esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Homogenization of Single-Walled Corrugated Board with Imperfections
## 2 Materials and Methods
### Numerical Model with Imperfections  
The main goal of this work was to implement geometric imperfections into the numerical model and to determine their influence on the stiffness reduction of corrugated board. Cardboard with a B flute (see Table 2) and material parameters contained in Table 1 were analyzed.  
The application of imperfections in the numerical model of corrugated board consists in the appropriate definition of the buckling mode of the RVE. In the layers subjected to compression, the influence of imperfections is crucial. Therefore, due to the nature of the load, two cases should be considered: compression and bending. During compression, the entire cross section of the RVE is compressed, i.e., both the bottom and top liners. With regard to bending, one liner is compressed (assumed to be the bottom liner) and the other is tensioned. In the case of the liner, which is stretched, the imperfection does not affect the load capacity (in other words, it is not present); therefore, the tensile layers can be modeled as undeformed. During compression and bending, all or part of the middle cardboard layer is compressed. However, it was assumed that the influence of imperfections in this layer on stiffness is negligible due to the already existing corrugated shape of fluting.  
The load direction of the corrugated board also affects the mode of buckling. During compression or bending in the machine direction, imperfections in the compressed layers are in the form of a half-sine wave across the entire depth of the RVE, where the support is the connection between liners and fluting (see Figure 4a,b). On the other hand, if the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wave (Flute)** & **Wave Length (mm)** & **Height (mm)** & **Take-Up Factor (+)** \\ \hline B & 6.5 & 2.46 & 1.32 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric parameters of waves.  
cardboard is loaded in the cross direction, then a half-wave appears in both directions, which causes dome-shaped imperfections. In this case, the support was provided by the external edges of the liners in the cross direction and the connection of the liners with fluting (see Figure 4c,d). Corrugated board may also contain imperfections that reflect in-plane shear and twisting of the RVE. For this reason, it was necessary to simulate these two load cases (see Figure 4e,f).  
Based on the above description, six modes of imperfection were analyzed, and thus six numerical models, which are presented in Figure 4. In each case, the amount of imperfections, i.e., the maximum deviation from the ideal shape (red color), was 5% of the cardboard height, which was 0.123 mm. For finite element method computation, the four-node quadrilaterals shell elements with reduced integration (S4R elements) were used. In all seven numerical models (one model without imperfections and six models with imperfections), the same mesh size was assumed. The approximate global size of the element was 0.1625 mm, which gave 4920 nodes, 4800 elements and 29,520 degrees of freedom.
## 4 Parametric Study FEM Buried Pipe under Haul Road
### General Considerations  
The most important considerations taken into account for the development of the parametric analysis are described below:  
* It is considered that the contact walls of the trench are made of natural soil in order to more accurately estimate the behaviour of the pipe (see Figure 17). Additionally, it is considered that the trench walls do not require external control measures due to possible instability during its construction.
* For the calculation of the residual lateral pressure of the lateral backfill on the pipe, the stiffness of the trench is taken into account.
* The model considers longitudinal and transverse symmetry to reduce computational costs (see Figure 20).
* The surface load applied on the pipe is located in the central area of one of the rear wheel prints and the pipe is considered to be unaffected by the pressure of the front axle tire of the mining truck or the lateral rear wheel.
* According to the model boundary conditions, a restriction on the longitudinal displacement of the soil and the pipe is considered. A qualitative analysis of the effect of the model length on the variation in the vertical stress results produced by a surface load was carried out for model lengths of 3 m, 6 m, 9 m, 12 m and 15 m, for mesh sizes of 0.12 m. According to the results, it is concluded that the longer the pipe length, the less affected by this restriction are the results. Thus, for the parametric analysis, a model with a total length of 12 m and a minimum and maximum mesh density of 0.05 m and 0.5 m, respectively, was considered (longitudinal and transversal symmetrical model). Even when the results of vertical pressures on the pipe are slightly affected, the trends of stresses and deformations in the pipe will remain basically the same.
