esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization-Based Approaches to Control of Probabilistic Boolean Networks
## 3 Polynomial Optimization Approach
### Problem Formulation  
For PBN (1), consider the following finite-time optimal control problem.  
**Problem 1**: _Suppose that, for PBN (1), the initial state \(x(0)=x_{0}\) and the control time \(N\) are given. Then, find a control input sequence \(u(0),u(1),\ldots,u(N-1)\) minimizing the following cost function:_  
\[J=E\left[\sum_{k=0}^{N-1}\left\{Qx(k)+Ru(k)\right\}+Q_{f}x(N)\mid x(0)=x_{0} \right], \tag{4}\]  
Figure 2: State transition diagram under \(u(0)=1\).  
_where \(Q,Q_{f}\in\mathcal{R}^{1\times n}\), \(R\in\mathcal{R}^{1\times m}\) are weighting vectors whose element is a non-negative real number._  
In this problem, we use some linear function with respect to the state and the control input as a cost function. Noting that the relation \(\delta^{2}=\delta\) holds for a binary variable \(\delta\), it is unnecessary to add the quadratic term such as \(x_{i}^{2}(k)\). Moreover, one of the main purposes in control of gene regulatory networks is that the expression of a certain gene is inhibited or activated (see e.g., [20]). Hence, simple linear cost function (4) is sufficient.
# Optimizing Flight Delay Predictions with Scorecard Systems
## 2 Literature Review
### An Innovative Approach-Scoreards
Their capacity to adjust to dynamic operational conditions, combined with their focus on simplicity and ease of use, renders them an indispensable asset for airlines aiming to augment operational performance, elevate passenger satisfaction, and secure long-term sustainability. As the aviation sector continues to evolve, the adoption of scorecards and similar innovative methodologies will be essential for maintaining competitiveness and ensuring high levels of service quality in an increasingly complex and demanding global market. In recent years, the growing use of AI/ML models in regulated industries, such as banking and insurance, has brought increased attention to the need for transparency in predictive systems. According to the European Banking Authority (EBA), for an AI/ML model to be approved for operational use in such environments, it must meet strict interpretability and auditability standards. Initially, the emphasis was on developing \"fully interpretable models\", but this has evolved into the broader concept of \"explainable models\", which do not necessarily require full transparency of internal logic, but must still provide insight into key model components, such as the list and order of input factors and their relative influence on the predicted outcome [38]. In parallel, the field of explainable artificial intelligence (XAI) has emerged, aiming to enhance complex AI/ML models with complementary tools--such as feature importance rankings, surrogate models, or counterfactual analysis--that make their decisions more understandable to human users [39]. In the context of transportation, recent studies have explored XAI applications to support interpretable delay predictions, passenger behavior modeling, and risk assessments in multimodal logistics. By positioning scorecards within this broader movement, our study responds to the increasing demand for transparent, auditable, and operationally feasible predictive tools, particularly in critical and high-impact sectors such as aviation. The long-term hypothesis is formulated as follows: every business area, in the first steps, will be covered by intuitive and fully interpretable scorecards methods; then, when the field is open to advanced analytics and many processes are covered by fully automatic tools with many predictive models inside, then AI/ML models will be a natural consequence, but scorecards should still be used to verify and test the correctness of the AI/ML.
## 3 Materials and Methods
### Dimensionality Reduction and Classification Stage  
The resulting matrices were then fused using LDA and projected into a 3D space to better separate the five classes by maximizing the \(S_{b}\) matrix and minimizing the \(S_{w}\) matrix of the features to cluster the data in groups as compact and as separated as possible, obtaining an optimal subspace, \(G\), from the eigenvectors of \({S_{w}}^{-1}S_{b}\) associated with the highest eigenvalues, as explained in Section 2.4. The projected data were then fed to train and test an SVM model with a linear kernel, a box constraint of 1, a one vs. one coding design, an Error-Correcting Output Codes (ECOCs) model, and a Hinge loss function, using 80% of the data for the training process and 20% for validation, to classify and differentiate the condition of the bearings.
