esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrofitting of Steel Structures with CFRP: Literature Review and Research Needs
## 1 Introduction  
Steel structures around the world are susceptible to deterioration over their service life period. This deterioration can reduce the potential strength and stiffness of the steel members due to cracks and corrosion induced by fatigue loading and extreme weather conditions, respectively. Additionally, defects may arise during the design and construction phases. The conventional approach for retrofitting steel structures is by using steel plates that are attached to the structure by weld or bolt [1]. However, this approach presents disadvantages, including the residual stress imposed by welding, which can lead to new damage to the structure [2, 3]. Furthermore, the steel plates are susceptible to corrosion and their heavy weight poses challenges during installation [4, 5]. Alternatively, the application of externally bonded carbon fiber-reinforced polymer (CFRP) can offer a durable solution to address these challenges [6, 7]. The high strength-to-weight ratio and corrosion resistance of CFRP materials have an important role in their selection for retrofitting steel components [8, 9, 10].  
In recent years, the application of advanced composite materials has been gaining acceptance in retrofitting civil infrastructures. Among these types of materials, CFRP and graphite fiber-reinforced polymer (GFRP) are well established [11]. However, CFRP exhibits superiority over GFRP due to its higher strength. Studies show that CFRP retrofitting systems can effectively enhance the flexural capacity of steel members and prolong their fatigue life [4, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]. CFRPs are classified based on their elastic modulus into Low Modulus (LM), Normal Modulus (NM) or Intermediate Modulus (IM), High Modulus (HM), and Ultra-High Modulus (UHM). There is not a unanimous approach to characterize the elastic modulus range of each category. However, it can be expressed relative to the steel elastic modulus as presented in Table 1[33].  
The significance of this review lies in its comprehensive and up-to-date examination of the current state of knowledge on steel elements retrofitted with CFRP. This paper integrates findings from multiple dimensions and examines key parameters affecting the performance of CFRP retrofitted components and the bond behavior between CFRP and steel interfaces, including adhesive types, surface preparation, bond length, etc. By synthesizing findings from various investigations, this review aims to guide future research efforts and enhance the application of CFRP in retrofitting steel structures.  
This paper is presented in the following sections: (i) a review of studies on the bond behavior between CFRP and steel; (ii) research findings regarding the flexural behavior of the retrofitted steel beams; and (iii) a review of numerical simulations of CFRP/steel retrofitted elements and their bond behavior. Finally, gaps in previous research are identified, and suggestions for further investigations are provided.
## 2 Methods
### Data Collection and Analysis
#### 2.5.2 Joint Coordination Analysis  
A three-dimensional motion capture system (Qualisys system) was used to collect the kinematic data of the lower limb joints of the subjects during natural gait walking. The system is equipped with 8 infrared cameras, and the sampling frequency is 200 Hz. In total, 48 reflective markers were precisely pasted on the skin of the subjects. The pasting positions include the left and right acromiolacicular joints, iliac crests, greater trochanters, medial and lateral epicondyles of the knee joint, medial and lateral malleoli, and the first and fifth metatarsal heads. In addition, there are the anterior superior iliac spine, posterior superior iliac spine, a rigid plate with four markers connected to the thoracic spine, bilateral thighs, and the lower legs with elastic Velcro straps, as well as three marker-rigid plates connected to the heels. The kinematic data reduction was calculated using Visual 3D software ([[https://www.visual-3d.com/](https://www.visual-3d.com/)]([https://www.visual-3d.com/](https://www.visual-3d.com/)) (accessed on 30 March 2025)). The original kinematic and kinetic data were filtered using a fourth-order, zero-lag recursive Butterworth filter with a cut-off frequency of 20 Hz. Using the angle-angle diagram to construct the vector of the angle between any two segments in the time series, the coupling angle was accurately calculated. The coupling angle is the angle of this vector relative to the horizontal line, ranging from 0\({}^{\circ}\) to 360\({}^{\circ}\). Based on the improved vector coding technology, the changes in joint coordination during the gait cycle were deeply analyzed, specifically including the coordination between the hip-knee joint, hip-ankle joint, and knee-ankle joint (see Figure 3) [18].  