* To ensure that the pipe does not fail and to suggest design recommendations, the admissible stresses of the ASTM A53 Grade B material pipe will be considered in the analysis of the results.
## 3 System Framework and Algorithm Procedure
### Basic DMAC Procedure  
In this section, we detail the procedures of basic DMAC algorithm. We assume that four nodes (denoted as Nodes 1, 2, 3 and 4) connect to the AP and attempt to contend the channel to facilitate the demonstration of DMAC. The basic DMAC includes two procedures: contention processing and data transmission processing. Figure 2 demonstrates the workflow for two phases.  
In the contention phase, each node selects a random value within a minimum CW as its back-off counter and utilizes CSMA/CA mechanism to compete for the channel. Different from legacy IEEE 802.11 CSMA/CA mechanism, all of the nodes get the same CW by the beacon from the AP and this value is determined according to the number of node in the network. Meanwhile, the node immediately sends a DTR when its counter decreases to zero and the channel is idle. Otherwise, the node will give up this opportunity in the current turn if the collision incurs. Finally, the AP broadcasts a beacon frame to indicate the termination of the contention period. During this phase, the AP follows a rule of First Come First Serve (FCFS) and it puts all nodes that successfully transmit a DTR into CQ. Factually, all of the nodes can be recorded in the queue through their association IDs that are assigned by the association response management frame from the AP during association phase.  
In the data transmission phase, the AP picks up first node in the contention queue and it broadcasts a beacon to initiate a data transmission process. The beacon triggers all of the nodes to perform a matching algorithm. Subsequently, the matching node utilizes the subsequent time slot to transmit a data frame. After successfully receiving the data frame, the AP will acknowledge it and piggybacks the ID of the subsequent node into this acknowledgement frame. The subsequent nodes in the CQ can overhead this ACK frame, thus also obtaining a transmission opportunity. Successively, all of the nodes in the CQ will be consecutively scheduled to send a data frame without any collision incurring during data transmission phase. Similar to DCF, A Short Inter-Frame Space (SIFS) is inserted into any two data transmission interval after a successful transmission. Thus, a bigger inter-frame space, such as Distributed Inter-Frame Space (DIFS), automatically triggers a new contention process.
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
## 4 Discussion
### Design of Dental Base  
The results of this study demonstrate that the IMW is one of the two measurements that showed lower accuracy and precision than ICW for all 3 material groups. Orthophathic surgical splints are designed to engage the occlusal contours of the maxillary and mandibular teeth and have a fitting surface on both sides. For this reason, the splints were designed and printed in a horseshoe shape without any bases. However, horseshoe-shaped models are at greater risk of distortion particularly towards the posterior end. A study conducted by [PERSON] et al. reported that models with horseshoe-shaped bases print less accurately than other types of dental bases such as the American Board of Orthodontics base design or a horseshoe design with a posterior connection, both of which were shown to be accurate [25]. An addition of a structure that connects the left and right sides of the posterior ends of the virtual OSS at the designing stage may help to improve the accuracy and precision of the 3D-printed splints. This additional support structure will need to be removed prior to the OGS, as it would interfere with the position of the tongue and prevent the OSS from being seated properly.
# Broadband Flexible Microstrip Antenna Array with Conformal Load-Bearing Structure
## 3 Theoretical Analysis
### The Equivalent Medium Model of the Honeycomb Core Layer
In the transmission coefficient and the maximum error of the cell equation and layer equation are within 0.01 dB and 0.004 dB, respectively. Thus, the layer equivalent is in good agreement with the honeycomb core layer in the EM performances. In short, the equivalent EM parameters of the honeycomb core layer can be effectively extracted in the frequency domain through full-wave analysis. Then, the axial homogeneous layer can be established through the equivalent EM parameters. Hence, the equivalent homogeneous medium plane can be used for the design and simulation of CLAS due to its high accuracy in EM performance, simplicity in the model, and simulation efficiency.
