esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Dynamics of Jack-Up Platform and Seabed Foundation
### Liquefaction in Seabed Foundation  
There are two mechanisms for the wave-induced seabed liquefaction that have been widely verified in previous experiments and numerical simulations. The key point to distinguish the two liquefaction mechanisms is the different ways of generating pore water pressure. The first mechanism is the residual liquefaction, which is related to the accumulation of pore pressure in the seabed under cyclic loading. Residual liquefaction can occur under the attack of ocean waves and earthquakes. The second mechanism is the momentary liquefaction, which can only occur in a very dense seabed under wave troughs. Seabed liquefaction is an important factor affecting the stability of marine structures built on a seabed foundation. Here, the wave-induced momentary liquefaction in the seabed foundation of jack-up platforms will be analyzed.  
The judgment criterion for the momentary liquefaction adopted in this study is based on the following three-dimensional stress-based criterion:  
\[\frac{1}{3}\left(\sigma_{x}^{\prime}+\sigma_{y}^{\prime}+\sigma_{z}^{\prime} \right)\geq 0 \tag{6}\]  
where \(\sigma_{x}^{\prime}\), \(\sigma_{y}^{\prime}\), and \(\sigma_{z}^{\prime}\) are the current effective stresses at a point in the seabed foundation. The physical meaning of Equation (6) is that seabed soil is in a tensile state when the current mean effective stress at a point is greater than or equal to zero, and it can be considered that the seabed soil at this point has become liquefied. The distribution of the liquefaction zone in the seabed foundation in Case 2 is shown in Figure 16.  
In Figure 16, it is observed that liquefaction only occurs in the superficial layer of the seabed foundation, and the maximum liquefaction depth is about 1 m. It is well-known that a typical feature of momentary liquefaction is the periodic appearance and disappearance in the process of wave motion. Specifically, momentary liquefaction occurs in the superficial layer of the seabed foundation surrounding the pile legs of the jack-up platform when each wave trough reaches the platform. When the momentary liquefaction occurs, the soil particles in the superficial layer of the seabed foundation are in a suspended state, and easily transported by scouring. As a result, some large-scale scouring pits around the pile legs would more easily appear, which has an adverse effect on the long-term stability of the offshore platform [25; 26; 27].  
Figure 16: Predicted liquefaction zone in the seabed foundation when a wave trough reaches the jack-up offshore platform at \(t=54.9\) s in Case 2 (\(H=5\) m, \(d=50\) m, \(T=15\) s) (note: the red zone is the liquefaction zone).  
It can also be seen in Figure 16 that no momentary liquefaction occurs in the seabed directly below the four spudcans. It is indicated that the spudcans installed on the tip of the pile legs can not only reduce the base pressure of the foundation, but also inhibit the occurrence of momentary liquefaction in the surrounding seabed soil, which could make the offshore platform structure more stable in service.
# Acceptance Model for Mobile Building Information Modeling (BIM)
## 1 Introduction
### Research Methodology  
As part of the efforts to improve the use of mobile BIM, this work analyzes the relationships among factors affecting acceptance of mobile BIM by construction practitioners and presents a mobile BIM TAM. The research processes followed are classified thusly.  
1. Define the mobile BIM characteristics based on a literature review: The characteristics of mobile applications and BIM were defined through a literature review to suggest TAMs. The analysis results were assessed for external factors that affected mobile BIM acceptance.
2. Collect data to propose a mobile BIM TAM: The survey responses from construction practitioners (i.e., designers, contractors, Construction Managers (CMs), and BIM contractors) were collected. An overview of the data collection process is as follows.  
The survey data were obtained from a sample of experienced BIM users or those experiencing BIM at work. The survey was conducted between 22 March and 29 April, and a total of 111 responseswere received. Each question was measured on a 7-point Likert scale ranging from \"strongly disagree\" to \"strongly agree.\"  
Among the 111 respondents, 31 were designers, 37 were from construction management organizations, 11 were from construction organizations, 18 were engineers, and 14 were researchers. The respondents' average experience in construction was about 11.6 years and the average experience with BIM was about 3.5 years. Their average preference for using BIM tools was 5.84 points on the 7-point Likert scale. The respondents' characteristics are detailed in Table 1.  