# A Survey of Vehicular VLC Methodologies
## 3 Vehicular VLC Overview
### Prototype scenarios reviews
In [85], a commercial traffic signal transmitter was used to design and test a prototype for I2V VLC communications, where 200 Fresnel lenses were integrated into a photodetector at a receiver, based on an open-source microcontroller platform (Arduino Due) for decoding. The performance of the encoder was also evaluated. PER checked system performance and achieved error-free range coverage (PER \(\geq 10^{-5}\)) from 18 to 50 m with lower latency, making it suitable for 5G and cellular-V2X integration. The authors in [81] presented an analysis of the optical and communication performance of I2V VLC systems, where a typical traffic signal was used to transmit information to the receiver in a realistic implementation. The receiver was used on an amplified photodiode and an optical condenser, in addition to receivers in the no-lens case, and the results were shown. The use of different lens combinations is essential in the design of I2V VLC systems because they are realistically effective in ITS applications. The system was tested and compared for distances between 3 and 50 m, as a no-lens case does not achieve communication over distances greater than 20 m, even though it has a field of view exceeding 150 degrees. In [86], the prototype of the V2V VLC was tested under exposure to fluorescent lamps at night and in daylight conditions. The study focused on two degrading factors: the effect of vehicle misalignment and the maximum communication distance. A communication range of 75 m was achieved by relying on the vehicle's rear lights; thus, a narrow field of view and optical filters can improve the receiver device. However, evaluating the VLC system in real driving situations as compared with experiments remains relatively complex with regard to repeatability of measurement and communication safety and thus requires more effort and testing. In [87], the focus was on using the Angle Diversity Receiver (ADR) for vehicular VLC systems to enable multi-directional signal reception. Different scenarios for movement conditions were studied, such as left turn, U-turn, and right turn. Moreover, software-defined radio platforms were introduced to obtain preliminary results for experimental verification, where a packet delivery rate of 99% was achieved.
## 5 Conclusions  
In this study, an experiment was performed on RC columns with variables affecting the lateral confinement of the RC member, such as the configuration of the transverse reinforcement, the yield strength of the transverse reinforcement, and the axial force ratio. The main variables were configuration of transverse reinforcement, yield strength of transverse reinforcement, and axial force ratio. The conclusions obtained from the experiments are as follows.  
1. S-series specimens with alternating square and octagonal shapes showed maximum strength values similar to those of the H-series confined with square ties, even with a 55% decrease in transverse reinforcement amount. The spiral S-series transverse reinforcement effectively prevented buckling of longitudinal reinforcement and provided lateral confinement to the core concrete, thereby allowing specimens to exhibit ductile behavior.
2. Compared to the H-series, the S-series had a 1.1 to 2.2 times higher ductility factor. The S-series showed superior ductile performance even under high axial force ratio when the yield strength of transverse reinforcement was increased. The curvature in the plastic hinge zone was high for the S-series, comprised of high-strength transverse reinforcement. The use of high-strength spiral transverse reinforcement in RC members subject to high axial force is expected to significantly improve the lateral confinement, and improve the ductility.
3. Compared to the H-series, the S-series specimens except US30 showed similar or higher energy dissipation capacity, and were thus verified as effective against seismic loads.  
Conceptualization, M-J.K.; methodology, M-J.K.; validation, M-J.K.; formal analysis, M-J.K.; investigation, M-J.K. and B.-S.L.; resources, D.-H.K. and S.-P.H.; data curation, M-J.K. and B.-S.L.; writing--original draft preparation, M-J.K. and D.-H.K.; writing--review and editing, K.-H.K. and S.-P.H.; visualization, M-J.K. and D.-H.K.; supervision, K.-H.K.; project administration, M.-J.K.; funding acquisition, K.-H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 11: Calculate of energy dissipation (kN-mm).  
