esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 1 Introduction
The transmission removal problem can be cast into the framework of mixture image minus reflection removal image; however, the dedicated network designed to recover the reflection image as much as possible is still worth further study. In order to have a better recovery effect than the results generated by the reflection removal-oriented approach, the analysis of different levels of feature maps and the selection of appropriate feature maps from different dimensions, including resolution, texture, and style, are involved in meeting this requirement. In addition, we also apply our framework to the application of photographer identification. The contributions of our work can be summarized as follows:  
Figure 1: Image shooting principle under an ambient light environment. (**a**) Real-world glass scenes with reflections; (**b**) imaging principles; (**c**) reflection removal versus transmission removal. * We propose a transmission removal network to recover the reflection layer from the mixture image. In order to recover the weaker components from the mixture image, the edge and color distribution of transmission layer is introduced as a prior to guide the reflection layer separation. In a practical application scenario, we can replace the real transmission layer with a predicted one through reflection removal. * By analyzing the performance exhibited at different levels of the feature maps, we improve the generated results in different dimensions such as resolution, texture, and style to enhance the visual perception of the estimated reflection layer. We also use PatchGAN as the discriminator to enhance the perceptual strength of the generated image and ground truth. * We consider the portrait's appearance in the reflection layer as a separate problem and improve the style loss to solve the problem of the dim portrait in the reflection layer. Thus, the reflection layer image can be effectively applied to photographer identification application. The remainder of this paper is organized as follows. Section 2 introduces the related works about reflection removal and transmission removal. Sections 3 and 4 describe the proposed method and analyze the experimental results, respectively, and Section 5 concludes this paper.
# Imbalanced Diagnosis Scheme for Incipient Rotor Faults in Inverter-Fed Induction Motors
## 2 Background
### Diagnosis Signals  
From the scientific literature, it seems to be a consensus that MCSA is a noninvasive diagnostic method [3; 4; 8; 12; 15] with several advantages: easy acquisition of signals; signal collection does not interfere with the operation of the motor; the obtained signal is reliable under noisy environments; and sensors with reasonable accuracy are not too expensive. Once an IM is manufactured, free of rotor faults, and it is fed from an ideal stator current signal, the temporal expression of this current contains exclusively the fundamental component of its power supply:  
\[i_{0}(t)=I_{m}\cos(\omega_{0}t+\phi_{0}), \tag{1}\]  
where \(\omega_{0}\) denotes the power supply angular pulsation, \(I_{m}\) denotes the amplitude of the stator current, and \(\phi_{0}\) is the phase. However, when a rotor breakage develops, fault-related harmonics emerge in the observed signal and can be used for diagnosis purposes. Nevertheless, when the IM supply comes from an inverter or from the mains with the presence of considerable harmonic content, the amplitude quantification of these fault patterns complicates their identification [16].  
This project will utilize a single stator phase as a monitoring signal, which will be obtained from the motor when it is powered by an inverter or the grid. A data acquisition system will then digitize the signal in the time domain. Following this, the digital signal will undergo processing in both the time and frequency domains to identify fault patterns.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 4 IBC Time Analysis Method
### Lognormal Distribution  
A continuous probability distribution called the lognormal distribution is widely used to explain positive and skewed data. It describes a random variable with a logarithmic distribution. The lognormal distribution is frequently used to model data with positive skewness, such as financial returns or natural phenomenon sizes. It offers a method for transforming data into a normal distribution for analysis. Equation (2) gives the probability density function (PDF) of the lognormal distribution:  
\[\mathrm{N}\big{(}x;\ \mu,\sigma^{2}\big{)}=\frac{1}{\sqrt{2\pi}\mathrm{\alpha x }}\mathrm{exp}\left(-\frac{\left(\ln x-\mu\right)^{2}}{2\sigma^{2}}\right) \tag{2}\]  
where \(x\) is a variable, \(\mu\) is the logarithmic mean, \(\sigma\) is the logarithmic standard deviation, In is the natural logarithm, and \(\mathrm{exp}\) is the exponential function.  
A lognormal process is statistically defined as the multiplicative product of several positive independent random variables. The lognormal distribution changes into a normal distribution when the natural logarithm of the data collection is used. Because the logarithm is undefined for non-positive numbers, the lognormal distribution is only appropriate when the amount of interest is positive. The lognormal distribution has a concave contour on the left side.
