esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Simulations
### Results and Discussions
#### 6.3.3 Discussions  
To analyze the performance differences between random amplitude scaling and random temporal shifting, the amplitude scaling technique performs better than temporal shifting. In this paper, we consider several possible reasons. First, time-based ranging typically uses an external signal source and heavily relies on complex signal processing methods, which means it generally relies on super-resolution methods like the MUSIC algorithm to obtain the first arrival peak as an arrival time. However, in our CIR training sets, CIR information is not continuous; it is selected as time intervals, which makes the time sequence discrete. This decreases the resolution of the time sequence in CIR and possibly leads to an incorrect recognition for the model. The power-based features vary over time, but the shape of the peak does not change rapidly between a few sampling points. Moreover, the entire CIRs' peaks can be utilized to estimate the location, rather than solely relying on the arrival time of the first peak. This may be a reason why the power-based feature is more effective than the time-based feature, and power scaling exhibits a better performance in augmentation. Meanwhile, the time-based ranging relies highly on the LOS condition, a positive bias could be induced in NLOS propagation and a larger variance tends to exist in the NLOS condition [63].  
To analyze the performance of methods in Table 4, we re-examine the fundamental principles of these four methods. The \(\Pi\) Model and Temporal Ensembling only use a consistency loss for learning while the AMT uses both the weights of the student model and consistency loss for learning. Interactions in weights can help learn the performance of supervised training rapidly from the beginning instead of receiving feedback at the end of each epoch to punish inconsistent items, which greatly reduces time costs. Similarly, like AMT, the pseudo label also uses the training weights of supervised training, manifesting as no significant positioning error at the beginning of training. However, due to the small amount and sparse distribution of labeled data, the pseudo-label method cannot learn information beyond this sparse distribution, which leads to a limited performance during training with dense but unlabeled data, and results in a relatively low positioning accuracy.
## 3 A Demonstration of the Proposed Framework: A Case Study  
### Model for PCaPC Frame with MT Connection  
The proposed PCaPC frame structure incorporates an MT connection, aiming to enhance seismic resilience through self-centering behavior and minimized post-earthquake residual deformation [44; 45; 46]. Under seismic excitation, relative rotations between precast beam and column elements are permitted via MT joints, effectively mimicking hinge behaviors without serious plastic deformation. As the seismic demand diminishes, the combination of unbonded prestressed tendon tension and vertical gravity load facilitates the recentering of the joint, restoring structural alignment and reducing residual drift. This mechanism ensures that the structural components remain largely elastic during seismic events, thereby shortening functional downtime and reducing repair needs. The connection layout and tendon configuration are detailed in Figure 8.  
Compared to traditional PCaPC connections, this system introduces the following key advantages:  
* Unbonded prestressed tendons at the beam permit self-centering rotations, while bonded anchorage zones at the beam ends ensure effective force transfer and enhance the overall stability of the connection.
* The MT connections are positioned at low-moment zones, enabling early stiffness reduction and hinge relocation under seismic loading.
* No cast-in situ concrete is required at joints beyond grouting sleeves.
## 2 Literature Review
### The Development of GAI in Conjunction with PBL  
In recent years, the rapid advancement of GAI technology has significantly transformed traditional teaching paradigms, with its application in education becoming increasingly widespread. Since OpenAI introduced ChatGPT-3.5, a leading example of a large language model, the use of GAI in education has expanded to encompass various dimensions. These include fostering GAI literacy and enhancing digital competence among learners and educators [19], exploring effective methods to leverage GAI in teaching and learning [20], analyzing its impact on students and teachers [21], and addressing ethical and moral implications [22].  
When integrating GAI with PBL, existing research has predominantly focused on two main areas. The first involves utilizing PBL as a framework for advancing AI education. For example, project-based curricula have been designed to cover diverse GAI disciplines and applications, emphasizing hands-on projects to enhance students' understanding of AI theories and practical applications. Gamification strategies have also been employed within PBL to improve learning outcomes in AI courses [23]. The second area explores the role of GAI as a pedagogical aid to optimize PBL instruction. Studies in this domain examine how GAI can improve classroom efficiency, provide personalized support, and enhance learning outcomes by scalably assessing student engagement and performance through machine learning models analyzing dialogue data.  