Classification of Coordination Modes (Figure 4):  
1. In-Phase Coordination: When the coupling angle is in the range of 22.5\({}^{\circ}\) to 67.5\({}^{\circ}\) or 202.5\({}^{\circ}\) to 247.5\({}^{\circ}\), it indicates that the two segments rotate synchronously in the same direction.
2. Antiphase Coordination: When the coupling angle is in the range of 112.5\({}^{\circ}\) to 157.5\({}^{\circ}\) or 292.5\({}^{\circ}\) to 337.5\({}^{\circ}\), it indicates that the two segments rotate in opposite directions.
3. Proximal Phase Coordination: When the coupling angle is in the range of 157.5\({}^{\circ}\) to 202.5\({}^{\circ}\) or 337.5\({}^{\circ}\) to 360\({}^{\circ}\), it indicates that the movement mainly occurs in the proximal segment.
4. Distal Phase Coordination: When the coupling angle is in the range of 67.5\({}^{\circ}\) to 112.5\({}^{\circ}\) or 247.5\({}^{\circ}\) to 292.5\({}^{\circ}\), it indicates that the movement mainly occurs in the distal segment.
## 5 Characterization Using the Time Domain Measurement Data
### Modeling of Time-Varying Characteristics
#### 5.2.2 CHMM Modeling Method  
The solid red line in Figure 10a shows the time domain waveform of the electromagnetic noise measured at 315 MHz. First, an approximate estimate of the three elements for the \(\lambda\) is given. According to the measured data, the estimated values of \(P\) and \(I\) are:  
\[P=\left[\begin{array}{cc}0.995&0.005\\ 0.1&0.9\end{array}\right] \tag{10}\]  
\[I=\left(0.9,0.1\right)^{T} \tag{11}\]  
To make a rough estimate of the value of \(B\), it is actually to estimate the four values of \(\mu_{1}\), \(\mu_{2}\), \(\sigma_{1}\), and \(\sigma_{2}\). We select the threshold according to the method in [PERSON] (1953) and take the mean and variance of the noise below and above the threshold as a rough estimate of \(\mu_{1}\), \(\sigma_{1}\), \(\mu_{2}\), and \(\sigma_{2}\). Based on the measurement data, the extracted results are \(\mu_{1}=0.0019\), \(\mu_{2}=2.5470\), \(\sigma_{1}=5.9967\), and \(\sigma_{2}=35.7637\).  
Secondly, the Baum-Welch algorithm is used to get accurate values of the \(\lambda=(P,B,I)\). The accurate estimation results for elements of CHMM are:  
\[P=\left[\begin{array}{cc}0.9993&0.0007\\ 0.1761&0.8239\end{array}\right] \tag{12}\]  
\[I=\left(1,0\right)^{T} \tag{13}\]  
\[b_{j}(y)=\frac{1}{\sqrt{2\pi}\sigma_{j}}e^{-\frac{(y-\mu_{j})^{2}}{2\sigma_{j }^{2}}},\ j=1,2 \tag{14}\]  
where \(\mu_{1}=0\), \(\mu_{2}=0.8770\), \(\sigma_{1}=5.9696\) and \(\sigma_{2}=17.9763\).  
By using the previous parameterized CHMM model, we can simulate the noise, and the waveform in the time domain is shown in Figure 10a.  
As shown in Figure 10a, dotted lines represent simulated noise while the real lines represent measured noise. Figure 10b,c shows the APD and NAD values of the measured noise and the noise simulated by the CHMM model. It can be seen that the noise simulated by the CHMM model was very similar to the measured noise, whether APD or NAD values. Thus, the CHMM model can be used to characterize the time-varying characteristics of electromagnetic noise. In addition, it can be concluded from these discussions that the electromagnetic noise in industrial scenes is different from the additive white Gaussian noise. It will seriously affect the signal-to-noise ratio of wireless communication systems, thus affecting the quality of data transmission in the Industrial Internet of Things. Corresponding measures must be taken to reduce the interference of electromagnetic noise, usually using OFDM or interleaved coding technology.