# Experimental Study on the In-Plane Seismic Performance of a New Type of Masonry Wall System
## 1 Introduction  
Unreinforced masonry (URM) buildings are the most representative construction system since the early ages of mankind. Most of them are constructed without the consideration of earthquake design requirements or reference to any particular design code. It is common for the total or partial collapse of URM buildings to occur during an earthquake due to the poor quality of the materials and construction technology, as shown in Figures 1 and 2, e.g., [1; 2; 3; 4; 5; 6]. The low tensile strength, low ductility and low ability to dissipate energy are the main reasons for limiting the use of URM in middle- and high-seismicity regions. On the other hand, masonry has several advantages as a structural material, such as thermal and acoustic efficiency, economy, simple construction technology, excellent fire behavior and, usually, low embodied energy. Masonry has good durability and adequate performance with respect to healthy indoor environment, and can be effectively considered as an alternative low-cost construction solution for low- to medium-rise residential buildings.  
The increasing interest in this construction technique in the last few decades has resulted in many experimental tests on URM sub-assemblages [7; 8; 9; 10; 11] and on complete URM buildings [12; 13; 14; 15; 16; 17; 18]. These studies have provided significantly valuable insights into the seismic performance of URM buildings. However, many characteristics of URM buildings are yet to be fully understood, and URM buildings are always severely damaged when an earthquake occurs [13; 14; 16; 17; 18].  
On the other hand, some studies on mortar-free masonry units considering the interlocking mechanism have been conducted, e.g., [19; 20; 21; 22]. However, most of the studies are on brick units with unevenness, and there is no study targeting block units as in this study.  
Under these backgrounds, the authors developed two types of new masonry wall systems for high seismic performance, including loading bearing capacity, deformability and energy dissipation capacity, convenience of construction and reliability of construction. The proposed masonry wall system only has a main block and a key block, without joint mortar. Two types of block systems have different key block shapes: one is the peanut shape, and the other is the dumbbell shape. It is expected that the proposed masonry wall system will be applied mainly to low-rise masonry houses, masonry fences and infilled masonry walls in developing countries.  
In this study, the proposed two types of concrete block walls, as well as a typical concrete block wall, were experimentally investigated to evaluate their in-plane seismic performance. In the tests, full-scale, single-story specimens were tested under static cyclic in-plane loading, and failure patterns and cracks were carefully observed.  
In this paper, the in-plane loading bearing capacity, energy dissipation capacity and reuse ratio of block walls are discussed in detail.
## 3 Particle Swarm Optimization
### Recent Advancements in PSO for a Better Convergence Rate
#### 3.6.6 Cooperative Particle Swarm Optimization  
Cooperative PSO (CPSO) is an extension of the standard PSO algorithm that promotes collaboration and information sharing among multiple sub-swarms or groups of particles [121]. Figure 7 shows the graphical presentation of CPSO working. In cooperative PSO, instead of having a single global best solution for the entire swarm, each sub-swarm maintains its own local best solution, and particles from different sub-swarms communicate and cooperate to collectively search for optimal solutions [122]. The sub-swarms operate independently, exploring different regions of the search space. Periodically, particles exchange information about their best solutions with particles from other sub-swarms [123]. This information sharing allows particles to gain insights from successful regions discovered by other sub-swarms, promoting exploration beyond local optima and facilitating a more-thorough exploration of the search space.  
Figure 6: Flow chart of hybrid PSO algorithm.  
3.6.7. Self-Organizing Hierarchical Particle Swarm Optimization  
In this approach, particles are organized into a hierarchical structure, where higher-level particles oversee the behavior of lower-level particles [124]. This hierarchical arrangement enables a more-efficient search process by coordinating the exploration of different regions of the search space. It is an advanced variant of the standard PSO algorithm, which introduces a hierarchical organization among particles to enhance their exploration and exploitation capabilities [125]. The higher-level particles guide the lower-level particles based on their own experiences and the information they receive from the lower-level particles [126]. The higher-level particles take on a supervisory role, adjusting the influence of different components in the PSO equation to balance global and local search tendencies [127]. They communicate with and provide guidance to the lower-level particles, influencing their search patterns and facilitating the discovery of promising regions. This allows for a distributed and coordinated exploration, as different levels of particles focus on different regions and scales within the search space [128; 129]. The hierarchical organization helps to mitigate the risk of premature convergence and aids in escaping local optima by facilitating exploration in unexplored regions.
# Predicting Phubbing Through Machine Learning: A Study of Internet Usage and Health Risks
## 3 Materials and Methods
### Time Series Method  
A time series is a dataset consisting of values of a given variable recorded at regular intervals over time. This method of analysis is used to identify trends, cycles, seasonal effects, and random fluctuations from past data and to use these patterns to predict future behaviour. Time series analysis is widely used in a wide range of disciplines, such as economics, finance, energy, environmental sciences, and social sciences [42].  