**Acknowledgments:** This work was supported by the Priority Research Center's Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1A6A1A03 032988). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A2B3001656). This research was supported by a grant (21 CTAP-C153039-03) from Technology Advancement Research Program (TARP) funded by the Ministry of Land, Infrastructure, and Transport of the Korean government.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
Temperature measurement was performed at 100 mA current injection to confirm the change in operation temperature after h-BN passivation; the average temperature for the LED with h-BN passivation was 33 \({}^{\circ}\)C, which is significantly lower than the temperature of 45 \({}^{\circ}\)C for the reference LED, as shown in Figure 4c. Both devices have a 3-min transition time from room temperature to working temperature when using a 100 mA injection current, before becoming saturated. The temperature drops because the heat is not localized just to the contact pad region during current injection and is effectively dispersed, since the thermal conductivity of h-BN (550 W/mK for basal plane, 30 W/mK for perpendicular) is substantially higher than that of GaN (253 W/mK) [23; 24]. Figure 4d demonstrates how, due to an overload of 8 V, the light output drops with time. Rather than waiting for the LED to entirely fail, this overload determines the LED's expected lifetime when the light output exceeds 70% of its previous luminous intensity. Because of the operational temperature of the devices, the light output of both devices decreases over time when the overload is applied. It took roughly 1010 h for a reference LED to attain 70% of its output, which is the predicted device lifetime, and it climbed to about 2500 h following h-BN passivation. The heat dispersion effect of h-BN is responsible for the increased lifetime expectancy, which can be proved by the fact that the metal has been effectively passivated to the LEDs. Figure 2: (**a**,**b**) Scanning electron microscope (SEM) image, (**c**) transmission electron microscopy (TEM) image and (**d**) Raman spectrum of h-BN film. Figure 4: Infrared camera images during operation of (**a**) reference LED and (**b**) LED with h-BN passivation. (**c**) Operation temperatures on both devices as a function of operation time at 100 mA. (**d**) Normalized light output decay of LED with and without h-BN passivation. Figure 3: Current-voltage (I-V) characteristics of the investigated devices at (**a**) forward bias and (**b**) reverse bias. (**c**) Electroluminescence (EL) spectra and (**d**) light output power of conventional and h-BN-passivated InGaN/GaN LEDs.
# Digital Shearography for NDT: Phase Measurement Technique and Recent Developments
## 4 Recent Developments of Shearographic NDT
### New Shearographic System with SLM  
As technology has advanced, new technologies have been applied to shearography, such as the spatial light modulator [56], to further improve system performance. In traditional shearography, the shearing amount is controlled by mechanical adjustment, and the piezoelectric ceramics that introduce the phase shift tend to be nonlinear, producing random errors that are difficult to eliminate. In 2018, [PERSON] et al. proposed a new shearographic system with a spatial light modulator [57] in which the spatial light modulator can accurately change the phase and shearing amount, eliminating the nonlinear random error with higher efficiency. The shearography system with spatial light modulator is shown in Figure 14a, while the experimental results are shown in Figure 14b,c. It can be seen that the quality of the phase diagram is good, so the system can meet NDT applications on the basis of precise control.  
Figure 14: _Cont._  
Figure 13: Experimental setup of directed acoustic shearography.
## 2 Materials and Methods
### Covariance Wavelet Analysis and Wavelet Transform  
The CWA uses the wavelet transform as a base. The continuous form of the wavelet transform (CWT) is expressed as the following Equation (1):  
\[W_{n}^{X}(s)=\sum_{n^{\prime}-1}^{N}x_{n^{\prime}}\psi*\left[\left(n^{\prime} -n\right)\frac{\delta t}{s}\right] \tag{1}\]  
where \(x_{n}\) is the time series, \(\psi\) is the Morlet mother function, (*) indicates the complex conjugate, \(n\) is the number of points, \(s\) is the wavelet scale, and \(\delta t\) is the localized time index [27]. The power to the CWT for a time series given (\(X\)) is defined as follows in Equation (2):  
\[\left|W_{n}^{X}(s)\right|^{2} \tag{2}\]  
The CWA takes the information obtained by the CWT to find patterns of activity in time-frequency spaces by means of the cross-wavelet transform (XWT), as previously described in detail [23].
# Three-Dimensional Convolutional Vehicle Black Smoke Detection Model with Fused Temporal Features
## 2 Related Works
### Object Detection  
In recent years, object detection tasks have witnessed substantial progress, particularly with deep learning-based methods such as the YOLO series models [10; 11] and Faster R-CNN [12]. Typically, such models consist of two main components, namely backbone and head. The backbone component is usually pre-trained on a common dataset, e.g., ImageNet [13] or COCO [14], for the feature extraction of the input image. On the other hand, the head component is used for predicting object classes and bounding boxes.  
Object detection models can be categorized into one-stage and two-stage detectors based on their head design. Notably, YOLOv4 [11] and SSD [15] are representative one-stage detectors, while RCNN [16] and Faster R-CNN [12] are prominent two-stage detectors. One-stage detectors directly regress bounding boxes and predict class probabilities from the input image, whereas two-stage detectors first generate region proposals before classifying them. In the post-processing stage, both detector types remove low-confidence bounding boxes and apply non-maximum suppression (NMS) to obtain the final detection results.  