# Malware API Calls Detection Using Hybrid Logistic Regression and RNN Model
###### Abstract  
Behavioral malware analysis is a powerful technique used against zero-day and obfuscated malware. Additionally referred to as dynamic malware analysis, this approach employs various methods to achieve enhanced detection. One such method involves using machine learning and deep learning algorithms to learn from the behavior of malware. However, the task of weight initialization in neural networks remains an active area of research. In this paper, we present a novel hybrid model that utilizes both machine learning and deep learning algorithms to detect malware across various categories. The proposed model achieves this by recognizing the malicious functions performed by the malware, which can be inferred from its API call sequences. Failure to detect these malware instances can result in severe cyberattacks, which pose a significant threat to the confidentiality, privacy, and availability of systems. We rely on a secondary dataset containing API call sequences, and we apply logistic regression to obtain the initial weight that serves as input to the neural network. By utilizing this hybrid approach, our research aims to address the challenges associated with traditional weight initialization techniques and to improve the accuracy and efficiency of malware detection based on API calls. The integration of both machine learning and deep learning algorithms allows the proposed model to capitalize on the strengths of each approach, potentially leading to a more robust and versatile solution to malware detection. Moreover, our research contributes to the ongoing efforts in the field of neural networks, by offering a novel perspective on weight initialization techniques and their impact on the performance of neural networks in the context of behavioral malware analysis. Experimental results using a balanced dataset showed 83% accuracy and a 0.44 loss, which outperformed the baseline model in terms of the minimum loss. The imbalanced dataset's accuracy was 98%, and the loss was 0.10, which exceeded the state-of-the-art model's accuracy. This demonstrates how well the suggested model can handle malware classification.  
malware; malware detection; API calls; logistic regression; neural network; weight initialization Article
## 1 Introduction
Our methodology tackles class imbalance by employing various data resampling techniques, such as adaptive synthetic (ADASYN) and synthetic minority oversampling technique (SMOTE), for binary and multi-class classification, along with edited nearest neighbors (ENN) and class weighting strategies to enhance model robustness. The findings reveal that our Transformer-CNN model significantly outperforms prior methods, achieving an impressive 99.71% accuracy in binary classification and 99.02% in multi-class classification on the NF-UNSW-NB15-v2 dataset [10; 11], as well as 99.93% accuracy in binary classification and 99.13% in multi-class classification on the CICIDS2017 dataset [12; 13], highlighting its efficacy in diverse operational contexts. Below, we outline the key contributions of our research:* We create a highly efficient intrusion detection system using an advanced hybrid Transformer-CNN model, integrated with techniques such as ADASYN, SMOTE, ENN, and class weights to effectively tackle class imbalance challenges. * An enhanced data preprocessing pipeline is applied, which first utilizes a combined outlier detection approach using Z-score and local outlier factor (LOF) to identify and handle outliers, followed by correlation-based feature selection. This structured approach refines model input, enhancing accuracy and reducing computational complexity. * Using the NF-UNSW-NB15-v2 and CICIDS2017 datasets, this study highlights the exceptional performance of the proposed model, demonstrating its superiority compared to current state-of-the-art models in the field. This paper is organized into several sections: Section 2 delivers an extensive overview of the relevant literature, offering insights into existing research in the field. Section 3 outlines the methodology utilized in this study, detailing the approaches and techniques employed. Section 4 showcases the results derived from the experimental procedures, providing an analysis of the data obtained. Following this, Section 5 engages in a thorough discussion of the findings, interpreting their significance and implications. Section 6 highlights the limitations encountered within the proposed methodology, providing a critical assessment of its scope. Section 7 concludes the study by summarizing the primary contributions and key insights gained. Lastly, Section 8 presents potential avenues for future research, suggesting directions for further exploration and investigation.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### MAC Layer--LoRaWAN
#### 2.4.9 GPS-Free Positioning  
LoRaWAN networks support geolocation without any hardware dependency using Received Signal Strength (RSSI) or Time Difference of Arrival (TDOA) methods from 200 m to 20 m resolution. It is not necessary to have any specific frame or protocol message, any data frame received by at least 3 GWs are enough to determine the location of the end-node.  
An overview of the architecture is shown in Figure 20. Since GPS coordinates of the GWs are known and fixed, by using TDOA node location can be extracted by GeoLocation solver.  
