esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+
## 1 Introduction
Therefore, the broad objective of this research is to apply encoder-decoder CNN models based upon a newly constructed complex dataset for multiclass damage segmentation. Some feature engineering and regularization techniques will also be recommended to deal with the dataset. Since this study uses a newly designed dataset, a classic encoder-decoder architecture will be used to investigate the training behavior. Therefore, the first objective of this research is to investigate the applicability of U-Net and DeepLabV3+ to detect multiple damages on concrete surfaces. Both architectures will be reconstructed and compared. Since the limitation of related research [7] was difficulty in constructing a dataset with multiple types of damage in an individual image, a custom dataset mostly consisting of multiple damages in every image will be constructed. The research conducted by [PERSON] et al. [30] already deals with multiple damage in an image and their damages are rebar exposure and delamination. Therefore, this paper will deal with different types of damages, namely cracks, voids, and spallings. Moreover, the dataset is also purposely built to deal with a complex foreground, which corresponds to a highly complex real-world scenario. A previous study [26] showed that the Dice loss function successfully dealt with the class imbalance problem. In this study, because of the class imbalance issue, a multiclass generalized Dice loss (GDL) function is used. Therefore, the second objective of this research is to investigate the use of the GDL function in multiclass damage segmentation. Finally, the third objective of this study is to investigate the use of two regularization techniques, namely L1 regularization and spatial dropout [32] on multiclass damage segmentation for complex images. For evaluation purposes, every model with different loss functions and different regularization combinations will be qualitatively and quantitatively compared. The rest of this paper is organized as follows. Section 2 will discuss the dataset's construction, CNN architectures, applied regularizations, evaluation metrics, and training algorithms. Section 3 will discuss the results of the CNN training, which comprises the quantitative comparisons, qualitative comparisons, and limitations of the work. Finally, Section 4 concludes the paper.
## 5 Conclusions  
In this study, the reflective crack resistance of the asphalt overlay method using F-SAMI technology was evaluated using the small and large-scale pavement acceleration tests, and the following conclusions were drawn.  
As a result of the evaluation of reflective crack resistance through an MMLS3 indoor test of a fiber-reinforced, stress-absorbing membrane interlayer (F-SAMI)-applied pavement specimen, it was found that the resistance to reflective cracking of the F-SAMI-applied specimen was superior to that of the specimen with tack coating between the two layers.  
Regarding the effect of the F-SAMI composition ratio, it was found that the F-SAMI including aggregates had better resistance to reflective cracking, while the asphalt binder did not appear to have a clear trend with the application rate.  
Based on evaluation of the reflective crack resistance of the F-SAMI-applied pavement structure through accelerated pavement testing (APT), when the F-SAMI method was applied the reflective crack occurred about 10,000 load passes later compared to the tack coating method. The crack length in the surface is similar for both sections, but the reflective crack in the F-SAMI section was dispersed, resulting in a decreasing crack width of more than 50%.  
Therefore, the F-SAMI method is expected to increase the overall pavement life by suppressing the reflective cracks occurring from the joints or cracks in the lower layer if applied to the overlay construction on the old pavement.  
This research was funded by Korea Institute of Civil Engineering and Building Technology (Project No. 2020-0449).  
The author declares no conflict of interest.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 2 Related Work
### Self-Supervised and Multi-Task Learning
#### 2.2.1 Self-Supervised Learning  
HuBERT [38], as self-supervised learning (SSL), has achieved excellent results in several speech-related tasks at the same time, such as automatic speech recognition, speaker verification, etc. The HuBERT generates pseudo-labels by clustering speech data in an offline way, randomly masks the input features and predicts the masked pseudo-labels to calculate the loss function. By alternating the clustering and prediction, the network gradually learns the intrinsic features step by step.  
DeepCluster [39] was originally an SSL method for the vision domain. This method processes the features directly, groups features iteratively through a clustering algorithm [40] and uses the clustering results as pseudo-labels to update the network weights. It has learned to have a better representation by continuously training so that features are located around a cluster center.