## 3 Preliminaries  
To achieve permanent operation of a WRSN, timely replenishment of energy for sensors after sending charging requests is necessary, ensuring all nodes can work steadily during network operations. Moreover, allocating requests for replenishment by the vehicle or drones is a key issue for the novel WRSN model. To reduce the number of nodes that run out of energy, some nodes with an urgent need for charging or located far from the base station should be arranged to be charged by drones. Therefore, finding an optimal allocation scheme of nodes to satisfy all the charging requests is the main issue that needs resolving.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Literatures** & **Vehicle** & **Drone** & **Pad** & **Scheduling Scheme** \\ \hline Proposed model & Used & Used & Not used & LDFM \\ [33] & Used & Used & Used & \(K\)-mean, greedy, static \\ [25] & Used & Used & Not used & Hybrid \\ [13] & Used & Not used & Not used & Genetic \\ [15] & Used & Not used & Not used & Gravitational search \\ [20] & Not used & Used & Not used & Iterative \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the proposed model with the previous literature.
# Unveiling the Shadows--A Framework for APT's Defense AI and Game Theory Strategy
## 5 Conceptual Framework
### APT Defense Methodology  
According [30] to [PERSON], [PERSON], [PERSON], Ruan, Yang, Cheng, and Conan, advanced persistent threats (APTs) pose significant security challenges due to their stealth, persistence, and adaptability. This paper presents a hybrid framework that integrates machine learning-based anomaly detection with game-theoretic defense strategies to enhance cybersecurity resilience. We develop the isolation forest algorithm for unsupervised anomaly detection and a Stackelberg game model for optimal defensive decision making; this methodology achieves robust detection and mitigation of APTs. Experimental evaluations using synthetic network traffic demonstrate the stability and effectiveness of this approach, providing a scalable, automated defense mechanism against evolving cyber threats [22, 29, 31].  
The proposed architecture comprises three functional layers: a data preprocessing module for normalization and cleaning, an anomaly detection engine using isolation forest, and a strategic decision layer that applies Stackelberg game modeling for real-time mitigation and response. First, the anomaly detection layer processes raw network data using the isolation forest algorithm to detect deviations from normal behavior. Next, the strategic modeling component uses a Stackelberg game to anticipate attacker behavior and calculate optimal defensive actions. Finally, an automated response engine executes mitigation procedures, including user isolation and credential revocation.  
As illustrated in Figure 4, the proposed defense framework follows a logical flow starting from data acquisition to autonomous mitigation. The model continuously monitors system and network behavior, detecting anomalies through an unsupervised learning process. Upon identification of suspicious activity, a game-theoretic model predicts the attacker's potential next steps and generates optimal defense strategies. Importantly, the framework includes a feedback loop that allows the system to learn from past incidents, improving accuracy and adaptability over time. This modular architecture ensures scalability, real-time responsiveness, and proactive adaptation to evolving APT tactics. Most current approaches rely on static detection mechanisms and lack adaptability to novel APT tactics. Our framework addresses this gap by integrating dynamic anomaly detection and strategic game modeling.
# A Network Analysis-Based Approach for As-Built BIM Generation and Inspection
## 3 Methodology
### BIM Reconstruction  
As-built BIM is reconstructed from point cloud data using the proposed method, and the four-room apartment point cloud dataset utilised in the experiment is presented in Figure 2.  
The process begins with the acquisition of point cloud data using laser scanning technology, while the key process is outlined as follows:  
(1) Data Acquisition and Preprocessing:  
Point Cloud Acquisition: High-resolution point cloud data are collected using laser scanners. These data include millions of points that capture the precise details of the building's structure.  
Preprocessing: The raw point cloud data often contain noise and unnecessary points. Preprocessing steps, such as denoising and simplification, are crucial to ensure data quality. Techniques, like enhanced plane boundary line detection and corner recalibration algorithms, are employed to refine the data.  