While these studies underscore the value of integrating GAI with PBL, the research focus has been limited to these two mainstream trends. There remains a noticeable gap in exploring how GAI can be integrated more comprehensively into PBL, not merely as a tool for instruction or discipline-specific development but as an integral component of the PBL process itself. Specifically, the potential of GAI to function as a resource that supports deep learning and facilitates student-driven exploration within PBL environments remains underexplored.  
Furthermore, there is a scarcity of research on leveraging GAI as a comprehensive learning platform that seamlessly integrates with PBL to create a student-centered, interactive, and highly personalized learning experience. Such an approach could revolutionize PBL by combining the strengths of GAI and project-based frameworks to foster more profound engagement, adaptability, and individualization in learning.
## 3 Damage Location Prediction Model Using AE Signal Data  
### Experimental Setup of Pile Hit Test  
A pile hit test was conducted to collect the experimental data for the ANN prediction model. The experimental setup of the pile hit test is illustrated in Figure 2. A circular section concrete column of a building was determined as the test specimen. This concrete column represented a deep pile, since the concrete column and pile shared the same structure and components. The height of the test pile was 11 m, and the diameter of the pile was 1 m. A platform was connected to the pile at the height of 10 m from ground level.  
It was more difficult to detect the damage of deep piles compared to upper structures, as the piles were always hidden in soil. When detecting the damage of deep piles, how to install the sensors for receiving signal was always a confusing problem for engineers. There were three main methods for installing the sensors: installing the sensors on pile body,installing the sensors on platform, and installing sensors on both pile body and platform (mix-installation). However, the efficiency of the three installation methods for detecting the damage in a deep pile were need to be studied.  
In this experiment, six AE sensors were installed on the pile body and platform. In which, three AE sensors were installed on the pile body at the height of 11 m from the ground level on three perpendicular sides. The three AE sensors were marked as S1, S2 and S3, and they were classified in group 1. The group 1 was named as pile-installed group. Another three AE sensors were installed on the platform on three perpendicular sides corresponding to the pile-installed sensors. The three AE sensors were marked as S4, S5 and S6, and they were classified in group 2. The group 2 was named as plate-installed group. For the group 3, the AE sensors of the group 1 and group 2 were combined together as a mix-installed group. Thus, there were six AE sensors in the group 3, they were S1, S2, S3, S4, S5, and S6 (Figure 3a).  
The experimental data were collected by a Micro-II Digital AE system installed on the platform (Figure 3g). The threshold (trigger level for collecting AE signals) was set as 40 dB after pretesting, which can effectively prevent surrounding noise interference. The AE sensor type was R.45 IC (Figure 3h), which was commonly selected for structural health monitoring of concrete structures. The operating specifications of R.45 IC type AE sensor are shown in Table 1. AE sensors were installed on the pile and platform using high vacuum grease, which can help AE sensors to gather signals. The six AE sensors were connected to the AE system with cables and the AE system can output the AE signal data for users.  
Figure 3: Experimental setup of the pile hit test. (**a**) Two-dimensional sketch of experimental setup. (**b**) Three-dimensional sketch of experimental setup. (**c**) Cross-section of the pile. (**d**) Hit process. (**e**) Hammer. (**f**) Input signal. (**g**) AE data acquisition system (Micro-II Digital AE system). (**h**) R.45 IC-type AE sensor.