###### Abstract  
Techniques for noncontact measurement of vital signs using camera imaging technologies have been attracting increasing attention. For noncontact physiological assessments, computer vision-based methods appear to be an advantageous approach that could be robust, hygienic, reliable, safe, cost effective and suitable for long distance and long-term monitoring. In addition, video techniques allow measurements from multiple individuals opportunistically and simultaneously in groups. This paper aims to explore the progress of the technology from controlled clinical scenarios with fixed monitoring installations and controlled lighting, towards uncontrolled environments, crowds and moving sensor platforms. We focus on the diversity of applications and scenarios being studied in this topic. From this review it emerges that automatic multiple regions of interest (ROIs) selection, removal of noise artefacts caused by both illumination variations and motion artefacts, simultaneous multiple person monitoring, long distance detection, multi-camera fusion and accepted publicly available datasets are topics that still require research to enable the technology to mature into many real-world applications.  
Keywords: vital signs; contactless; video camera imaging; imaging photoplethysmography (iPPG); region of interest (ROI); noise artefacts +
Footnote †: journal: Applied Sciences
## 1 Introduction
In this method, a view vector is generated based on the predicted asset returns obtained by random forests, and the confidence matrix which contains the uncertainty of each view is measured by the differences in the predicted values of multiple trees. * We propose a decomposition-based multi-objective DIRECT algorithm, named multiDecompose, to handle the random forest-based BL model mentioned above in a short time while maintaining high accuracy, in which an indicator is explored to encourage potentially optimal hyperrectangles to be chosen in all directions. With this indicator, the algorithm can provide a better exploration of the search space of an MOP, especially when stuck in a local optimal Pareto set or a part of the global Pareto set. * We demonstrate the superiority of the proposed algorithm over NSGA-II and MOEA/D on the MOP and DTLZ benchmark problems as well as the effectiveness of solving random forest-based BL model. Moreover, we also test the performance of random forest-based BL model by a comparison with the MV model using the real-world data. The rest of this paper is organized as follows. We first review some of the past studies on BL models and multi-objective DIRECT algorithms in Section 2. Section 3 is dedicated to relevant preliminaries. Thereafter, in Section 4, a random forest-based BL model is presented, followed by a decomposition-based multi-objective DIRECT algorithm. Then, in Section 5, we demonstrate the superiority of the proposed algorithm over NSGA-II and MOEA/D as well as its effectiveness on the model introduced before. Furthermore, we compare our model with the MV model using the real-world data. Finally, the findings and future perspectives are discussed in Section 6.
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 2 Related Work
### Side-Channel-Based Monitoring
This multi-level fusion approach is designed to address the complexities of real-time detection by considering both the input of the physical sensor and the data flowing through the system network, improving the overall reliability and robustness of the anomaly detection process. [PERSON] et al. explored the use of heterogeneous optical sensors in smart home environments, applying artificial intelligence techniques to combine data from optical sensors and machine learning models for remote monitoring and anomaly detection [51]. Their work emphasizes the potential of using diverse sensor modalities to capture a wide range of events, enhancing the ability to detect abnormal behaviors and improving the overall security posture of smart homes. In contrast to these approaches, which mainly employ _data fusion_, our work focuses on leveraging a _decision fusion_ mechanism to create a highly scalable and flexible framework. While data fusion combines raw sensor data or signals to generate a more comprehensive input for further analysis, decision fusion aggregates the outputs of multiple independent monitoring channels to make more accurate and reliable security decisions. This approach allows our framework to handle a wide range of smart and non-smart devices, such as IoT appliances, traditional sensors, and non-digital devices, by cross-verifying the outputs from each channel in a coherent and structured way. By merging decisions from heterogeneous channels (e.g., audio and network traffic), we enhance the reliability and robustness of the monitoring system. In addition, this design facilitates the easy integration of new monitoring channels and techniques, ensuring that the system can adapt and scale as users incorporate more devices into their smart homes over time. For example, users can gradually add cameras, power meters, or environmental sensors to extend the monitoring coverage, thus enhancing the system's overall performance without requiring significant changes to the underlying framework. This flexibility in upgrading the monitoring system over time makes our approach highly adaptable to the evolving needs of smart home environments. Table 2 summarizes the reviewed studies. Each of these side channels offers unique advantages and challenges. Encrypted network traffic provides a balance between security and privacy, while audio and vision offer rich contextual information at the cost of potential privacy concerns. System load monitoring is less intrusive but cannot work for smart devices that do not cause significant power changes. Therefore, our core idea is to create a more comprehensive and robust monitoring system that takes advantage of the strengths while mitigating the limitations of multiple channels.