The main objective of time series analysis is to reveal the structural features of past values of a variable to predict its future trends and thus support decision-making processes. This analysis process is usually based on three main components: trends (long-term trends), seasonality (regular cyclical changes), and random fluctuations (noise). For example, a long-term upward trend in the energy demand may be combined with seasonal effects, resulting in a higher demand during the summer months [43].  
The methods used in time series analysis can be divided into two main groups: parametric and non-parametric approaches. Parametric methods assume that the data fit a model and estimate the parameters of this model. For example, the Autoregressive Integrated Moving Average (ARIMA) model is one of the most widely used methods in time series analysis and provides effective results, especially in stationary series. The Seasonal ARIMA (SARIMA) model is a more appropriate approach in series with seasonal effects. In addition, the Holt-Winters method is another popular method that considers both trend and seasonal effects [44].  
Non-parametric methods do not rely on model assumptions and provide more flexible analysis. Machine learning and deep learning approaches have become widespread in time series analysis in recent years. Deep learning methods such as long short-term memory (LSTM) are highly effective in learning complex and non-linear relationships [45].  
Time series analysis plays a critical role not only in forecasting but also in anomaly detection and policy development. For example, strategic communication policies can be developed by analysing the changes in the perceptions of electric vehicles on social media over time. Therefore, time series analysis has an important place in both theoretical and applied studies [46].
## 5 Discussion  
### Advantage Analysis of the Informer Model  
To the best of our knowledge, the transformer model based on the self-attention mechanism has not been explored in modeling crop yield in previous studies. In this study, we used a transformer model called Informer to predict rice yield based on remote sensing and climate data. Our results show that the Informer model exhibited the best performance in predicting rice yield compared with the other four machine learning models. This was mainly because the Informer model had the self-attention mechanism. Enabled by the self-attention mechanisms, the Informer model can compute correlations between each pair of remote sensing and climate variables at all time steps, and effectively extract the temporal dependence of long time series [29; 34].  
Compared with traditional deep learning models, such as CNN and RNN, the Informer model abandons recurrence and convolutions completely, which effectively improves model scalability and training efficiency and makes the model more parallelized [29]. In addition, another advantage of the Informer model is that it can address some deficiencies of regular transformer models, such as the consumption of considerable resources [43]. The Informer model's structure makes it better than traditional deep learning models in handling long sequence time-series forecasting problems. Therefore, the Informer model proposed in our study can be extended to other cropping areas for predicting crop yield and guiding agricultural practice.
## 3 Method
### 2D Image Reconstruction Loss as Supervision  
Following the fashion of most unsupervised approaches, we conduct synthesis image as fundamental supervision of each task in our network by using _differentiable inverse warping_[24] with a bilinear sampler between nearby frames. Given frame \(M_{t}\) and depth \(D_{t}\), the image projected into a structured three-dimensional point cloud can be presented as:  
\[P_{t}(x,y,z)=D_{t}(x,y)\cdot K^{-1}\cdot[i,j,1]^{T} \tag{3}\]  
where \((i,j)\) are the coordinates of the image pixel and \((x,y,z)\) are the point coordinates, \(K\) is the homogeneous camera intrinsic matrix. This way we compute per frame point cloud \(Pt,t\in(1,...)\).  
We transform the nearby point cloud \(P_{t-1}\) using camera intrinsic and known rigid scene transformation T following \(P_{t}^{{}^{\prime}}=KTP_{t-1}\), then back project it to the image plane to get the warped frame \(M_{t}^{p}\). \(M_{t}^{p}\) is warped through both forward and backward directions.  
The self-supervision approach for optical flow is similar, only the warped image \(M_{t}^{f}\) is obtained by the predicted flow, which is also in both directions. Let \(F_{t}^{f}\) be the forward flow from \(M_{t-1}\) to \(M_{t}\), \(F_{t}^{b}\) be the backward flow from \(M_{t}\) to \(M_{t-1}\). The \(M_{t}^{f}\) warped from \(M_{t-1}\) is formulated as \(M_{t-1}(X+F_{t}^{f}(X))\), where X denotes image pixel of frame \(M_{t-1}\). For the backward direction, we define in the same way with \(F_{t}^{f}\) and \(F_{t}^{b}\) exchanged.  
The reconstruction consistency loss is then produced by comparing image \(M_{t}\) to warped image \(M_{t}^{{}^{\prime}}\). Note that the warping schemes make implicitly assumption that there is no occlusion in the scene so that incorrect deformations could be learned from the occluded regions. Therefore we mask occluded pixels to alleviate the negative effect of outliers and penalize photometric difference only for every non-occluded pixel. The occlusion mask is modeled as \(O_{t}\) in Section 3.2, the photometric consistency loss is formulated as:  
\[L_{re}=\sum_{X\in M}((1-O_{t})\|M_{t}(X),M_{t}^{p}(X)\|_{1}+(1-O_{t})\|M_{t}(X ),M_{t}^{f}(X)\|_{1}) \tag{4}\]  
By performing this way we also take into account the discrepancy between warped image \(M_{t}^{p}\) and \(M_{t}^{f}\) without occlusion since they should be ideally identical if predictions of the network are perfect.