1. Explore key factors for mobile BIM acceptance through factor analysis: The survey results were analyzed as follows. A principal component analysis (PCA) of the factors was conducted using the software package IBM Statistics SPSS 21.0. The results are shown using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test. Through this analysis, variables that did not have a significant impact on mobile BIM acceptance were removed, and the assessment items were classified as significant factors.
2. Validate the proposed model using a structural equation model (SEM): A hypothesis was established for the relationships between the factors from the factor analysis results. An analysis of the SEM was conducted using the software IBM Statistics AMOS 21.0 to validate the hypothesis. The result shows the ratio of \(\chi^{2}\) to the degrees of freedom (df), root-mean-square residual (RMR), parsimonious goodness of fit index (PGFI), Tucker-Lewis index (TLI), comparative fit index (CFI), and root-mean-square error of approximation (RMSEA). We eliminated the insignificant hypothesis of the set assumptions based on the path analysis results. Further, we checked the convergent validity and discriminant validity of the proposed model on the confirmatory factor analysis (CFA).
# ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
## 4 Deep Learning Classification for Human Pose Estimation  
_ViT Transformer-Based Classification for the Estimation of SMPL-X Pose from Low-Resolution SPI_  
The proposed framework utilizes Vision Transformer (ViT) [23] to classify infrared (IR) images from the LWIRPOSE dataset [60], which includes pose information for 12 activities: Direction, Discussion, Eating, greeting, Phone conversation, Posing, Purchases, Sitting, Smoking, Taking Photos, Waiting and Walking. To define the classification model, we evaluated the performance of various architectures, including CNN [61], VGG10 [62], ResNet [63], and ViT. As shown in Tables 1 and 2, ViT demonstrates superior accuracy, making it the preferred choice for this task. This classification constitutes a critical preprocessing stage for pose estimation within the SMPL-X model, thereby improving the accuracy of 3D human pose reconstruction from low-resolution SPI infrared images. By integrating deep learning-based feature extraction and classification, the proposed approach effectively addresses challenges in thermal imaging, such as low-resolution and noisy data, thereby improving human pose estimation in complex environments (see Figure 3).  
Image tokenization & feature embedding
* The input image is divided into smaller non-overlapping patches, following the standard ViT patch embedding mechanism.
* Each patch is flattened and linearly projected into a high-dimensional feature space, ensuring that local spatial features are retained.
* These projected embeddings serve as input tokens to the ViT Transformer encoder.
* **Transformer-based feature extraction*
* The Transformer Encoder processes the embedded patches using multihead self-attention (MHSA) and feedforward layers [65].
* This enables the model to capture both local and global dependencies in the IR image, preserving structural details essential for pose recognition.
* The Transformer aggregates spatial relationships between different body regions, enhancing the understanding of human posture even in low-resolution IR data.
* **Classification via Multi-Layer Perceptron (MLP)*
* The extracted feature representation from the Transformer Encoder is fed into a Multi-Layer Perceptron (MLP) [23] for human activity classification.
* The confusion matrix in the figure demonstrates the system's ability to classify different activities, such as walking, sitting, taking photos, eating, smoking, and talking on the phone, achieving high precision in most categories.
* Accurate classification is crucial for defining a pre-pose configuration that aligns with real-world human movements.
* **Pre-pose estimation with SMPL-X model*
* The classified activity is mapped onto the SMPL-X human pose model, a 3D parametric representation of the human body that captures detailed articulations and anatomical structure.
* The propose definition based on classification output serves as an initial pose estimation, allowing for more refined 3D reconstructions of human movement.
# AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
## 4 Experimental Results
### Performance Results
#### 4.3.1 Network Lifetime  
The network lifetime performance of AEF is shown in Figure 6. The \(x\)-axis represents the network size in terms of the number of sensor nodes and the \(y\)-axis illustrates the number of rounds. The summary of network lifetime improvement over CCEF and DEF is illustrated in Table 2. It can be observed that relative improvements decreases as the number of rounds or attacks increases.  