## 4 Experimental Verification
### Measurement of the Angular Indexing Error of the Mandrel  
In order to measure the angular indexing error of the mandrel caused by the coaxial deviation between the two centers, a 36-sided polygon was fixed to the mandrel, whose angular position error was measured by an autocollimator, as shown in Figure 11. The measured angular indexing error is cumulated angular errors from the encoder to the polygon, including (1) the angular indexing error  
Figure 10: Measured error of the spindle’s rotation angle.  
Figure 9: Measurement of the angular indexing error of the spindle.  
of the spindle at the top plane of the rotary stage, as given by Figure 10 and (2) the induced angular indexing error caused by the radial error motion of the tip of the lower center and the coaxiality error of the mandrel to the spindle, as given by Equation (16). It is noted that the polygon's angular accuracy was calibrated in advance by the above-mentioned autocollimator and the multi-tooth indexing table. Figure 12 shows the measured angular indexing error of the mandrel. It can be seen that the gear to be measured in this GMM has an angular indexing error in the range of \(-2\)-\(9^{\prime\prime}\). Such an error will cause measurement error in the gear parameters.  
Since all experiments in this study were carried out with the same starting point of the rotary stage, the prescribed starting angle of Figure 8 has a \(90^{\circ}\) shift to Figure 4. Therefore, all experimental data were shifted by \(90^{\circ}\). The measured angular indexing errors of the gear, shown in Figure 12, have been shifted \(90^{\circ}\) in Figure 13. This experiment was conducted by five times. Table 1 lists the statistical data. The maximum difference was about \(0.8^{\prime\prime}\) on average and beyond \(1^{\prime\prime}\) at two positions only.  
Figure 11: Measurement of angular indexing error of the mandrel: (**a**) the method using the polygon and autocollimator; (**b**) a photo of the experimental setup.  
Figure 12: Measured angular indexing error of the mandrel.
## 4 Results and Discussion
### RQ1: What NAS Approaches Are Applied to GANs in the Literature?
#### 4.1.2 Reinforcement Learning Approaches
In T2 IGAN, the architecture search is formulated as a MDP, and an RL-based search strategy is adopted to efficiently navigate a cell-based search space that encompasses both convolutional operations and lightweight transformer components. The search processjointly optimizes both the cell structure and the associated operation weights using a composite reward function based on metrics such as IS and FID. Ultimately, the final generator is constructed by stacking the best-performing cells discovered during the search, yielding a competitive architecture for text-to-image synthesis. By leveraging an off-policy RL, T2 IGAN is able to significantly reduce the computational burden, achieving competitive performance in a fraction of the search time compared to earlier approaches. While the method primarily focuses on optimizing the generator architecture (with the discriminator following a pre-defined design), it effectively demonstrates the feasibility and advantages of combining transformer-based representations with adversarial learning. Overall, T2 IGAN represents a meaningful step toward automated, efficient GAN design and underscores the potential of off-policy RL techniques in advancing generative model architecture search. These studies collectively showcase the evolution of reinforcement learning applications in GAN architecture search. From AutoGAN's focus on generator optimization, through AGAN's comprehensive search of both GAN components, to off-policy frameworks like E\({}^{2}\)GAN and T2 IGAN that emphasize efficiency improvements, each approach contributes unique strengths to the field. However, a notable challenge of these RL-based methods is the considerable variation in training time, which can complicate scalability and practical deployment.