# Asynchronous Quantum-Resistant Blockchain for Secure Intelligence Sharing
## 3 Preliminary
### Crystals-Kyber  
CRYSTALS-KYBER, designed by [PERSON] et al. [20]. and adopted as the sole post-quantum public key encryption and keyestablishment algorithm by NIST, relies fundamentally on the computational difficulty associated with the Learning with Error (LWE) problem. Its efficiency stems significantly from employing the Number Theoretic Transform (NTT) for polynomial multiplications, effectively reducing computational complexity from O(n\({}^{2}\)) to O(nlogn). In the key generation phase, a random matrix \(A\), a secret vector \(s\), and an error vector \(e\) are generated, enabling computation of the public key through the equation \(b=As+e\). Encryption processes involve the sender computing polynomial values based on the recipient's public key and additional randomly selected vectors and error terms, embedding the message within the ciphertext. Subsequently, the recipient employs the corresponding private key to decrypt the ciphertext and successfully recover the original message. The rigorous mathematical underpinning, computational efficiency, and robust protocol design collectively underscore CRYSTALS-KYBER's significance as a standard for secure cryptographic communications in the emerging quantum computing context.
# Fuzzy Logic-Based Controller for Bipedal Robot
## 2 Kinematics of Robot and Motion Trajectory Planning
### Kinematic Equations of Robot
The angle \(\theta_{1}\) is a parameter related to the stable equilibrium of the robot. The guarantee of \(\theta_{1}\) angle value for the purpose of stable balance of the robot is not performed directly by the driving motor but depends on the dynamic state of the robot as well as the control algorithm applied on the joints driven by the motor. Therefore, \(\theta_{1}\) is both a parameter related to the design problem of the motion trajectory and one of the independent coordinates related to the problem of controlling the robot's motion. The following sections will continue to present issues related to this problem. The angles \(\theta_{6}\), \(\theta_{7}\) represent the posture of the robot body and swinging feet, respectively, in stationary as well as in moving states. Using the homogeneous coordinate transformation transfer matrix method, the robot kinematic equations are set up as follows:  
\[\left\{ \begin{array}{l}a_{t}cos\theta_{13}+a_{t}cos\theta_{12}+a_{f}cos \theta_{1}+d_{f}-x_{b}=0;cos\theta_{13}=cos(\theta_{1}+\theta_{2}+\theta_{3}) ;\;cos\theta_{12}=cos(\theta_{1}+\theta_{2});\\ a_{t}sin\theta_{13}+a_{t}sin\theta_{12}+a_{f}sin\theta_{1}-y_{b}=0;\qquad sin \theta_{13}=sin(\theta_{1}+\theta_{2}+\theta_{3});\;sin\theta_{12}=sin(\theta _{1}+\theta_{2});\end{array} \right. \tag{3}\]  
\[\left\{ \begin{array}{l}a_{t}cos\theta_{15}+a_{t}cos\theta_{14}+a_{t}cos \theta_{13}+a_{t}cos\theta_{12}+a_{f}cos\theta_{1}+d_{f}-x_{b}=0;cos\theta_{15 }=cos(\theta_{1}+\cdots+\theta_{5});\;cos\theta_{14}=cos(\theta_{1}+\cdots+ \theta_{4});\\ a_{t}sin\theta_{15}+a_{t}sin\theta_{14}+a_{t}sin\theta_{13}+a_{t}sin\theta_{12 }+a_{f}sin\theta_{1}-y_{k}=0;\qquad sin\theta_{15}=sin(\theta_{1}+\cdots+ \theta_{5});\;sin\theta_{14}=sin(\theta_{1}+\cdots+\theta_{4});\end{array} \right. \tag{4}\]  
From the kinematic structure diagram, it is possible to derive  
\[\theta_{6}=\theta_{k}-(\theta_{1}+\theta_{2}+\theta_{3}+\theta_{4}+\theta_{5 })-\frac{\pi}{2} \tag{5}\]  
\[\theta_{7}=\theta_{b}-(\theta_{1}+\theta_{2}+\theta_{3})+\frac{\pi}{2} \tag{6}\]  
With the position and state of the robot at each step being determined by the operational coordinates (1), Equations (3)-(6) allow solving the generalized coordinates (2), in which the joint coordinates \(\theta_{2}\), \(\ldots\), \(\theta_{7}\) are driven, thus creating the basis for controlling robot movements. In contrast, the generalized coordinates (2) are identified by sensors allowing the solution of Equations (3)-(6) to find the robot's posture at each step taken. Therefore, the systems of kinematic Equations (3)-(6) are used when designing motion trajectories for the robot.
# Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter
## 3 Result  
### Ocean Wave Inversion Results  
According to Figure 11, the 2D OWS were calculated using the _FFT_2 based on the relative SSH inversion results in the buoy area (Figure 10a,c). The high precision 2D OWS (4000 \(\times\) 4000 matrix size) were obtained further by four times upsampling, after that, a 5 \(\times\) 5 sliding window was used to calculate the average value of the window corresponding to each matrix element, and \(k_{p}\) was estimated based on the coordinates \((k_{x},k_{y})\) corresponding to the maximum value of the average matrix. The \(k_{x}\) and \(k_{y}\) denote the wavenumber vectors in the ground range (viz. the cross-track) and azimuth direction, respectively, and  
Figure 10: The relative SSH of the GNSS buoy area by the DInIRA. The band pass filtering results of the Ku band: (**a**) after processing (**b**) before processing. The band pass filtering results of the Ka band: (**c**) after processing; (**d**) before processing.  
Figure 9: Inversion result of SSH in: (**a**) The Ku band; (**b**) the Ka band.  
the angle in polar coordinates is in degrees clockwise to the true north direction (N). In addition, the black circles play an auxiliary role to indicate different wavelengths (i.e., 40, 20, and 10 m), and the blue circles indicate the corresponding MWW of the wind waves and swells. It can be inferred that the Ku and Ka bands had the same ability to invert the WPD in Figure 11. However, the MWW inversion results in the Ku and Ka bands showed a noticeable difference, and the MWW of the swells in the Ku band was \(\sim\)13.08 m longer than the Ka band.
## 3 Data and Methods  
### Bathymetry  
A bathymetric map of the SE Tyrrhenian Sea was created by joining high-resolution multibeam data with the lower resolution data provided by the European Marine Observation and Data Network (EMODnet) [52]. High-resolution multibeam data (Palinuro and Marsili volcanic chains) were collected by using a Reson Seabat 8160 multibeam sonar system, which is effective at depths ranging from 50 to 3500 m. This system is integrated with differential global positioning and characterized by a 50 kHz ping source, 150\({}^{\circ}\) beamwidth for the transmitted pulse, and a 126-beam receiver. The sound velocity in seawater was measured using a sound velocimeter profile, with real-time recordings and updates made every 8 h during data acquisition. Data were processed with partial, real-time beam filtering, swath and spatial mode editing, despiking, and digital terrain model (DTM) rendering. The final DTM is reported with a 20 m grid cell size (Figure 3).
# Guided Spatial Transformers for Facial Expression Recognition
## 3 Methodology
### Masks Generator
#### 3.1.2 Landmarks-Based Soft Masks  
This strategy is a modified version of the binary masks with a soft background.  
To soften the background of the landmark-based binary mask, we fix a threshold of 0.15. All the pixels that have intensities below 0.15, now get the value of 0.5, whereas the rest change their value to 1. As a consequence, we obtain images as shown in the third column of Figure 1.  
With this transformation, we pursue to improve the training of the network, as introducing many zeros may deteriorate the learning of those areas in which there is usually a zero due to the functioning of the backpropagation algorithm. When the backpropagation algorithm propagates the error, those inputs with zero will not modify their associated weights in the network, as they did not contribute to the final prediction. However, these areas could still carry important information about the context and could be relevant for some images. For this reason, we considered that introducing non-zero values may benefit the network's learning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Strategy**} & \multicolumn{2}{c}{**Accuracy \(\pm\)**} & \multirow{2}{*}{**Average Epochs**} \\  & **95\% Confidence Interval** & & **to Converge** \\ \hline ZeroR & 49.26 \(\pm\) 0.17 & - \\ Simple-CNN & 69.78 \(\pm\) 0.16 & 68 \\ \hline Baseline STN & 70.37 \(\pm\) 0.16 & 89 \\ STN with landmarks—binary masks v1 & 70.57 \(\pm\) 0.16 & 70 \\ STN with landmarks—binary masks v2 & 70.54 \(\pm\) 0.16 & 58 \\ STN with landmarks—binary masks v3 & 70.59 \(\pm\) 0.16 & - \\ STN with landmarks—soft masks & 70.72 \(\pm\) 0.16 & 71 \\ STN with dilated masks & 70.53 \(\pm\) 0.16 & 58 \\ STN with saliency masks & 70.60 \(\pm\) 0.16 & 108 \\ \hline STN with patches masks & 66.47 \(\pm\) 0.16 & 67 \\ STN with weighted masks & 68.60 \(\pm\) 0.16 & 51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation of the different strategies on AffectNet.  