* **FCO:** AEF outperforms CCEF by 2.92 times or 292% for the FCO performance parameter at 50% FTR as shown in Figure 6a. For 70% and 90%, FTR network lifetime also have significant improvements of 2.81 folds and 2.71 times, respectively, as illustrated in Figure 6b,c. In comparison with DEF, we observe relatively lower improvements of 1.25, 1.20, and 1.15 times with respect to 50%, 70%, and 90% FTR.
* **10 PCO:** AEF shows a gain of 2.64, 2.73, and 2.74 times against CCEF at 50%, 70%, and 90% FTR, respectively. In case of DEF, there is also relativity small marginal improvements of 1.11 and 1.05 times at 50% and 70% FTR, respectively. However, AEF shows little decrease in network lifetime at 90% FTR.
* **LCO:** Performance improvement over the number of rounds required for the last node to be depleted for 50% FTR is 2.36, for 70% FTR is 2.37, and for 90 % is 2.41 folds in comparison with CCEF. In the case of DEF, marginally less improvements of 1.05 (50% FTR), 1.06 (70% FTR), and 1.06 (90% FTR) times are observed.  
It can be observed that, with an increase in the number of the FTRs or the rounds, the relative improvement in network lifetime decreases. However, there is no set trend; however, the larger the number of rounds for a small network size, the more the margin of improvement decreases. This trend by part is because of random deployment, different network sizes, and dynamic path selection. It can be observed from Table 2 that there is significant improvement against CCEF. There is also network lifetime gain against DEF; however, the margin of improvement is less. The reason is because DEF considers energy-aware routing.  
Figure 6: Network lifetime: six simulation experimental setups using FCO, 10 PCO, and LCO for 50% and 70% false traffic ratios (FTRs) for network sizes of 400, 700, and 1000 nodes.
# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
## 3 Experimental Setting
### Acceleration Decomposition Based on EMD  
EMD algorithm is used to decompose the raw acceleration data into several IMF components and one residual res where different components have different trends and characteristics, representing different features of the fluctuations of the raw acceleration data. The decomposition results for the four data sets are shown in Figures 9-12. For every picture, the horizontal axis is the time point, and the vertical axis is the decomposition value. All components are listed in order of extraction from highest to lowest frequency.  
As can be seen from Figures 9-12, the first few IMF components are random components with an insignificant pattern of variation. The middle components have the fluctuating feature to reflect more features of the original data. The last few IMF components with low-frequency reflect the slow change process of acceleration. The last component is the residual, which reflects the overall trend of the acceleration data.
# Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits
## 5 Results  
This section presents the performance of the SL model architecture presented in Section 4. First, the model's performance is assessed by quantifying the error between the ground truth and the model prediction. Then, the ground truth is post-processed with different distance thresholds to obtain different true encounters. Each true encounter is compared with the model prediction, and the effect of the decision threshold is analyzed. Subsequently, a decision threshold is fixed, and the model's performance is computed for each sample in both synthetic and realistic test sets. Finally, the computational cost of the SL model inference is evaluated and contrasted with the time required for the SGP4 model to compute trajectories and assess encounters.
# Cross-Pol Transponder with Frequency Shifter for
###### Abstract  
Ground-based synthetic aperture radar (GBSAR) systems are popular remote sensing instruments for detecting the ground changes of landslides, glaciers, and open pits as well as for detecting small displacements of large structures, such as bridges and dams. Recently (2017), a novel mono/bistatic GBSAR configuration was proposed to acquire two different components of displacement of the targets in the field of view. This bistatic configuration relies on a transponder that consists--in its basic implementation--of just two antennas and an amplifier. The aim of this article was to design and experimentally test an improved transponder with cross-polarized antennas and frequency shifter that is able to prevent possible oscillations even at very high gain, as required in long-range applications. The transponder was successfully field-tested, and its measured gain was 91 dB gain.  
b 2018; 10, 1364; doi:10.3390/rs10091364
## 6 Simulation and Results
### Filtering Efficiency  
Filtering efficiency evaluates the ratio of the number of detected malicious packets to the total number of malicious packets. In Figure 6, we have compared the filtering efficiency between the new proposed scheme (RWFS) and the Cui et al. scheme in [22].  