(2) Segmentation and Classification:  
Segmentation: The pre-processed point cloud data are segmented into different regions corresponding to various building components such as walls, floors, ceilings, and other structural elements. Region segmentation methodology is widely used to achieve this.  
Classification: Once segmented, the data are classified into recognisable building elements. This involves using domain knowledge-based heuristic methods to analyse features and associate them with specific building components.  
(3) 3D Modelling and BIM Integration:  
3D Model Generation: Using the classified data, 3D models of the building elements [41] and mechanical, electrical and plumbing engineering (MEP) components [4] are generated. For example, the detection of walls and doors involves template-matching algorithms to create accurate geometrical representations.  
BIM Integration: The 3D models are then converted into BIM objects in the IFC format, which is compatible with various BIM software. This step ensures that the spatial relationships and geometries of the building elements are accurately represented in the BIM model.
## 5 ML-Powered Smart IS Leveraged for Multi-Objective Healthcare Management
### Patient Monitoring Systems  
Patient monitoring systems use progressive ML methods to frequently gather, assess, and interpret patient data in real-time, presenting precious perspectives for healthcare providers. By leveraging multi-objective optimization fundamentals, these models aim to gain a balance among different healthcare purposes, like developing patient findings, optimizing resource application, and increasing total healthcare effectiveness. Patient monitoring systems promote individualized medicine by customizing interventions according to personal patient profiles, enabling timely and targeted healthcare responses. The incorporation of ML increases the adaptability and predictive capabilities of these models, promoting the early detection of potential health challenges and allowing proactive healthcare management that is in alignment with a variety of healthcare purposes. In this regard, [PERSON] and [PERSON] [46] presented a method, termed \"Multi-objective Chaotic Butterfly Optimization\", in conjunction with deep neural networks (DNN), for the optimization of a sustainable healthcare management structure. Their system supports chaotic butterfly optimization algorithms with DNNs to solve the complications of healthcare management. By considering several impacts concurrently, like sustainability, effectiveness, and resource application, the presented strategy sought to increase decision-making in healthcare models. On the other hand, [PERSON], [PERSON] [47] solved the statistical multi-objective optimization of sensor networks and the extraction of optimum detectors for structural health monitoring (SHM). Their research contributed to developing structural health tracking by optimizing sensor placement in networks. The statistical multi-objective optimization technique is used to balance numerous targets concurrently. Moreover, the authors looked into the extraction of optimal detectors, highlighting their significance in the area of SHM. [PERSON] [PERSON], [PERSON] [48] proposed a distance coefficient-multi-objective information fusion algorithm modeled for optimal sensor placement in SHM. Their multi-objective technique considered different parameters simultaneously, enabling a well-balanced and holistic sensor network model. Their algorithm develops the effectiveness and accuracy of SHM by strategically placing sensors according to a combination of distance coefficients and several milestones, proposing a complex solution for optimal sensor placement in structural health tracking applications. Also, [PERSON], [PERSON] [49] presented the DIOS (design and improvement of surveillance) system, which is structured for optimizing infectious disease surveillance. Their model leveraged numerical techniques to simulate and optimize surveillance network topologies within numerous goals. Their stress is on improving an effective surveillance sys
# Fault Diagnosis via Neural Ordinary Differential Equations
## 4 Fault Diagnosis via Neural ODE Applied to Nonlinear Benchmark System
### Data-Set  
The data-set for the Four Tank Process are generated using the exact model of the system, including typical dynamic possibilities present in the real system, such as controllers, set-point modifications, measurement noise, perturbations, and also actuators, sensors and process faults. A total of 1,000,000 samples are obtained for each case (actuator, sensor, and process fault scenarios), considering only the known signals (measurements and inputs) and the labels for each fault. An extract of the whole data-set is shown in Figure 3, and its corresponding actuator faults are shown in Figure 4.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Units** \\ \hline Bottom area, \(A_{i}\), for \(i=1,2,3,4\) & cm\({}^{2}\) \\ Outlet pipe cross section, \(a_{i}\), for \(i=1,2\) & cm \\ Outlet pipe cross section, \(a_{i}\), for \(i=3,4\) & cm \\ Gravity constant, \(g\) & cm\({}^{2}\)/s \\ Pump constants, \(k_{i}\), for \(i=1,2\) & cm\({}^{3}\)/(Vs) \\ Main valve positions, \(\gamma_{i}\) for \(i=1,2\) & \\ Heights 1 and 2, \(h_{1o}\), \(h_{2o}\) & cm \\ Heights 3 and 4, \(h_{3o}\), \(h_{4o}\) & cm \\ Inputs 1 and 2, \(u_{1o}\), \(u_{2o}\) & cm\({}^{3}\)/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Process Parameters.  
