esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Statement  
In the information-theoretic framework for steganalysis technology [9; 10], the difference between the probability distributions of the original carrier and the steganographic carrier can be quantified using the Kullback-Leibler (KL) divergence, which is expressed as follows:  
\[KL(\mathbf{P_{\mathrm{C}}}|\mathbf{P_{\mathrm{S}}})=\sum_{x\in\mathbf{C}}\mathbf{P_{\mathrm{C }}}(\mathbf{x})log\frac{\mathbf{P_{\mathrm{C}}}(\mathbf{x})}{\mathbf{P_{\mathrm{S}}}(\mathbf{x})}. \tag{1}\]  
For steganography technology [7; 13], the primary aim is to minimize the Kullback-Leibler (KL) divergence between the original carrier's and steganographic carrier's probability distributions, which enhances the imperceptibility of the hidden communication and makes detection more challenging. Conversely, steganalysis technology seeks to identify the presence of embedded secret information with maximum precision. This detection pro cess typically involves extracting distinctive features from both the original carrier and the steganographic carrier through a specific operation and then analyzing the KL divergence between these extracted features to determine the likelihood of covert communication.  
Steganalysis technology performs feature extraction operation \(\varphi\) by searching for maximization of \(KL(\varphi_{C}|\varphi_{S})\), thereby completing the corresponding detection task.  
Most existing steganalysis methods [19; 20; 21; 22; 23] for speech streams operate under the assumption that the embedding rate is known, which can be formally described as follows:  
\[\varphi_{k}=max\sum_{i=1}^{N}KL(\varphi_{C_{i}}|\varphi_{S_{i}}(a_{k})),a_{k} \in A. \tag{2}\]  
where \(N\) represents the number of samples, and \(a_{k}\) represents a specific embedding rate. From the above formula, it can be seen that most existing covert communication methods search for a transformation operation, \(\varphi\), that maximizes the Kullback-Leibler (KL) divergence between the original carrier and the steganographic carrier under the assumption that the embedding rate is known.  
For the steganalysis of speech streams with unknown embedding rates [24; 25], the formal description is as follows:  
\[\varphi=max\sum_{i=1}^{N}KL(\varphi_{C_{i}}|\varphi_{S_{i}}(A)). \tag{3}\]  
In Equation (3), \(A\) represents the summation across all embedding rates. By combining Equations (2) and (3), it can be seen that steganalysis of speech streams with unknown embedding rates eliminates the assumption of known embedding rates.  
However, existing steganalysis techniques [24; 25] for speech streams with unknown embedding rates exhibit certain limitations. First, many of these methods rely on manually crafted feature representations, which often fail to comprehensively capture the complex and subtle characteristics of steganographic modifications. Second, current approaches [24; 25] do not effectively account for the relationships between different embedding rates. Most detection models are trained separately for speech samples with varying embedding intensities, overlooking the intrinsic dependencies between different embedding levels.
# System Proposal for Mass Transit Service Quality Control Based on GPS Data
## 1 Introduction
Having reviewed related studies, introduced the proposed system and formalised the problem, in the fourth section we explain the challenges that must be tackled in quality control of a public road passenger transport service. The fifth section describes the proposed system, looking specifically at its operating principles and its constituent components; the system was tested under real conditions in a local transport company on the island of Gran Canaria. In the sixth section the results obtained when evaluating the quality of service are discussed, based on the criterion of punctuality on one of the company's routes for a period of one year. Finally, the seventh section contains the main conclusions.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
###### Abstract  
Narrowband Internet of Things (NB-IoT) is a promising technology for healthcare applications since it reduces the latency necessary in acquiring healthcare data from patients, as well as handling remote patients. Due to the interference, limited bandwidth, and heterogeneity of generated data packets, developing a data transmission framework that offers differentiated Quality of Services (QoS) to the critical and non-critical data packets is challenging. The existing literature studies suffer from insufficient access scheduling considering heterogeneous data packets and relationship among them in healthcare applications. In this paper, we develop an optimal resource allocation framework for NB-IoT that maximizes a user's utility through event prioritization, rate enhancement, and interference mitigation. The proposed Priority Aware Utility Maximization (PAUM) system also ensures weighted fair access to resources. The suggested system outperforms the state-of-the-art works significantly in terms of utility, delay, and fair resource distribution, according to the findings of the performance analysis performed in NS-3.  