# Closed-Form Method for Atmospheric Correction (CMAC) of SmallSat Data Using Scene Statistics
## 2 Materials and Methods
### Forward Scatter Effects and Calibration
Such AOIs were given the name \"quasi invariant areas\" (QIAs) in recognition of the special role they can play as targets to assess AC performance under variable atmospheric effects. QIAs provide the opportunity to objectively evaluate accuracy, stability and AC correction limits by the CMAC and Sen2 Cor software. Data presented for the comparisons presented here are the output from the processing software, alone, with no other treatment except the statistical analyses documented in downloadable Excel spreadsheets (see Supplementary Materials). While the investigation of AC here is relative to S2, a research-grade satellite that includes onboard radiometry, CMAC is applicable to smallsats without additional processing or other relationships beyond the Atm-I model and CMAC Equation. Hence, the level of accuracy achievable for AC of S2 data serves as a direct indication of the AC accuracy achievable for smallsat data.
## 3 Numerical Experiments and Discussion
### Logistic Regression  
For the binary classification, the following convex function related to the model of logistic regression is widely used:  
\[f(x)=\sum_{i=1}^{m}\log(1+\exp(-y_{i}\xi_{i}^{T}x)),\]  
where \(\xi_{i}\) represents the rows of matrix \(\Xi\), \(\text{dim}(\Xi)=m\times d\) and \(y_{i}\in\{-1,1\}\), \(i=\overline{1,d}\). Matrix \(\Xi\) and vector \(y\) represent the training dataset.  
For the computations, we used two datasets: SONAR (\(m=208\), \(d=60\)) and CINA0 (\(m=16,033\), \(d=132\)). The first was used for a comparison of different methods in [36]. The second is a well-known test dataset, which can be downloaded from [[https://www.causality.inf.ethz.ch/challenge.php?page=datasets](https://www.causality.inf.ethz.ch/challenge.php?page=datasets)]([https://www.causality.inf.ethz.ch/challenge.php?page=datasets](https://www.causality.inf.ethz.ch/challenge.php?page=datasets)) (accessed on 14 March 2024). The error was computed as \(f(x^{k})-f(x^{*})\). For the SONAR dataset, the values \(h=0.1\), \(\beta_{1}=0.9999\), and \(\beta_{2}=10\) were used, and a benchmark solution was obtained with method (4) in the case of \(2\times 10^{4}\) iterations. For CINA0, the following parameters were used: \(h=10^{-6}\), \(\beta_{1}=0.99\), \(\beta_{2}=2\) and a benchmark solution was obtained for \(5\times 10^{3}\) iterations of method (4). For both datasets, \(x^{0}\) was chosen as a vector of zeroes.  
In Figure 11, plots of the dependence of error on the iteration number in log-log axes are presented. As can be seen, the adding of \(\beta_{2}\
eq 0\) led to the damping of oscillations typical for the standard HBM.  
Figure 10: Plots of the dependence of the error on the iteration number for the regression problem with smoothed elastic net regularization in log-log axes for different model datasets. Blue line corresponds to the HBM, red line—to method (4).
## 3 Case Study Analysis of Online PD Monitoring in Power Transformers
### Transformer C
#### 3.3.2 Results of Partial Discharge Source Localization  
The location of the PD sources was determined by searching for the area on the transformer tank (near the L2 phase of the low-voltage side) where the amplitude of the acoustic emission pulses was the highest. The highest 'loudness' area was found approximately 60-70 cm below and 10-20 cm to the right of the location where the CH7 transducer was attached to monitor PDs in the L2 phase. Then, we attempted to find a more precise location of the PD source using the TDoA technique. For this purpose, three independent tests were performed. In each of these tests, the transducers were placed in a different location (Figure 16), but always near the previously identified area where the maximum level of acoustic emission signal was recorded. The coordinates of the acoustic emission transducers for each measurement test are listed in Table 10.  