Figure 19: LinkADRReq.  
Since GPS modules are energy hungry and have additional hardware costs, LoRaWAN's builtin GeoLocation functionality becomes an efficient alternative. The resolution of the system is highly depended on the GW deployment density.
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 4 Evolution of Pavement Image Processing Techniques
### Deep Learning Approaches
#### 4.3.2 Object Detection
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Dataset** & **Target Classes** & **mAP** \\ \hline Faster RCNN [51] & Smartphone-captured images & \begin{tabular}{c} 6 classes: transverse cracks, longitudinal cracks, \\ potholes, alligator cracks, intact manholes, \\ damaged manhole surroundings \\ \end{tabular} & 0.963 \\ \hline Faster RCNN [52] & UAV-captured images & 3 classes: cracks, potholes, rutting & 0.926 \\ \hline Faster RCNN [53] & MIT-CHN-ORR dataset & 4 classes: linear cracks, nonlinear cracks, alligator cracks, general distress & 0.991 \\ \hline Faster RCNN [54] & \begin{tabular}{c} Custom HTD dataset \\ (Road Inspection Agency) \\ \end{tabular} & 1 class: cracks & 0.731 \\ \hline YOLOv3-Tiny (Lightweight) [55] & HD camera images & 1 class: cracks & 0.900 \\ \hline YOLOv5 [56] & RDD2020 & \begin{tabular}{c} 4 classes: transverse cracks, longitudinal cracks, \\ alligator cracks, potholes \\ \end{tabular} & 0.548 \\ \hline YOLO-LWNet [57] & RDD2020 & \begin{tabular}{c} 4 classes: transverse cracks, longitudinal cracks, \\ alligator cracks, potholes \\ \end{tabular} & 0.497 \\ \hline YOLOv5 [58] & RDD2022 & \begin{tabular}{c} 4 classes: transverse cracks, longitudinal cracks, \\ alligator cracks, potholes \\ \end{tabular} & 0.678 \\ \hline YOLOv5s [59] & RDD2022 (India subset) & \begin{tabular}{c} 4 classes: transverse cracks, longitudinal cracks, \\ alligator cracks, potholes \\ \end{tabular} & 0.380 \\ \hline YOLOv8n [60] & RDD2022 (China subset) & \begin{tabular}{c} 4 classes: transverse cracks, longitudinal cracks, \\ alligator cracks, potholes \\ \end{tabular} & 0.860 \\ \hline SSD [61] & RDD2018 & \
\begin{tabular}{c} 8 classes: wheel-marked longitudinal cracks, \\ construction joint cracks, evenly spaced transverse \\ cracks, etc. \\ \end{tabular} & 0.770 \\ \hline SSD [62] & RDD2020 &
\begin{tabular}{c} 4 classes: transverse cracks, longitudinal cracks, \\ alligator cracks, potholes \\ \end{tabular} & 0.730 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Application of object detection in pavement distress detection. Figure 7: YOLO network flowchart. However, challenges persist in model robustness, as evidenced by performance inconsistencies across datasets. Notably, the mAP scores on public benchmarks like the RDD remain significantly lower than the higher accuracy achieved on HD (high-definition) images from mobile devices and drones, highlighting ongoing limitations in generalization capability and real-world applicability. Additionally, [PERSON] et al. [63] conducted comparative evaluations of common object detection methods on the public RDD2022 dataset, where YOLOv3 and SSD achieved mAP scores of 0.625 and 0.557, respectively, while the improved Faster-RCNN reached 0.698. These results not only confirm the higher accuracy of two-stage detectors but also reveal that current advanced networks still exhibit limited overall recognition accuracy for distress detection.
## 6 Shear and Torsional Performance  
[PERSON] et al. [59; 60; 61] developed a modified calculation model based on ACI 440.1R-06 [54] to predict the ultimate shear capacity of SS-ECC beams reinforced with BFRP bars. The ratio of the predicted values to the experimental results was 0.97, indicating a good agreement between the model and the experimental findings. As illustrated in Figure 14, the experimental data revealed that SS-ECC beams with a low longitudinal reinforcement ratio confirmed greater shear capacity, which increased with a higher stirrup ratio or a lower shear span ratio.  