NB-IoT; utility; interference mitigation; prioritized healthcare; resource allocation +
Footnote †: journal:  
Article
## 6 Conclusions  
This paper focuses on quantifying geometric information from rebar mesh, discussing methods for obtaining geometric data, detecting intersection features, and calculating mesh spacing.  
To extract geometric information, the study enhances the traditional Unet model by adding multiple residual blocks. The improved Unet model performs better in training and capturing rebar mesh features, especially in image segmentation tasks affected by noise. The inclusion of residual blocks also increased the model stability, optimization efficiency, training speed, and overall performance.  
For detecting rebar mesh intersections, the paper proposes an algorithm based on morphological operators and various filter sizes. The results show that intersection detection is reliable when compared with manual annotations and demonstrates robustness in complex reinforcement patterns. The paper also presents a method for calculating rebar spacing based on the detected intersections, offering an automated approach for mesh spacing calculation.  
This paper primarily focuses on the extraction and quantification of geometric features and the distribution of rebar mesh using deep learning image algorithms, and proposes a method for calculating the spacing of rebar mesh. This provides significant assistance in the on-site detection and verification of rebar mesh spacing. However, the current method for calculating rebar mesh spacing based on image feature patterns is still limited by external factors such as the shooting angle and mesh depth. Future research could develop a database of rebar mesh images captured from multiple angles and under varying conditions, and create more flexible methods for calculating rebar mesh spacing. This would enable automated measurement across different shooting angles, thereby improving the accuracy and adaptability of rebar mesh detection.  
Methodology, Y.X., H.L. and Y.S.; Validation, Y.S.; Formal analysis, Y.L.; Investigation, Y.L.; Writing--original draft, Y.S.; Writing--review & editing, Y.S.; Visualization, Y.L.; Supervision, Y.L.; Project administration, X.N.; Funding acquisition, Y.X., X.N. and H.L. All authors have read and agreed to the published version of the manuscript.  
Science and Technology Research and Development Program of CHINA RAILWAY (N2023G005). The APC was funded by Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited.  
Not applicable.  
Written informed consent has been obtained from the patient(s) to publish this paper.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Figure 10: Actual detection results of rebar mesh spacing: (**a–f**) Comparison of Detection Results and True Values for Different Rebar Specim Spacings.  
Conflicts of Interest: Authors [PERSON] and [PERSON] were employed by the company Beijing Jingwei Information Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Using Machine Learning to Predict Pedestrian Compliance at Crosswalks in Jordan
## 3 Materials and Methods
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{2}{c}{**Description**} \\ \hline City & Amman, Irbid, and Zarqa \\ Pedestrian Gender & 1 = male, and 2 = female. \\ Pedestrian Age & 1 = child, 2 = adult, and 3 = elderly. \\ Carrying Bags & 0 = pedestrian not holding bags, and 1 = pedestrian carrying bags. \\ Opposing Pedestrian & Number of pedestrians crossing in the opposite direction. \\ Pedestrian Direction & 0 = downstream to upstream, and 1 = upstream to downstream. \\ Crossing Conditions & 0 = single-stage crossing, 1 = two-stage crossing. \\ Using Mobile Phone & 0 for pedestrian not using mobile phone while crossing and 1 for pedestrian using mobile. \\ Pedestrian & 0 = pedestrian who does not cross within marked crosswalk and does not adhere to signal, 1 = \\ Compliance & pedestrian who complies with traffic signal and crosswalk together. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The selected features. Figure 1: Pedestrian crossing behavior at a signalized intersection No.1. The dataset comprising observations of 2437 pedestrians was utilized to develop predictive models employing four distinct ML techniques: artificial neural network (ANN), support vector machine (SVM), decision tree (ID3), and random forest (RF). The following sections offer a concise overview of each technique.