We have assumed that the attacker knows the MAC method that was used to generate the watermark in RWFS and the MAC method that was used to generate the MAC in the Cui et al. scheme. Therefore, the attacker can only randomize values that are secret parameters of each scheme.  
For this factor, we have injected the network with different numbers of malicious packets and wanted to know how many packets were detected and rejected by the scheme. From the results in Figure 6, we can see that our scheme is better, with an accuracy of 99.95% rejection in the case of injecting 5000 malicious packets against 99.77% for the Cui et al. scheme. This percentage of filtering efficiency proves that the scheme can perform well against any number of injected packets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Topographical Area & 100 m \(\times\) 100 m \\ Access point location & (50, 50) \\ Number of nodes & 100 \\ Fraction of advanced nodes (\(m\)) & \(m=0.1\) \\ Initial energy of normal node (\(E_{0}\)) & \(E_{0}\)= 0.5 J \\ \(E_{elec}\) & 50 nJ/bit \\ \(E_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ \(E_{mp}\) & 0.0013 pJ/bit/m\({}^{2}\) \\ \(E_{DA}\) & 5 nJ/bit \\ Packet header size & 150 bits \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
Figure 6: The filtering efficiency analysis between RWFS (the proposed scheme) and the Cui et al. scheme. The bar graphs show the percentage of filtered malicious packets for a different scenario where a different number of false packets was injected.
## 3 Results  
### Dilatometry and CCT Diagram  
The continuous cooling transformation (CCT) diagram determined from the Gleeble dilatometer traces is presented in Figure 1. The hardness was nearly 650 HV10 with the highest cooling rate (96 \({}^{\circ}\)C/s), but 600 HV10 was still achieved with 24 \({}^{\circ}\)C/s. Since the hardness level of 600 HV was still obtained with lower cooling rates, more time for auto-tempering could be provided with the use of a slower cooling media, such as oil or a polymer suspension. The slower cooling rates will not result in a fully martensitic microstructure, as shown in the CCT diagram of Figure 1 and the micrographs in Figure 2. The martensite start temperature was estimated as approximately 350-380 \({}^{\circ}\)C based on the CCT diagram.  
Figure 1: Continuous cooling transformation (CCT) diagram for the steel. Abbreviations: P + F is pearlite and ferrite, B is bainite and M is martensite.  
The optical microscope images show that low cooling rates produced multiphase microstructures. The hot-rolled (HR) sample represents the microstructure prior to Gleeble testing or reheating and quenching. The lowest cooling rate (3 \({}^{\circ}\)C/s) tested with the Gleeble had a highly mixed microstructure consisting of bainite (approximately 50-55%), pearlite (30-40%), and ferrite (5-10%). There was very little pearlite and ferrite with a cooling rate of 12 \({}^{\circ}\)C/s and the phase fractions were estimated at 50%-60% for martensite and 40-50% for bainite. Bainite was still present at 48 \({}^{\circ}\)C/s but at less than 10%. The highest cooling rate (96 \({}^{\circ}\)C/s) showed close to 100% martensite with only a minor fraction of bainite. The phase fractions were highly localized within the Gleeble samples with the slow and medium cooling rates (<48 \({}^{\circ}\)C/s). Based on the dilatometry, high quenching rates can be used to obtain hard, fully martensitic microstructures, whereas slower cooling rates with possible isothermal holding provide possibilities for obtaining different bainitic or multiphase structures.