According to the usage of temporal information, object detection models can be categorized into temporal context frame-oriented models (e.g., Faster-RCNN [12], R-FCN [17] and FPN [18]) and single frame-oriented models (e.g., vanilla YOLO models). Compared to the single-frame model, temporal context frame-oriented models incorporate temporal interactions between adjacent frames into the procedure [19]. This paradigm has been shown to mitigate the effects of image deterioration problems such as motion blur, background occlusion, and deformation [7]. We believe this is uniquely advantageous for the detection of black smoke, which is often indistinguishable from roads or shadows and blurs as the vehicle moves.
# Dielectric Spectroscopy Studies and Modelling of Piezoelectric Properties of Multiferroic Ceramics
## 1 Introduction
Therefore, the chemical composition 0.67 Bi\({}_{1.02}\)FeO\({}_{3}\)-0.33 BaTiO\({}_{3}\) (67 BFBT) was chosen as the research material due to its potential use as an alternative to lead-based piezoelectric materials [8; 11; 12]. The aim of the present research was to synthesise 0.67 Bi\({}_{1.02}\)FeO\({}_{3}\)-0.33 BaTiO\({}_{3}\) (67 BFBT) multiferroic ceramics and characterise the produced ceramics in terms of crystal structure, surface morphology, impedance spectroscopy, and piezoelectric properties, including modelling with the electric equivalent circuit method and the use of a circumferential magnetic field to reveal the effect of a weak magnetic field on the piezoelectric and dielectric properties of 67 BFBT ceramics.
# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 5 Results
### Comparative Insights
\begin{table}
\begin{tabular}{l l r r r r r} \hline \hline
**Dataset** & **Method** & **Level** & \multicolumn{2}{c}{
\begin{tabular}{c} **Time** \\ **(Seconds)** \\ \end{tabular} } & **Rules** & **Precision** & **Lightness** \\ \hline A & No Partition & 1 & 1883.783 & 10.000 & 0.774 & 6.200 \\  & Random & 2 & 8.331 & 2.400 & 0.454 & 2.170 \\  & Correlation & & 76.361 & 10.000 & 0.774 & 6.200 \\  & Random & 3 & 4.826 & 3.800 & 0.530 & 2.960 \\  & Correlation & & 76.500 & 10.000 & 0.774 & 6.200 \\  & Random & 4 & 4.616 & 1.000 & 0.226 & 1.000 \\  & Correlation & & 26.385 & 10.000 & 0.774 & 6.200 \\  & Random & 5 & 4.391 & 0.200 & 0.077 & 0.200 \\  & Correlation & & 12.405 & 10.000 & 0.774 & 6.200 \\  & Random & 6 & 4.281 & 0.600 & 0.151 & 0.600 \\  & Correlation & & 3.894 & 2.000 & 0.768 & 2.000 \\ \hline B & No Partition & 1 & 966.701 & 19.000 & 0.797 & 10.477 \\  & Random & 2 & 5.970 & 9.600 & 0.786 & 6.367 \\  & Correlation & & 5.657 & 14.000 & 0.789 & 7.277 \\  & Random & 3 & 3.930 & 6.100 & 0.615 & 4.029 \\  & Correlation & & 4.124 & 14.000 & 0.789 & 7.277 \\  & Random & 4 & 3.618 & 3.500 & 0.466 & 2.311 \\  & Correlation & & 4.117 & 7.000 & 0.765 & 3.873 \\  & Random & 5 & 3.533 & 1.900 & 0.544 & 1.467 \\  & Correlation & & 3.884 & 1.000 & 0.779 & 1.000 \\ \hline C & No Partition & 1 & 623.870 & 34.000 & 0.819 & 12.196 \\  & Random & 2 & 6.225 & 14.600 & 0.794 & 7.099 \\  & Correlation & & 151.448 & 46.000 & 0.812 & 16.217 \\  & Random & 3 & 4.205 & 9.200 & 0.783 & 4.534 \\  & Correlation & & 16.231 & 26.000 & 0.809 & 10.634 \\  & Random & 4 & 3.614 & 5.500 & 0.766 & 3.984 \\  & Correlation & & 7.606 & 12.000 & 0.784 & 6.088 \\  & Random & 5 & 3.494 & 5.000 & 0.761 & 3.924 \\  & Correlation & & 2.779 & 7.000 & 0.766 & 3.719 \\  & Correlation & & 3.566 & 4.700 & 0.758 & 3.929 \\  & Correlation & & 2.964 & 4.000 & 0.754 & 4.000 \\  & Random & 7 & 3.411 & 5.200 & 0.761 & 3.924 \\  & Correlation & & 2.918 & 4.000 & 0.754 & 4.000 \\  & Random & 8 & 3.504 & 4.000 & 0.754 & 4.000 \\  & Correlation & & 2.795 & 4.000 & 0.754 & 4.000 \\  & Random & 9 & 3.528 & 4.100 & 0.756 & 3.986 \\  & Correlation & & 2.645 & 4.000 & 0.754 & 4.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Method Performance Metrics Across Datasets and Feature Groupings. reliable outcomes in a single run, a relatively large number of extracted action rules, their high confidence, and high lightness (in comparison to \"random\" methods) present a preferable alternative, particularly when dealing with large datasets or applications where the quality and diversity of discovered action rules are essential.
