was prepared and augmented to enhance model robustness. Two training approaches were implemented: an automatic process using the YOLOv8 framework on the Roboflow platform and a manual process in the Google Colab environment. Both models were evaluated using standard object detection metrics, including mean Average Precision (mAP) and mAP@50-95. The results demonstrate that both methods achieved comparable detection performance, with final mAP50 values of 0.25-0.3 and mAP50-95 values around 0.15. The manual approach yielded lower training losses and more stable metric progression, suggesting better generalization and a reduced risk of overfitting. The findings highlight the potential of AI-driven vision systems to support intelligent de-icing decision-making in aviation. Future research should focus on refining localization, minimizing false alarms, and adapting detection models to specific aircraft components to further enhance operational safety and reliability.  
AI model; machine learning; machine vision systems; ice detection; YOLO framework 2020 academie Editor: [PERSON]  
8 May 2025  
11 June 2025  
7 July 2025  
###### Abstract  
Aircraft icing and snow accumulation are significant threats to flight safety and operational efficiency, necessitating rapid and accurate detection methods. The aim of this study was to develop and comparatively evaluate artificial intelligence (AI) models for the real-time detection of ice and snow on aircraft surfaces using vision systems. A custom dataset of annotated aircraft images under various winter conditions was prepared and augmented to enhance model robustness. Two training approaches were implemented: an automatic process using the YOLOv8 framework on the Roboflow platform and a manual process in the Google Colab environment. Both models were evaluated using standard object detection metrics, including mean Average Precision (mAP) and mAP@50-95. The results demonstrate that both methods achieved comparable detection performance, with final mAP50 values of 0.25-0.3 and mAP50-95 values around 0.15. The manual approach yielded lower training losses and more stable metric progression, suggesting better generalization and a reduced risk of overfitting. The findings highlight the potential of AI-driven vision systems to support intelligent de-icing decision-making in aviation. Future research should focus on refining localization, minimizing false alarms, and adapting detection models to specific aircraft components to further enhance operational safety and reliability.  
Article  
Academic Editor: [PERSON]
# Research on Predicting the Mechanical Characteristics of Deep-Sea Mining Transportation Pipelines
## 4 Construction of the Predictive Model for Pipeline Mechanical Characteristics
### Construction Method of Pipeline Mechanical Properties Prediction Model
#### 4.1.3 Evaluation Metrics  
Based on the results of the trained BP neural network model, the effectiveness of the model training is typically assessed through metrics such as the mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient [43]. The calculation formulas are as follows:  
Mean absolute error (MAE):  
\[\text{MAE}=\frac{1}{h}\sum_{i=1}^{h}\lvert\gamma_{i}-\hat{\gamma}_{i}\rvert \tag{15}\]  
Root mean square error (RMSE):  
\[\text{RMSE}=\sqrt{\frac{1}{h}\sum_{i=1}^{h}\bigl{(}Y_{i}-\hat{\gamma}_{i} \bigr{)}^{2}} \tag{16}\]  
Correlation coefficient:  
\[\text{R}=\frac{\sum_{i=1}^{h}\bigl{(}Y_{i}-\hat{Y}\bigr{)}\bigl{(}\hat{Y}_{i}- \hat{Y}_{i}^{\prime}\bigr{)