Furthermore, we believe that this way of expressing the data maintains the possibility of determining the transformation from the whole image and not only from the regions delimited by the landmarks, using more context but still retaining relevant regions.
## 3 Results
### Proteome Analysis after Growth on IMOS
In the presence of IMOSs, the overexpression of SdhA allows the transfer of electrons from succinate to the respiratory chain, thereby increasing energy production in the form of ATP. The Mdh enzyme, which transforms malate into oxaloacetate, is suppressed. Finally, we observed a reduction in TasA, a protein involved in sporulation and released by the bacteria prior to the process [29]. This result suggests that sporulation could be delayed with IMOS as a carbon substrate when compared to glucose. Overall, our results show that IMOS as a substrate leads to a reshaping of _B. subtilis_ CU1 metabolism and functions. A proposed model for IMOS metabolism in _B. subtilis_ CU1 is presented Figure 3. Our observations raised the question whether these changes could enhance the probiotic effects of the bacteria. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Identified Proteins**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Access** \\ **Number** \\ \end{tabular} } & \multicolumn{1}{c}{**Name**} & \multicolumn{1}{c}{**Spots**} & \multicolumn{1}{c}{**Fold Change**} & \multicolumn{1}{c}{_p-Value_} \\ \hline _Amino acid \& protein synthesis_ & & & & \\ Serine-glycine hydroxymethyltransferase & P39148 & glyA & 949 & \(-\)4.0 & \(<\)0.001 \\ Elongation factor G & P80868 & fusA & 412 & \(-\)3.3 & \(<\)0.001 \\
60 kDa chaperonin & P28598 & groEL & 692 & \(-\)2.1 & 0.016 \\ Elongation factor Tu & P33166 & tufa & 1022 & \(-\)3.3 & \(<\)0.001 \\ Aspartyl/glutamyl+tRNA(Asn/Gln) amidotransferase subunit B & O30509 & gatB & 600 & 1.7 & 0.001 \\ Chaperone protein Dnak & P17820 & dnaK & 483 & \(-\)1.7 & 0.005 \\ Methionine-tRNA ligase & P37465 & meT5 & 1076 & 2.1 & 0.023 \\ Phenylalanine–tRNA ligase beta subunit & P17922 & pheT & 326 & 1.9 & 0.035 \\ _Energy metabolism_ & Cytidylate kinase & P38493 & cmk & 867 & 2.2 & 0.002 \\ Acetyl-coenzyme A synthetase & P39062 & acSA & 1020 & 2.2 & 0.002 \\ ATP synthase subunit beta & P37809 & atpD & 878 & \(-\)2.1 & 0.003 \\ _Carbohydrate metabolism \& transport_ & Methylmalonate semialdehyde dehydrogenase & P42412 & iolA & 586 & 2.1 & \(<\)0.001 \\ Alanine dehydrogenase & O08352 & ald & 845 & \(-\)1.5 & 0.004 \\ Isocitrate dehydrogenase & P99126 & icd & 894 & \(-\)2.9 & \(<\)0.001 \\ Maltose-6-phosphate glucosidase & P54716 & malA & 609 & 2.2 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Identified proteins differentially expressed in _B. subtilis_ CU1 after growth on IMOS. Protein differentially expressed have been selected on the criteria of a fold change \(\geq\) 1.3 and \(p\)\(\leq\) 0.05 (Student’s \(t\)-test), then identified by Nano-LC MALDI-TOF/TOF (_n_ = 3 per condition).