Figure 3: An extract from the data-set of the four tank system. Above: Inputs, Below: Measurements.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 3 Proposed Methodology
### Hybrid Fusion Mechanism  
Building upon the dual-stream architecture, we further introduce the Attention-Enhanced Feature Fusion (AEFF) module to improve modality interaction and cross-modal feature utilization. To fully exploit multispectral information and achieve effective modality complementarity, this study introduces the AEFF, which integrates cross-modal feature enhancement (CFE) and iterative learning strategies (ICFE), adaptive aggregation pooling and maximal pooling, and the convolutional and attentional fusion module (CAFM). The primary objective of AEFF is to maximize the retention of useful features while minimizing computational complexity, thereby improving the network's adaptability to complex environments. Figure 3 illustrates the structure of the AEFF module, highlighting its role in enhancing feature fusion through convolutional and attentional mechanisms.
## 2 Theoretical Basis
### The NAT of Misaligned System
Referring to (3), \(\beta_{1}\) and \(\beta_{2}\) are related to the product of the third-order coma coefficient \(W_{131}\) and the aberration field decenter vector; \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\) are related to the third-order astigmatism coefficient \(W_{222}\) and the aberration field decenter vector. Obviously, the effect of reducing the sensitivity of the optical system can be achieved by reducing the third-order aberration coefficient of each surface. The aberration coefficient is related to the incident angle and height of the edge ray and the incident angle of the chief ray, the refractive index of the lens and the \"aplanatic\" parameter. It is consistent with the previous research to reduce the sensitivity of the optical system by optimizing the incident angle and deflection angle and balancing the primary aberration coefficients of each surface. The difference is that tilt sensitivity has a different relationship to the field of view. The sensitivity of the astigmatism terms is linearly related to the field of view, where \(\alpha_{1}\), \(\alpha_{2}\) are the coefficients of the first-order term, and the \(\alpha_{3}\) is constant term. The sensitivity of the coma terms has nothing to do with the field of view, where \(\beta_{1}\), \(\beta_{2}\) are the coefficients of the coma terms. The sensitivity of the aberration term in the field of view is measured by the aberration coefficient related to the misalignment. For systems with the same tilt angle and different tilt directions, the absolute values of \(\beta_{1}\), \(\beta_{2}\), \(\alpha_{1}\), \(\alpha_{2}\) and \(\alpha_{3}\) are equal, that is, the first-order coefficients of the sensitivity of the astigmatism terms, the constant term of the sensitivity of the astigmatism terms and the coefficients of the coma terms are equal in absolute values. Considering the different correlation between the sensitivity of the aberration term and the field of view, the tilt sensitivity of the secondary mirror in the optical system has different weights in different fields of view. Therefore, the relationship between the third-order aberration coefficient, the aberration field decenter vector and the field of view must be considered comprehensively to meet the requirement of reducing the tilt sensitivity of the secondary mirror in the catadioptric optical system.