[PERSON] et al. [62] studied the torsional performance of BFRP-reinforced PE SS-ECC beams without stirrups through experiment and analysis. It is reported that the SS-ECC beams with a high fiber-volume fraction failed due to compression damage. It was found that rather than the longitudinal reinforcement ratio, the strength of SS-ECC and the height/width ratio of the beam are the critical factors to determine the ultimate torque. The effectiveness of developed FEM for prediction of torsional performance was verified and the torque-angle of twist relationship achieved a good agreement with the experimental results, as shown in Figure 15. The change in stress distribution of the cross-section was marked with three points in the typical curve. Point A represents the section subjected to pure torsional loading, where the longitudinal BFRP bars experience tensile stress. At Point B, with an increase in the angle of twist, the cross-section is subjected to shear stress. The shear stress within the SS-ECC is comparatively high, while the tensile stress in the longitudinal BFRP bars remains significantly lower than their tensile strength. At Point C, although the ultimate torque was attained, the tensile stress in the BFRP bars continued to be minimal. Nevertheless, the SS-ECC reached its maximum shear stress, leading to the failure of the SS-ECC.  
Figure 13: Influencing factors on ductility index: (**a**) effect of longitudinal reinforcement ratio; (**b**) effect of shear span ratio; (**c**) effect of stirrup ratio [59].  
[PERSON] et al. [63] proposed an empirical formula to estimate the ultimate torsional moment for FRC and ECC beams that are reinforced with GFRP bars. They analyzed the individual contributions of the concrete, PP fibers, and GFRP bars to torsional strength. The predicted outcomes showed good agreement with the experimental results.
# Investigation of the Fatigue Life of Bottom-Up Cracking in Asphalt Concrete Pavements
## 2 Experimental Study of the Dynamic Modulus and OT of Asphalt Pavements  
### Test Materials  
In order to obtain the fatigue life of bottom-up cracks in asphalt pavements, including fatigue cracking and crack propagation, the OTs at different temperatures need to be conducted to obtain the fracture parameters \(A\) and \(n\) of the asphalt mixture and the dynamic modulus of AC of different grades can be used to evaluate the tensile stress at the bottom of the AC layer. These parameters will be utilized to determine fatigue cracking and crack propagation. In this paper, an AH-70 common matrix asphalt was used in the laboratory mixture test; the coarse aggregate and fine aggregate are limestones and the gradation was composite grading. The technical specifications of AH-70 are shown in Table 1. Three common asphalt mixtures (AC-13, AC-20, and AC-25) were adopted in the following experiments. The selection of AC at all levels is shown in Table 2, and the asphalt/stone ratios were 4.7%, 4.1%, and 3.8%, respectively. Through testing, the basic performance indexes of asphalt and aggregate could meet the basic requirements of the Technical Specifications for Construction of Highway Asphalt Pavement JTG F40-2004 [21].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Test Items** & **Measured Data AH-70** & **Technical Requirements AH-70** & **Test Method** \\ \hline Penetration (25 \({}^{\circ}\)C, 100 g, 5 s, 0.1 mm) & 79.4 & 60–80 & T0604 \\ Softening point (\({}^{\circ}\)C) & 46.76 & \(\geq\)46 & T0606 \\ Ductility (10 \({}^{\circ}\)C) (cm) & 147.5 & \(\geq\)20 & T0605 \\ Ductility (15 \({}^{\circ}\)C) (cm) & \(>\)200 & \(\geq\)100 & T0605 \\
60 \({}^{\circ}\)C Dynamic viscosity (Pa.s) & 297 & \(\geq\)180 & T0620 \\ Asphalt flash point (\({}^{\circ}\)C) & 284 & \(\geq\)46 & T0611 \\ Residual needle penetration ratio (25 \({}^{\circ}\)C) & 82.1 & \(\geq\)61 & T0604 \\ Residual ductility (10 \({}^{\circ}\)C) (cm) & 8.5 & \(\geq\)6 & T0605 \\ Residual ductility (15 \({}^{\circ}\)C) (cm) & 33 & \(\geq\)15 & T0605 \\ \hline \end{tabular}
\end{table}
Table 1: Test results of asphalt raw materials.