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 5 Discussion and Conclusions
### Limitations  
The implementation of a fall detection system utilizing YOLO algorithms with pose estimation presents significant challenges due to its demand for substantial computational power required for real-time processing. The high-quality INSTA360 GO3 camera, the high-performance GPUs, and the additional hardware are cost-effective but required to achieve the desired speed and accuracy of the system. Addressing these constraints will be important for broader deployment and efficient system maintenance.  
Currently, the system differentiates a fall from someone lying down based on the sudden and abrupt movement typically associated with a fall. However, despite the high accuracy of YOLO, no system is infallible, and false positives and false negatives can still occur. False positives may lead to unnecessary stress and resource utilization, while false negatives could result in missed emergencies. Expanding the system's ability to cover edge cases will be essential to ensuring reliable fall detection in diverse environments. Continuous refinement of the algorithms and rigorous testing are necessary to minimize these issues, as the dynamic nature of fall scenarios and varying environmental conditions require ongoing adjustments to maintain high detection accuracy and overall system reliability.  
The system was not evaluated (1) under the full range of lighting variations found in real-world environments (for example, mixed lighting, rapid lighting changes, or areas with shadows may impact the system's accuracy); (2) in real-world environments where parts of a person's body may be hidden by objects, furniture, or other people, which can make it difficult for the system to detect a fall accurately; (3) taking into account the full diversity of physical abilities of the individuals being monitored (differences in movement patterns, walking speed, and the manner in which individuals fall can all influence detection accuracy); or (4) in scenarios where a person is performing exercises on a mat on the floor. The system has not been tested to determine if such activities may trigger false alarm notifications, as movements during exercise could resemble a fall.
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 2 Related Works
The above approaches employed CNN to model spatial dependence and made great progress in some prediction tasks. However, CNN is applicable to Euclidean space and is mostly used to process raster data. It has limitations in dealing with geographically distributed networks with complex topology. Therefore, it cannot capture spatial dependence accurately. Recently, GCN has received a lot of attention promoted by CNN, which can obtain local features from graph structures. For instance, [PERSON] et al. [20] used GCN to capture the topology of urban road networks to obtain spatial dependence inan urban traffic prediction task. The experiments showed that the prediction has good performance. In this research, we mainly considered the spatial distribution characteristics of the observation stations in terms of geographical location. The GCN network is employed to analyze the geospatial relations of the target area and its multi-order neighbors to capture spatial features between nodes. We summarize the advantages and disadvantages of existing temperature prediction methods, as shown in Table 1.  
Considering the temporal features of multidimensional meteorological data and the spatial features of urban areas, this research takes advantage of the multi-meteorological element model by considering various meteorological influences such as temperature, wind speed, and precipitation, and sets up a time-sliding window. Simultaneously, considering the meteorological spatial features of the surrounding sites and learning the relationships of meteorological elements between different regions, we integrate GCN and BiLSTM to build a temperature prediction model combining the bidirectional propagation characteristics of BiLSTM and the spatial data features extraction of GCN. This model will enhance the network capacity and model complexity to improve the accuracy of temperature prediction further.