## 3 Results
### Training Energy Efficiency  
The results of the efficiency measurements are presented in Table 8. Additional time and power columns have been included to help identify the causes of high or low energy efficiency in a particular card in the test. Analysis of the measurement results shows a significant advantage of the Nvidia card when training neural networks. This card achieved energy efficiency more than double that of AMD and more than four times that of Intel in all tests conducted.  
During the ResNet50 PyTorch, MobileNetV2 TensorFlow, and ResNet50 TensorFlow tests, the Nvidia card consumed the highest power (280 W on average) while achieving the shortest training times (13.33 s for YOLO 11 PyTorch, 54.71 s for Resnet50 PyTorch, 3.3 s for Mobilenet V2 Tensorflow, and 0.89 s for ResNet50 Tensorflow). The Intel card, despite the lowest power consumption during training (103 W on average), showed too low computing performance to achieve sufficiently short training times. As a result, the Intel card achieved very long learning times (148.03 s for YOLO 11 PyTorch, 912.41 s for ResNet50 PyTorch, 47.90 s for MobileNetV2 Tensorflow, and 13.40 s for ResNet50 Tensorflow), resulting in the lowest power efficiency in all tests performed.  
The AMD card, during testing, consumed similar power to the Nvidia card (248 W on average) but achieved significantly worse training times compared to the Nvidia card (45.33 s for YOLO 11 PyTorch, 129.79 s for ResNet50 PyTorch, 9.84 s for MobileNetV2  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **AMD** & **Intel** & **Nvidia** \\ \hline Power [W] & 13.25 & 33.87 & 5.38 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The power drawn by the graphics card at idle power.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**AMD**} & \multicolumn{2}{c}{**Intel**} & \multicolumn{2}{c}{**Nvidia**} & \multicolumn{2}{c}{**AMD**} & \multicolumn{2}{c}{**Intel**} & \multicolumn{2}{c}{**Nvidia**} \\ \cline{2-9}  & **T [s]** & **P [W]** & **T [s]** & **P [W]** & **T [S]** & **P [W]** & **E [ep/k]** & **E [ep/k]** & **E [ep/k]** \\ \hline YOLO 11 PyTorch & 45.33 & 256.50 & 148.03 & 99.64 & 13.33 & 200.02 & 0.09 & 0.07 & 0.37 \\ \hline ResNet50 PyTorch & 129.79 & 259.88 & 912.41 & 105.96 & 54.71 & 305.33 & 0.03 & 0.01 & 0.06 \\ \hline MobileNetV2 Tensorflow & 9.84 & 229.80 & 47.90 & 98.67 & 3.30 & 262.65 & 0.44 & 0.21 & 1.15 \\ \hline ResNet50 Tensorflow & 2.90 & 245.08 & 13.40 & 107.46 & 0.89 & 351.95 & 1.41 & 0.69 & 3.19 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Measure machine learning speed (T), power input (P), and energy efficiency (E).  
Tensorflow, and 2.90 s for ResNet50 Tensorflow). Despite this, the achieved times were short enough for the AMD card to achieve better energy efficiency than the Intel card.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 5 Conclusions  
A comprehensive comparison of fifteen notable feature detection and description techniques for image stitching was carried out in the study. The following are some of the contributions made by this research: (1) The evaluations give a better picture of how feature detectors and descriptors work across a variety of image datasets; (2) cumulative comparisons for a complete set of 85 detector-descriptor pairs were performed over a number of datasets; and (3) a rank was assigned to each detector and descriptor pair for each dataset, which shows the relative performance of the detector and descriptor for that particular type of dataset. Overall, it was identified that the AKAZE detector paired with the AKAZE descriptor outperforms all other combinations for all types of selected datasets. The AGAST detector combined with the BRIEF descriptor also performs very well for dataset 3 containing indoor scenes.  
In future, we can include more datasets, and a study of detectors and descriptors can be performed by varying parameters of each detector and descriptor. The ranking can also be made more application-specific by assigning suitable weights to each metric.  
Conceptualization, S.K.S. and K.J.; methodology, S.K.S.; software, S.K.S.; validation, S.K.S. and K.J.; formal analysis, S.K.S.; investigation, S.K.S.; writing--original draft preparation, S.K.S.; writing--review and editing, S.K.S. and A.K.S.; visualization, S.K.S. and A.K.S.; supervision, K.J. and A.K.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.