During the PD localization procedure, the phenomenon showed stability and high intensity, which allowed for recording a significant number of AE waveform sets in each of the three tests performed: 442 in Test 1, 465 in Test 2, and 122 in Test 3. Example AE pulses  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Designation**} & \multirow{2}{*}{**AE Sensor**} & \multicolumn{3}{c}{**AE Sensor Coordinates**} \\ \cline{3-5}  & & \(X\) **(m)** & \(Y\) **(m)** & \(Y\) **(m)** \\ \hline \multirow{4}{*}{Test 1} & CH1 & 0.860 & 0 & 0.265 \\  & CH2 & 1.195 & 0 & 0.330 \\  & CH3 & 0.990 & 0 & 0.480 \\  & CH4 & 0.695 & 0 & 0.490 \\ \hline \multirow{4}{*}{Test 2} & CH1 & 1.030 & 0 & 0.340 \\  & CH2 & 1.380 & 0 & 0.250 \\  & CH3 & 1.315 & 0 & 0.505 \\  & CH4 & 0.695 & 0 & 0.485 \\ \hline \multirow{4}{*}{Test 3} & CH1 & 0.870 & 0 & 0.470 \\  & CH2 & 0.945 & 0 & 0.215 \\ \cline{1-1}  & CH3 & 1.140 & 0 & 0.510 \\ \cline{1-1}  & CH4 & 1.145 & 0 & 0.240 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The coordinates and designations of the AE transducers used in the individual measurement tests (i.e., Test 1, Test 2, and Test 3) performed for ‘Transformer C’.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **AE: Pulse (1)** & **AE: No Pulse (0)** \\ \hline
**UHF: Pulse (1)** & TP = 32.7\% & FP = 17.3\% \\
**UHF: No Pulse (0)** & FN = 39.7\% & TN = 10.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Confusion matrix for pulses recorded by UHF and AE methods in ‘Transformer C’.  
The spatial distribution of localization points in all three TDoA localization tests is consistent and shows a high degree of similarity. While the range of solutions for the \(X\) and \(Z\) coordinates is relatively narrow (16 cm for \(X\) and 36 cm for \(Z\)), the range for the \(Y\) coordinate is significantly broader, reaching 90 cm (Table 11). These results suggest that the source of the PDs may be in the insulation of the low-voltage winding lead. Figure 19 shows a photograph of the tested transformer, with the area marked to indicate the range of \(X\) and \(Z\) coordinates of the PD source.
# Trajectory Planning for Intelligent Robotic and
## 2 Special Issue
Experimental tests on a UR5 robot yield to comparable results with respect to the classical controller, showing the feasibility of the proposed approach without the need for tuning the controller parameters. Furthermore, in Contribution 10 a virtual force control for a six-axis robotic arm is developed using both traditional mathematical modeling and machine learning techniques in the implementation of the dynamic model of the manipulator. The proposed position and force hybrid controller is based on a Raspberry Pi board with a real-time kernel, demonstrating the fulfillment of the motion control requirements with a low-cost device. The virtual force sensor is also used to detect collisions with the human operator. The work in Contribution 11 aims at developing a trajectory planning approach to avoid collisions for a SCARA robotic manipulator in the context of collaborative robotics. The authors show that the proposed approach based on topological path planning is a collision-free, deterministic, and predictable route planning method with superior performance with respect to alternative approaches, e.g., based on probabilistic roadmap methods or on generalized Voronoi diagrams. Finally, in Contribution 12 the authors propose a novel method for designing motion profiles utilizing a sinusoidal jerk model to generate fast and smooth trajectories characterized by low vibrations. Differently from previous studies, an analytical optimization procedure for the profile parameters is introduced to minimize both the motion duration and the excitation frequency contents. Numerical and experimental results on a linear axis show the effectiveness of the proposed method in comparison with alternative strategies in terms of smoothness and vibration attenuation. The presented contributions collectively explore diverse dimensions of trajectory planning for intelligent robotic and mechatronic systems. To summarize, Contributions 1, 4 and 5 delve into trajectory planning and control strategies for robotic manipulators, emphasizing factors like operational performance, motion profiles, and joint stiffness influences. Contributions 2 and 11 address the challenges of obstacle avoidance and collision-free trajectory planning in collaborative robotic applications. In the same context, the virtual force sensor presented in Contribution 10 is used to detect collision with the body of the human operator working alongside the robot. Contributions 3 and 7 focus on enhancing collaboration between humans and robots in specific tasks, such as bin-picking and slip detection for a robotic gripper. Contributions 6 and 9 explore the integration of artificial intelligence, with a focus on reinforcement learning and artificial neural networks, to improve dynamic obstacle avoidance, path planning, and position control in robotic systems. Finally, Contributions 8 and 12 explore the effects of motion design on mechanical vibrations.