## 2 Theoretical Development  
### The Problem Description  
Suppose that a general \(n\)-degree-of-freedom vibration system consists of mass matrix \(\mathbf{M}_{0}\) and stiffness matrix \(\mathbf{K}_{0}\). The damping matrix is neglected, as the damping of many practical systems is small, such as a shafting that undergoes torsional vibration concerned in this paper.  
The undamped vibration system is described by a second-order ordinary differential equation as follows:  
\[\mathbf{M}_{0}\ddot{\mathbf{q}}+\mathbf{K}_{0}\mathbf{q}=0. \tag{1}\]  
In general, \(\mathbf{M}_{0}\) is a symmetric and positive definite matrix, and \(\mathbf{K}_{0}\) is a symmetric and positive-semidefinite matrix. \(\mathbf{q}\in\mathbf{G}^{n}\) is the displacement vector, and \(\ddot{\mathbf{q}}\in\mathbf{G}^{n}\) is the acceleration vector.  
Assuming the displacement vector \(\mathbf{q}(t)=\mathbf{x}\mathbf{e}^{\mathrm{i}\omega t}\) and \(\lambda=\omega^{2}\), and substituting them in Equation (1) yields  
\[(\mathbf{K}_{0}-\lambda\mathbf{M}_{0})\mathbf{x}=0. \tag{2}\]  
According to the Cholesky decomposition, mass matrix can be defined as \(\mathbf{M}_{0}=\mathbf{D}_{0}^{2}\). And then supposing \(\mathbf{u}=\mathbf{D}_{0}\mathbf{x}\), Equation (2) can be turned into:  
\[(\mathbf{J}_{0}-\lambda\mathbf{I})\mathbf{u}=0 \tag{3}\]  
where  
\[\mathbf{J}_{0}=\mathbf{D}_{0}^{-1}\mathbf{K}_{0}\mathbf{D}_{0}^{-1} \tag{4}\]so that \(\mathbf{J}_{0}\) has the same eigenvalues as the original system \(\{\mathbf{M}_{0},\mathbf{K}_{0}\}\).
# Visual-Inertial Navigation System Based on Virtual Inertial Sensors
## 5 Conclusions  
The system presented in this paper has demonstrated a significant improvement in localization and mapping accuracy in complex environments, characterized by challenging backgrounds and strong external light interference. To address this challenge, we propose a novel VIMU-aided visual-inertial SLAM system for walking scenarios. The proposed system leverages both visual and inertial measurements to improve the performance of the SLAM system. Furthermore, it incorporates a virtual inertial navigation module to compensate for distorted visual measurements caused by camera motion. Our experimental results demonstrate that the proposed system outperforms the VINS-Fusion system in terms of tracking accuracy and robustness, particularly in challenging scenarios that feature fast and large-scale camera motion, as well as inaccurate initial pose estimation. The proposed system also achieves a significant reduction in trajectory errors, as measured by the absolute trajectory error (ATE), compared to the VINS-Fusion system.  
Our findings suggest that the proposed VIMU-aided visual-inertial SLAM system has considerable potential for practical applications in walking scenarios for both indoor and outdoor navigation. However, the incorporation of an additional sensor as an information source unavoidably leads to increased complexity of the fusion method network structure and computational cost. Therefore, to expand the application scenarios of this fusion effect, it is imperative to investigate more lightweight solutions that enable better performance on devices with limited computing power as a future research direction.  