# Single Trace Analysis against HyMES by Exploitation of Joint Distributions of Leakages
## 1 Introduction
However, due to the structure of the decryption algorithm, the SPA proposed in Reference [9] cannot be applied to HyMES. On the other hand, [PERSON] proposed a side-channel analysis technique with which to identify a secret key by using the probability distribution of the input and the output of a nonlinear function varying with the secret key value [13]. The joint distribution is a probability distribution considering two or more random variables. The side-channel analysis using the joint distribution has only been investigated in the symmetric key cryptography, and several traces have been used. In this paper, we present our proposed side-channel analysis on HyMES using a single power consumption trace. The main contributions of this paper are as follows. * We propose the first side-channel analysis on HyMES using the joint distributions of leakages. We target the non-linearity of the pre-computation table in HyMES implementation [6]. By analyzing the leakage that occurs while calculating the parity-check matrix, we are able to recover the Goppa polynomial \(g(z)\) and the support \(L_{sec}\), which are the secret keys of HyMES. The proposed method only uses one power consumption trace, and it is the first joint distribution based analysis for public-key cryptography. Moreover, we demonstrate that any other public-key cryptosystem using nonlinear operation can be vulnerable to our attack. The details of our attack are presented in Section 3. * We present an experimental result validating the efficiency of the proposed attack. We confirm that the proposed method works as expected by using simulated power traces. These simulated traces are collected by adding noise to the Hamming weight model. The attack success rate is 100% up to the noise standard deviation of 1.3. Further, we show that the performance of the proposed attack increase when several traces are used. When 10 traces are used, the attack success rate is 100% up to the noise standard deviation of 2.1. The remainder of this paper is organized as follows. Section 2 provides background on code-based cryptography and discusses the side-channel attack used for the analysis. Section 3 provides a high-level description of the proposed attack. Section 4 shows the experimental results using the simulation traces. Finally, the conclusion is drawn in Section 5.
## 3 Experiments and Results
### Ablation Experiments  
To investigate the effect of each module on the experimental results, this article began with the Baseline network, which includes the encoder, teacher generator, student generator, enhanced anomaly discriminator, and in-painting block. The MSAC module and the CASE are added to the teacher network, and the E-JPU module is introduced to the student network's discriminator. Ablation experiments were then conducted, and the corresponding visualization results are compared in Figure 12.  
As shown in Figure 12b, the heatmap generated by the Baseline network exhibits localization deviations in anomaly detection, primarily manifested by the lack of key feature responses, resulting in limited detection accuracy. Figure 12c demonstrates that, after integrating the MSAC module, the model effectively suppresses background interference and significantly improves anomaly localization precision through a collaborative mechanism of multi-scale feature fusion and spatial-channel attention. Figure 12d further reveals that the E-JPU module enhances sensitivity to small-sized anomalies by constructing pyramid feature maps to refine detail extraction. However, its standalone application remains insufficient for characterizing complex anomalies. Ultimately, as illustrated in Figure 12e, the proposed method achieves a breakthrough in detection accuracy under challenging operational conditions through the synergistic optimization of multi-scale feature coupling, attention-guided channel enhancement, and pyramid-based detail refinement.  
According to the ablation study results in Table 7, the inclusion of the MSAC module significantly improved the baseline model's performance, with the AUC increasing from 87.2% to 92.1% and ACC from 83.8% to 89.2%, demonstrating the effectiveness of multi-scale feature fusion and spatial-channel attention mechanisms in suppressing complex background interference. The Baseline + E-JPU configuration improved the recall rate  
Figure 12: Visualization results on the rail fastener dataset: (**a**) original image; (**b**) baseline; (**c**) Baseline + MSAC; (**d**) Baseline + E-JPU; (**e**) proposed method.  
to 89.3% through pyramid feature refinement; however, due to the localized optimization characteristics of the single module, the F1 score increased by only 2.0%. The proposed method (Baseline + MSAC + E-JPU) achieved comprehensive improvements across all metrics, with an AUC of 94.7%, ACC of 90.1%, and F1 of 87.8%. The complementary strengths of MSAC in anomaly localization and E-JPU in detail enhancement facilitated joint optimization, leading to simultaneous improvements in small-object recall of 93.5% and detection accuracy under complex scenarios, thereby confirming the necessity of multi-module collaborative design in industrial anomaly detection.