Conceptualization, Y.C., J.Z. and J.D.; Methodology, Y.C.; Software, Y.C.; Validation, Y.C., J.Z. and J.D.; Formal analysis, J.D.; Investigation, J.Z.; Resources, Y.C.; Data curation, J.Z.; Writing--original draft preparation, Y.C. and J.Z.; Writing--review and editing, J.D. and T.S.; Visualization, Y.C.; Supervision, W.Q.; Project administration, W.Q.; Funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Related Work
### Large Kernel Convolutional Neural Network  
In recent years, vision transformer models [36; 37; 38; 39] have undergone rapid development in the field of computer vision. The Transformer backbone with multi-head self-attention (MHSA) model possesses long-range dependencies (effective receptive fields) and adaptive spatial aggregation characteristics, which have enabled vision transformer models to achieve outstanding results in various domains. While scholars have widely recognized that models with larger effective receptive fields perform better in vision tasks, most current CNN models still rely on stacking 3 \(\times\) 3 regular convolutions to enhance the network's effective receptive field. However, even with considerable depth, networks based on stacked small-kernel convolutions still struggle to capture long-range dependencies as effectively as Transformer models, which limits their performance.  
Some old-fashioned models, such as Inceptions [40] and VGG-Net [41], use large kernel convolutions to increase the effective receptive field of the network. Large kernel models gradually fell out of favor after VGG-Net. However, with the recent development of vision transformer models, scholars have also started to investigate large-kernel models for CNNs to bridge the gap between CNNs and transformers. Lsnet [42] points out that a smaller receptive field can only observe local features, leading the network to overlook relationships between features. Conversely, an excessively large receptive field can cause the network to capture too much irrelevant information, thereby reducing network performance. Therefore, selecting an appropriate effective receptive field is crucial for different vision tasks. SKNets [43] proposes a multi-branch structure with multiple large convolutional kernels. The features extracted from each branch are selectively combined in the channel dimension. SKNets endows the network with the ability to choose an appropriate receptive field adaptively. ConvNeXt [44] uses a 7 \(\times\) 7 depth-wise convolution to increase the network's effective receptive fields, significantly improving the performance of CNNs in vision tasks. RepLKNet [45] uses large kernels up to 31 \(\times\) 31 to increase effective receptive fields, achieving compelling performance. SLaK [46] leverages dynamic sparsity to scale neural architectures with a 51 \(\times\) 51 kernel, demonstrating that using large kernel convolutions to enhance effective receptive fields allows the network to capture richer contextual information, significantly impacting network improvement. LSKNet [15] proposes a selectively large kernel network suitable for remote sensing object detection. It can adaptively select spatial receptive fields, enabling better contextual information extraction.
# TCCCD: Triplet-Based Cross-Language Code Clone Detection
## 3 Background
### Code Clone  
Code cloning refers to the existence of similar or identical code fragments in a software system, which achieve the same function [1]. According to the research [1], code clones can be classified into the following categories:  
**Type-I:** code fragments that have the same syntax except for whitespace and comments.  
**Type-II:** code fragments that have the same syntax as Type-I clones, but also include variations in identifier names and literal values.  
**Type-III:** code fragments that exhibit more syntax changes compared to Type-I and Type-II clones, such as added, modified, or deleted statements.  
**Type-IV:** code fragments that have different syntax, but still perform the same functionality.  
The focus of our research on cross-language code clones primarily revolves around Type-IV clones, which are characterized by code fragments that exhibit different syntax, but possess similar functionality [44]. The provided Figure 1 showcases a cross-language code clone example depicting the implementation of the linked list reversal functionality using Java and Python. It is evident from Figure 1 that distinct programming languages exhibit notable differences in syntax and structure. In particular, significant disparities are observed in syntax rules, keyword usage, class and method definitions, variable declarations, and overall statement composition between Java and Python. These variationshighlight the inherent distinctions encountered in syntax and structure when comparing code clones across different programming languages.  
When performing clone detection across different programming languages, a major challenge lies in establishing a unified intermediate representation. This unified intermediate representation should possess the following characteristics:  
1. **Cross-language compatibility:** Due to the differences in syntax, rules, and control symbols between different programming languages, the intermediate representation must overcome these diversities and be compatible with various programming languages.
2. **Information preservation:** The intermediate representation needs to accurately preserve important information from the source code, including variables, functions, classes, and control flow structures.
3. **Comparability:** The intermediate representation should be comparable, allowing code written in different programming languages to be compared at the level of the intermediate representation. This requires the intermediate representation to capture both the syntax and semantic features of the code, enabling effective matching and comparison operations.  
To address these challenges, we chose the pre-trained model UniXcoder for code representation, transforming code from different programming languages into a shared vector space for effective representation. This approach enables cross-language code comparison and analysis as the foundation.
## 2 Internal Force Calculation of Lining
### Influence of Pressureless Water Conveyance Level on Lining Internal Force
#### 3.2.2 Influence of Water Conveyance Level on Internal Force Distribution of Lining  
To analyze the influence of water level on the internal force distribution of lining, a tunnel with an inner radius of 3 m is taken as an example and the internal force values of lining at different angles at 1, 3 and 5 m water levels are calculated, respectively. Figure 10 shows the distribution of lining internal force values with angles under three water levels.  
It can be seen from Figure 10 that the higher the water conveyance level, the more obvious the difference in internal force values at different angles of the lining. When the water level is 1 m, the internal force of the lining is small. Then the internal force increases with the increase in water level. Under different water levels, the maximum values of bending moment and axial force are always at the bottom of the lining and the shear forces at the upper and lower ends of the lining are always 0. When pressureless water conveyance is compared with pressurized water conveyance, the internal force of the former is much smaller than that of the latter and the distribution of the former is more irregular due to the influence of water level height.  
Figure 10: Influence of water conveyance level without pressure on internal force distribution of lining. (**a**) Bending moment distribution diagram. (**b**) Axial force distribution diagram. (**c**) Shear force distribution diagram.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
###### Abstract  
Human fall detection is a significant healthcare concern, particularly among the elderly, due to its links to muscle weakness, cardiovascular issues, and locomotive syndrome. Accurate fall detection is crucial for timely intervention and injury prevention, which has led many researchers to work on developing effective detection systems. However, existing unimodal systems that rely solely on skeleton or sensor data face challenges such as poor robustness, computational inefficiency, and sensitivity to environmental conditions. While some multimodal approaches have been proposed, they often struggle to capture long-range dependencies effectively. In order to address these challenges, we propose a multimodal fall detection framework that integrates skeleton and sensor data. The system uses a Graph-based Spatial-Temporal Convolutional and Attention Neural Network (GSTCAN) to capture spatial and temporal relationships from skeleton and motion data information in stream-1, while a Bi-LSTM with Channel Attention (CA) processes sensor data in stream-2, extracting both spatial and temporal features. The GSTCAN model uses AlphaPose for skeleton extraction, calculates motion between consecutive frames, and applies a graph convolutional network (GCN) with a CA mechanism to focus on relevant features while suppressing noise. In parallel, the Bi-LSTM with CA processes inertial signals, with Bi-LSTM capturing long-range temporal dependencies and CA refining feature representations. The features from both branches are fused and passed through a fully connected layer for classification, providing a comprehensive understanding of human motion. The proposed system was evaluated on the Fall Up and UR Fall datasets, achieving a classification accuracy of 99.09% and 99.32%, respectively, surpassing existing methods. This robust and efficient system demonstrates strong potential for accurate fall detection and continuous healthcare monitoring.  
a +
Footnote †: journal: Future Internet  
_Article_
# Slot Optimization Based on Coupled Airspace Capacity of Multi-Airport System
## 3 Multi-Airport System for Cooperative Optimization of Flight Schedules  
### Methodology Framework  
The methodology framework proposed in this study is shown in Figure 3. The main framework for model operation includes model inputs, flight constraints, capacity constraints, airport resource constraints, multi-objective optimization, solving algorithms, and the Pareto front. This section will elaborate on the model's construction.